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Table of Contents


Spring Data GemFire focuses on integrating the Spring Framework’s powerful, non-invasive programming model and concepts with Pivotal GemFire to simplify configuration and development of Java applications using GemFire.

This document assumes the reader already has a basic familiarity with the Spring Framework and Pivotal GemFire concepts and APIs.

While every effort has been made to ensure this documentation is comprehensive and complete, with no errors, some topics are beyond the scope of this document and may require more explanation (e.g. data distribution management with partitioning for HA while still preserving consistency). Additionally, some typos might have crept in. If you do spot mistakes or even more serious errors and you can spare a few cycles, please do bring these issues to the attention of the Spring Data GemFire team by raising an appropriate issue.

Thank you.

1. Introduction

This reference guide for Spring Data GemFire explains how to use the Spring Framework to configure and develop applications with Pivotal GemFire. It presents the basic concepts, semantics and provides numerous examples to help you get started.

2. Requirements

Spring Data GemFire requires JDK 8.0, Spring Framework 5 and Pivotal GemFire 9.0.x.

3. New Features

As of the 1.2.0.RELEASE, this project, formerly known as Spring GemFire, has been renamed to Spring Data GemFire to reflect that it is now a module of the Spring Data project.

3.1. New in the 1.2 Release

  • Full support for GemFire configuration via the SDG gfe namespace. Now GemFire components may be configured completely without requiring a native cache.xml file.

  • WAN Gateway support for GemFire 6.6.x. See Configuring WAN Gateways.

  • Spring Data Repository support using a dedicated SDG namespace, gfe-data. See Spring Data GemFire Repositories

  • Namespace support for registering GemFire Functions. See Configuring the Function Service

  • A top-level <disk-store> element has been added to the SDG gfe namespace to allow sharing of persist stores among Regions, and other components that support persistent backup or overflow. See [bootstrap-diskstore]

    The <*-region> elements no longer allow a nested <disk-store> element.
  • GemFire Sub-Regions are supported via nested <*-region> elements.

  • A <local-region> element has been added to configure a Local Region.

  • Support for the re-designed WAN Gateway in GemFire 7.0.

3.2. New in the 1.3 Release

  • Annotation support for GemFire Functions. It is now possible to declare and register Functions written as POJOs using annotations. In addition, Function executions are defined as annotated interfaces, similar to the way Spring Data Repositories work. See Annotation Support for Function Execution.

  • Added a <datasource> element to the SDG gfe-data namespace to simplify establishing a basic client connection to a GemFire data grid.

  • Added a <json-region-autoproxy> element to the SDG gfe-data namespace to support JSON features introduced in GemFire 7.0, enabling Spring AOP to perform the necessary conversions automatically on Region operations.

  • Upgraded to GemFire 7.0.1 and added namespace support for new AsyncEventQueue attributes.

  • Added support for setting subscription interest policy on Regions.

  • Support for void returns on Function executions. See Annotation Support for Function Execution for complete details.

  • Support for persisting Local Regions. See Local Region and [bootstrap:region:common:attributes].

  • Support for entry time-to-live and entry idle-time on a GemFire Client Cache. See Configuring a GemFire ClientCache.

  • Support for multiple Spring Data GemFire web-based applications using a single GemFire cluster, operating concurrently inside tc Server.

  • Support for concurrency-checks-enabled on all GemFire Cache Region definitions using the SDG gfe namespace. See [bootstrap:region:common:attributes].

  • Support for Cache Loaders and Cache Writers on Client, Local Regions. See [bootstrap:region:common:loaders-writers].

  • Support for registering CacheListeners, AsyncEventQueues and Gateway Senders on GemFire Cache Sub-Regions.

  • Support for PDX persistent keys in GemFire Regions.

  • Support for correct Partition Region bean creation in a Spring context when collocation is specified with the colocated-with attribute.

  • Full support for GemFire Cache Sub-Regions using proper, nested <*-region> element syntax in the SDG gfe namespace.

  • Upgraded Spring Data GemFire to Spring Framework 3.2.8.

  • Upgraded Spring Data GemFire to Spring Data Commons 1.7.1.

3.3. New in the 1.4 Release

  • Upgrades to Pivotal GemFire 7.0.2.

  • Upgrades to Spring Data Commons 1.8.x.RELEASE.

  • Upgrades to Spring Framework 3.2.x.RELEASE.

  • Integrates Spring Data GemFire with Spring Boot, which includes both a spring-boot-starter-data-gemfire POM along with a Spring Boot sample application demonstrating GemFire Cache Transactions configured with SDG and bootstrapped with Spring Boot.

  • Support for bootstrapping a Spring ApplicationContext in a GemFire Server when started from Gfsh. See Bootstrapping a Spring ApplicationContext in Pivotal GemFire for more details.

  • Support for persisting application domain object/entities to multiple GemFire Cache Regions. See Entity Mapping for more details.

  • Support for persisting application domain object/entities to GemFire Cache Sub-Regions, avoiding collisions when Sub-Regions are uniquely identifiable, but identically named. See Entity Mapping for more details.

  • Adds strict XSD type rules to, and full support for, Data Policies and Region Shortcuts on all GemFire Cache Region types.

  • Changed the default behavior of SDG <*-region> elements from lookup to always create a new Region along with an option to restore old behavior using the ignore-if-exists attribute. See Common Region Attributes and [bootstrap:region:common:regions-subregions-lookups-caution] for more details.

  • Spring Data GemFire can now be fully built and ran on JDK 7 and JDK 8.

Pivotal GemFire has not yet been fully tested and certified to run JDK 8; See GemFire User Guide for additional details.

3.4. New in the 1.5 Release

  • Maintains support for Pivotal GemFire 7.0.2.

  • Upgrades to Spring Data Commons 1.9.x.RELEASE.

  • Upgrades to Spring Framework 4.0.x.RELEASE.

  • Reference Guide migrated to Asciidoc.

  • Renewed support for deploying Spring Data GemFire in an OSGi container.

  • Removed all default values in the Spring Data GemFire XML namespace Region-type elements to rely on GemFire defaults instead.

  • Added convenience to automatically create Disk Store directory locations.

  • SDG annotated Function implementations can now be executed from Gfsh.

  • Enable GemFire GatewayReceivers to be started manually.

  • Support for Auto Region Lookups. See [bootstrap:region:auto-lookup] for further details.

  • Support for Region Templates. See [bootstrap:region:common:region-templates] for further details.

3.5. New in the 1.6 Release

  • Upgrades to Pivotal GemFire 8.0.0.

  • Upgrades to Spring Data Commons 1.10.x.RELEASE.

  • Maintains support on Spring Framework 4.0.x.RELEASE.

  • Adds support for GemFire 8’s new Cluster-based Configuration.

  • Enables 'auto-reconnect' functionality to be employed in Spring-configured GemFire Servers.

  • Allows the creation of concurrent and parallel Async Event Queues and Gateway Senders.

  • Adds support for GemFire 8’s Region data compression.

  • Adds attributes to set both critical and warning percentages on Disk Store usage.

  • Supports the capability to add the new EventSubstitutionFilters to GatewaySenders.

3.6. New in the 1.7 Release

  • Upgrades to Pivotal GemFire 8.1.0.

  • Upgrades to Spring Data Commons 1.11.x.RELEASE.

  • Upgrades to Spring Framework 4.1.x.RELEASE.

  • Early access support for Pivotal GemFire.

  • Support for adding Spring-defined Cache Listeners, Loaders and Writers on "existing" GemFire Regions configured in Spring XML, cache.xml or even with Pivotal GemFire’s Cluster Config.

  • Spring JavaConfig support added to SpringContextBootstrappingInitializer.

  • Support for custom ClassLoaders in SpringContextBootstrappingInitializer to load Spring-defined bean classes.

  • Support for LazyWiringDeclarableSupport re-initialization and complete replacement for WiringDeclarableSupport.

  • Adds locators and servers attributes to the <gfe:pool> element allowing variable Locator/Server endpoint lists configured with Spring’s property placeholders.

  • Enables the use of <gfe-data:datasource> element with non-Spring configured Pivotal GemFire Servers.

  • Multi-Index definition and creation support.

  • Annotation-based Data Expiration

  • OQL Query Extensions using Annotations

  • Configuring the Snapshot Service

3.7. New in the 1.8 Release

  • Upgrades to Pivotal GemFire 8.2.0.

  • Upgrades to Spring Data Commons 1.12.x.RELEASE.

  • Upgrades to Spring Framework 4.2.x.RELEASE.

  • Adds Maven POM to build SDG with Maven.

  • Adds support for CDI.

  • Enables a ClientCache to be configured without a Pool.

  • <gfe:cache> and <gfe:client-cache> elements use-bean-factory-locator attributes now default to false.

  • Adds durable-client-id and durable-client-timeout attributes to <gfe:client-cache>.

  • GemfirePersistentProperty now properly handles other non-entity, scalar-like types (e.g. BigDecimal, BigInteger).

  • Prevents SDG-defined Pools from being destroyed before Regions that use those Pools.

  • Handles case-insensitive GemFire OQL queries defined as Repository query methods.

  • Changes GemFireCache.evict(key) to call Region.remove(key) in SDG’s Spring Cache Abstraction support.

  • Fixes RegionNotFoundException with Repository queries on a client Region associated with a specific Pool configured for GemFire server groups.

  • Changes Gateway Senders/Receivers to no longer be tied to the Spring container.

3.8. New in the 1.9 Release

  • Upgrades to Pivotal GemFire 8.2.4.

  • Upgrades to Spring Data Commons 1.13.x.RELEASE.

  • Upgrades to Spring Framework 4.3.x.RELEASE.

  • Introduces an entirely new Annotation-based configuration model inspired by Spring Boot.

  • Adds support for suspend and resume in the GemfireTransactionManager.

  • Adds support in Repositories to use the bean id property as the Region key when the @Id annotation is not present.

  • Uses MappingPdxSerializer as the default GemFire serialization strategy when @EnablePdx is used.

  • Enables GemfireCacheManager to explicitly list Region names to be used in the Spring’s Caching Abstraction.

  • Configure GemFire Caches, CacheServers, Locators, Pools, Regions, Indexes, DiskStores, Expiration, Eviction, Statistics, Mcast, HttpService, Auth, SSL, Logging, System Properties.

  • Repository support with multiple Spring Data modules on the classpath.

3.9. New in the 2.0 Release

  • Upgrades to Pivotal GemFire 9.0.x.

  • Upgrades to Spring Data Commons 2.0.x.RELEASE.

  • Upgrades to Spring Framework 5.0.x.RELEASE.

  • Reorganizes the SDG codebase by better packaging different classes and components by concern.

  • Adds extensive support for Java 8 types, particularly in the SD Repository abstraction.

  • Changes to the Repository interface and abstraction, e.g. IDs are no longer required to be java.io.Serializable.

  • Sets @EnableEntityDefinedRegions annotation ignoreIfExists attribute to true by default.

  • Sets @Indexed annotation override attribute to false by default.

  • Renames @EnableIndexes to @EnableIndexing.

  • Introduces a InterestsBuilder class to easily and conveniently express Interests in keys/values between client and server when using JavaConfig.

  • Adds support for Off-Heap, Redis Adapter and GemFire’s new Security framework to the Annotation configuration model.

Reference Guide

4. Document Structure

The following chapters explain the core functionality offered by Spring Data GemFire for Pivotal GemFire.

Bootstrapping Pivotal GemFire with the Spring container describes the configuration support provided for bootstrapping, configuring, initializing and accessing Pivotal GemFire Caches, Regions, and related Distributed System components.

Working with Pivotal GemFire APIs explains the integration between the Pivotal GemFire APIs and the various data access features available in Spring, such as transaction management and exception translation.

Working with Pivotal GemFire Serialization describes the enhancements for Pivotal GemFire (de)serialization and management of associated objects.

POJO mapping describes persistence mapping for POJOs stored in Pivotal GemFire using Spring Data.

Spring Data GemFire Repositories describes how to create and use Spring Data Repositories to access data in Pivotal GemFire.

Annotation Support for Function Execution describes how to create and use Pivotal GemFire Functions using Annotations.

Bootstrapping a Spring ApplicationContext in Pivotal GemFire describes how to bootstrap a Spring ApplicationContext running in an Pivotal GemFire server using Gfsh.

Sample Applications describes the examples provided with the distribution to illustrate the various features available in Spring Data GemFire.

5. Bootstrapping Pivotal GemFire with the Spring container

Spring Data GemFire provides full configuration and initialization of the Pivotal GemFire In-Memory Data Grid (IMDG) using the Spring IoC container. The framework includes several classes to help simplify the configuration of Pivotal GemFire components including: Caches, Regions, Indexes, DiskStores, Functions, WAN Gateways, persistence backup along with several other Distributed System components in order to support a variety of use cases with minimal effort.

This section assumes basic familiarity with Pivotal GemFire. For more information, see the Pivotal GemFire product documentation.

5.1. Advantages of using Spring over Pivotal GemFire cache.xml

Spring Data GemFire’s XML namespace supports full configuration of the Pivotal GemFire In-Memory Data Grid (IMDG). The XML namespace is the preferred way to configure Pivotal GemFire in a Spring context in order to properly manage GemFire’s lifecycle inside the Spring container. While support for GemFire’s native cache.xml persists for legacy reasons, GemFire application developers are encouraged to do everything in Spring XML to take advantage of the many wonderful things Spring has to offer such as modular XML configuration, property placeholders and overrides, SpEL, and environment profiles. Behind the XML namespace, Spring Data GemFire makes extensive use of Spring’s FactoryBean pattern to simplify the creation, configuration and initialization of GemFire components.

Pivotal GemFire provides several callback interfaces, such as CacheListener, CacheLoader and CacheWriter, that allow developers to add custom event handlers. Using Spring’s IoC container, these callbacks may be configured as normal Spring beans and injected into GemFire components. This is a significant improvement over native cache.xml, which provides relatively limited configuration options and requires callbacks to implement GemFire’s Declarable interface (see Wiring Declarable Components to see how you can still use Declarables within Spring’s IoC/DI container).

In addition, IDEs, such as the Spring Tool Suite (STS), provide excellent support for Spring XML namespaces including code completion, pop-up annotations, and real time validation, making them easy to use.

5.2. Using the Core Namespace

To simplify configuration, Spring Data GemFire provides a dedicated XML namespace for configuring core Pivotal GemFire components. It is possible to configure beans directly using Spring’s standard <bean> definition. However, all bean properties are exposed via the XML namespace so there is little benefit to using raw bean definitions. For more information about XML Schema-based configuration in Spring, see the appendix in the Spring Framework reference documentation.

Spring Data Repository support uses a separate XML namespace. See Spring Data GemFire Repositories for more information on how to configure Spring Data GemFire Repositories.

To use the Spring Data GemFire XML namespace, simply declare it in your Spring XML configuration meta-data:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/geode http://www.springframework.org/schema/gemfire/spring-geode.xsd"> (3)

  <bean id ... >

  <gfe:cache ...> (4)

1 Spring Data GemFire XML namespace prefix. Any name will do but through out this reference documentation, gfe will be used.
2 The XML namespace prefix is mapped to the URI.
3 The XML namespace URI location. Note that even though the location points to an external address (which does exist and is valid), Spring will resolve the schema locally as it is included in the Spring Data GemFire library.
4 Example declaration using the XML namespace with the gfe prefix.

It is possible to change the default namespace from beans to gfe. This is useful for XML configuration composed mainly of GemFire components as it avoids declaring the prefix. To achieve this, simply swap the namespace prefix declaration above:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/geode" (1)
  xmlns:beans="http://www.springframework.org/schema/beans" (2)
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/geode http://www.springframework.org/schema/gemfire/spring-geode.xsd">

  <beans:bean id ... > (3)

  <cache ...> (4)

1 The default namespace declaration for this XML document points to the Spring Data GemFire XML namespace.
2 The beans namespace prefix declaration for Spring’s raw bean definitions.
3 Bean declaration using the beans namespace. Notice the prefix.
4 Bean declaration using the gfe namespace. Notice the lack of prefix since gfe is the default namespace.

5.3. Using the Data Access Namespace

In addition to the core XML namespace (gfe), Spring Data GemFire provides a gfe-data XML namespace primarily intended to simplify the development of Pivotal GemFire client applications. This namespace currently contains support for GemFire Repositories and function execution as well as includes a <datasource> tag that offers a convenient way to connect to the Pivotal GemFire data grid.

5.3.1. An Easy Way to Connect to GemFire

For many applications, a basic connection to a GemFire data grid using default values is sufficient. Spring Data GemFire’s <datasource> tag provides a simple way to access data. The data source creates a ClientCache and connection Pool. In addition, it will query the cluster servers for all existing root Regions and create an (empty) client Region proxy for each one.

  <locator host="remotehost" port="1234"/>

The <datasource> tag is syntactically similar to <gfe:pool>. It may be configured with one or more nested locator or server tags to connect to an existing data grid. Additionally, all attributes available to configure a Pool are supported. This configuration will automatically create client Region beans for each Region defined on cluster members connected to the Locator, so they may be seamlessly referenced by Spring Data mapping annotations, GemfireTemplate, and wired into application classes.

Of course, you can explicitly configure client Regions. For example, if you want to cache data in local memory:

  <locator host="remotehost" port="1234"/>

<gfe:client-region id="Example" shortcut="CACHING_PROXY"/>

5.4. Configuring a Cache

To use Pivotal GemFire, a developer needs to either create a new Cache or connect to an existing one. With the current version of GemFire, there can be only one open Cache per VM (technically, per ClassLoader). In most cases, the Cache should only be created once.

This section describes the creation and configuration of a peer cache member, appropriate in peer-to-peer (P2P) topologies and cache servers. A cache member can also be used in standalone applications and integration tests. However, in most typical production systems, most application processes will act as cache clients, creating a ClientCache instance instead. This is described in the sections Configuring a GemFire ClientCache and Client Region.

A peer cache with default configuration can be created with a very simple declaration:


During Spring container initialization, any application context containing this cache definition will register a CacheFactoryBean that creates a Spring bean named gemfireCache referencing a GemFire Cache instance. This bean will refer to either an existing cache, or if one does not already exist, a newly created one. Since no additional properties were specified, a newly created cache will apply the default cache configuration.

All Spring Data GemFire components that depend on the cache respect this naming convention, so there is no need to explicitly declare the cache dependency. If you prefer, you can make the dependency explicit via the cache-ref attribute provided by various SDG XML namespace elements. Also, you can easily override the cache’s bean name using the id attribute:

<gfe:cache id="myCache"/>

A GemFire Cache can be fully configured using Spring, however, GemFire’s native XML configuration file, cache.xml, is also supported. For situations where the GemFire cache needs to be configured natively, simply provide a reference to the GemFire XML configuration file using the cache-xml-location attribute:

<gfe:cache id="cacheConfiguredWithNativeXml" cache-xml-location="classpath:cache.xml"/>

In this example, if a cache needs to be created, it will use a file named cache.xml located in the classpath root to configure it.

The configuration makes use of Spring’s Resource abstraction to locate the file. This allows various search patterns to be used, depending on the runtime environment or the prefix specified (if any) in the resource location.

In addition to referencing an external XML configuration file, a developer may also specify GemFire System properties using any of Spring’s Properties support features.

For example, the developer may use the properties element defined in the util namespace to define Properties directly or load properties from a properties file:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/gemfire http://www.springframework.org/schema/gemfire/spring-gemfire.xsd
    http://www.springframework.org/schema/util http://www.springframework.org/schema/util/spring-util.xsd">

  <util:properties id="gemfireProperties" location="file:/path/to/gemfire.properties"/>

  <gfe:cache properties-ref="gemfireProperties"/>


Using a properties file is recommended for externalizing environment specific settings outside the application configuration.

Cache settings apply only if a new cache needs to be created. If an open cache already exists in the VM, these settings are ignored.

5.4.1. Advanced Cache Configuration

For advanced cache configuration, the cache element provides a number of configuration options exposed as attributes or child elements:

    enable-auto-reconnect="false" (2)
    use-bean-factory-locator="true" (3)
    use-cluster-configuration="false" (4)

  <gfe:transaction-listener ref="myTransactionListener"/> (5)

  <gfe:transaction-writer> (6)
    <bean class="org.example.app.gemfire.transaction.TransactionWriter"/>

  <gfe:gateway-conflict-resolver ref="myGatewayConflictResolver"/> (7)

  <gfe:dynamic-region-factory/> (8)

  <gfe:jndi-binding jndi-name="myDataSource" type="ManagedDataSource"/> (9)

1 Various cache options are supported by attributes. For further information regarding anything shown in this example, please consult the GemFire product documentation. The close attribute determines whether the cache should be closed when the Spring application context is closed. The default is true, however, for use cases in which multiple application contexts use the cache (common in web applications), set this value to false.
2 Setting the enable-auto-reconnect attribute to true (default is false), allows a disconnected GemFire member to automatically reconnect and rejoin the GemFire cluster. See the GemFire product documentation for more details.
3 Setting the use-bean-factory-locator attribute to true (defaults to false) is only applicable when both Spring (XML) configuration meta-data and GemFire cache.xml is used to configure the GemFire cache node (whether client or peer). This option allows GemFire components (e.g. CacheLoader) expressed in cache.xml to be auto-wired with beans (e.g. DataSource) defined in the Spring application context. This option is typically used in conjunction with cache-xml-location.
4 Setting the use-cluster-configuration attribute to true (default is false) enables a GemFire member to retrieve the common, shared Cluster-based configuration from a Locator. See the GemFire product documentation for more details.
5 Example of a TransactionListener callback declaration using a bean reference. The referenced bean must implement TransactionListener. A TransactionListener can be implemented to handle transaction related events (e.g. afterCommit, afterRollback).
6 Example of a TransactionWriter callback declaration using an inner bean declaration. The bean must implement TransactionWriter. The TransactionWriter is a callback that is allowed to veto a transaction.
7 Example of a GatewayConflictResolver callback declaration using a bean reference. The referenced bean must implement http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/util/GatewayConflictResolver.html [GatewayConflictResolver]. A GatewayConflictResolver is a Cache-level plugin that is called upon to decide what to do with events that originate in other systems and arrive through the WAN Gateway.
8 Enable GemFire’s DynamicRegionFactory, which provides a distributed Region creation service.
9 Declares a JNDI binding to enlist an external DataSource in a GemFire transaction.
Enabling PDX Serialization

The example above includes a number of attributes related to GemFire’s enhanced serialization framework, PDX. While a complete discussion of PDX is beyond the scope of this reference guide, it is important to note that PDX is enabled by registering a PdxSerializer which is specified via the pdx-serializer attribute. GemFire provides an implementing class org.apache.geode.pdx.ReflectionBasedAutoSerializer that uses Java Reflection, however, it is common for developers to provide their own implementation. The value of the attribute is simply a reference to a Spring bean that implements the PdxSerializer interface.

More information on serialization support can be found in Working with Pivotal GemFire Serialization

Enabling auto-reconnect

Setting the <gfe:cache enable-auto-reconnect="[true|false*]> attribute to true should be done with care.

Generally, 'auto-reconnect' should only be enabled in cases where Spring Data GemFire’s XML namespace is used to configure and bootstrap a new, non-application GemFire Server to add to a cluster. In other words, 'auto-reconnect' should not be enabled when Spring Data GemFire is used to develop and build an GemFire application that also happens to be a peer cache member of the GemFire cluster.

The main reason for this is that most GemFire applications use references to the GemFire cache or Regions in order to perform data access operations. These references are "injected" by the Spring container into application components (e.g. DAOs or Repositories) for use by the application. When a peer member is forcefully disconnected from the rest of the cluster, presumably because the peer member has become unresponsive or a network partition separates one or more peer members into a group too small to function as an independent distributed system, the peer member will shutdown and all GemFire component references (e.g. Cache, Regions, etc) become invalid.

Essentially, the current forced-disconnect processing logic in each peer member dismantles the system from the ground up. The JGroups stack shuts down, the Distributed System is put in a shutdown state and finally, the Cache is closed. Effectively, all memory references become stale and are lost.

After being disconnected from the Distributed System a peer member enters a "reconnecting" state and periodically attempts to rejoin the Distributed System. If the peer member succeeds in reconnecting, the member rebuilds its "view" of the Distributed System from existing members and receives a new Distributed System ID. Additionally, all Cache, Regions and other GemFire components are reconstructed. Therefore, all old references, which may have been injected into application by the Spring container are now stale and no longer valid.

GemFire makes no guarantee, even when using the GemFire public Java API, that application Cache, Region or other component references will be automatically refreshed by the reconnect operation. As such, GemFire applications must take care to refresh their own references.

Unfortunately, there is no way to be notified of a disconnect event, and subsequently, a reconnect event. If that were the case, the application developer would have a clean way to know when to call ConfigurableApplicationContext.refresh(), if even applicable for an application to do so, which is why this "feature" of Pivotal GemFire is not recommended for peer cache GemFire applications.

For more information about 'auto-reconnect', see GemFire’s product documentation.

Using Cluster-based Configuration

Pivotal GemFire’s Cluster Configuration Service is a convenient way for any peer member joining the cluster to get a "consistent view" of the cluster by using the shared, persistent configuration maintained by a Locator. Using the Cluster-based Configuration ensures the peer member’s configuration will be compatible with the GemFire Distributed System when the member joins.

This feature of Spring Data GemFire (setting the use-cluster-configuration attribute to true) works in the same way as the cache-xml-location attribute, except the source of the GemFire configuration meta-data comes from the network via a Locator as opposed to a native cache.xml file residing in the local file system.

All GemFire native configuration meta-data, whether from cache.xml or from the Cluster Configuration Service, gets applied before any Spring (XML) configuration meta-data. As such, Spring’s config serves to "augment" the native GemFire configuration meta-data and would most likely be specific to the application.

Again, to enable this feature, just specify the following in the Spring XML config:

  <gfe:cache use-cluster-configuration="true"/>
While certain GemFire tools, like Gfsh, have their actions "recorded" when schema-like changes are made (e.g. gfsh>create region --name=Example --type=PARTITION), Spring Data GemFire’s configuration meta-data is not recorded. The same is true when using GemFire’s public Java API directly; it too is not recorded.

For more information on GemFire’s Cluster Configuration Service, see the product documentation.

5.4.2. Configuring a GemFire CacheServer

Spring Data GemFire includes dedicated support for configuring a CacheServer, allowing complete configuration through the Spring container:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
    http://www.springframework.org/schema/gemfire http://www.springframework.org/schema/gemfire/spring-gemfire.xsd


  <!-- Example depicting serveral GemFire CacheServer configuration options -->
  <gfe:cache-server id="advanced-config" auto-startup="true"
       bind-address="localhost" host-name-for-clients="localhost" port="${gemfire.cache.server.port}"
       load-poll-interval="2000" max-connections="22" max-message-count="1000" max-threads="16"
       max-time-between-pings="30000" groups="test-server">

    <gfe:subscription-config eviction-type="ENTRY" capacity="1000" disk-store="file://${java.io.tmpdir}"/>


  <context:property-placeholder location="classpath:cache-server.properties"/>


The configuration above illustrates the cache-server element and the many options available.

Rather than hard-coding the port, this configuration uses Spring’s context namespace to declare a property-placeholder. property placeholder reads one or more properties files and then replaces property placeholders with values at runtime. This allows administrators to change values without having to touch the main application configuration. Spring also provides the SpEL and the environment abstraction to support externalization of environment-specific properties from the main codebase, easing deployment across multiple machines.
To avoid initialization problems, the CacheServer started by Spring Data GemFire will start after the Spring container has been fully initialized. This allows potential Regions, Listeners, Writers or Instantiators defined declaratively to be fully initialized and registered before the server starts accepting connections. Keep this in mind when programmatically configuring these elements as the server might start after your components and thus not be seen by the clients connecting right away.

5.4.3. Configuring a GemFire ClientCache

In addition to defining a GemFire peer Cache, Spring Data GemFire also supports the definition of a GemFire ClientCache in a Spring context. A ClientCache definition is very similar in configuration and use to the GemFire peer Cache and is supported by the org.springframework.data.gemfire.client.ClientCacheFactoryBean.

The simplest definition of a GemFire cache client using default configuration can be accomplished with the following declaration:


client-cache supports many of the same options as the cache element. However, as opposed to a full-fledged peer cache member, a cache client connects to a remote cache server through a Pool. By default, a Pool is created to connect to a server running on localhost, listening to port 40404. The default Pool is used by all client Regions unless the Region is configured to use a specific Pool.

Pools can be defined with the pool element. This client-side Pool can be used to configure connectivity directly to a server for individual entities or the entire cache through one or more Locators.

For example, to customize the default Pool used by the client-cache, the developer needs to define a Pool and wire it to the cache definition:

  <gfe:client-cache id="my-cache" pool-name="myPool"/>

  <gfe:pool id="myPool" subscription-enabled="true">
    <gfe:locator host="${gemfire.locator.host}" port="${gemfire.locator.port}"/>

The <client-cache> element also has a ready-for-events attribute. If set to true, the client cache initialization will include a call to ClientCache.readyForEvents().

Client-side configuration is covered in more detail in Client Region.

GemFire’s DEFAULT Pool and Spring Data GemFire Pool Definitions

If a GemFire ClientCache is local-only, then no Pool definition is required. For instance, a developer may define:


<gfe:client-region id="Example" shortcut="LOCAL"/>

In this case, the "Example" Region is LOCAL and no data is distributed between the client and a server, therefore, no Pool is necessary. This is true for any client-side, local-only Region, as defined by the GemFire’s ClientRegionShortcut (all LOCAL_* shortcuts).

However, if a client Region is a (caching) proxy to a server-side Region, then a Pool is required. There are several ways to define and use a Pool in this case.

When a client cache, Pool and proxy-based Region are all defined, but not explicitly identified, Spring Data GemFire will resolve the references automatically for you.

For example:


  <gfe:locator host="${geode.locator.host}" port="${geode.locator.port}"/>

<gfe:client-region id="Example" shortcut="PROXY"/>

In the example above, the client cache is identified as gemfireCache, the Pool as gemfirePool and the client Region as "Example". However, the client cache will initialize GemFire’s DEFAULT Pool from gemfirePool and the client Region will use the gemfirePool when distributing data between the client and the server.

Basically, Spring Data GemFire resolves the above configuration to the following:

<gfe:client-cache id="gemfireCache" pool-name="gemfirePool"/>

<gfe:pool id="gemfirePool">
  <gfe:locator host="${geode.locator.host}" port="${geode.locator.port}"/>

<gfe:client-region id="Example" cache-ref="gemfireCache" pool-name="gemfirePool" shortcut="PROXY"/>

GemFire still creates a Pool called "DEFAULT". Spring Data GemFire will just cause the "DEFAULT" Pool to be initialized from the gemfirePool. This is useful in situations where multiple Pools are defined and client Regions are using separate Pools.

Consider the following:

<gfe:client-cache pool-name="locatorPool"/>

<gfe:pool id="locatorPool">
  <gfe:locator host="${geode.locator.host}" port="${geode.locator.port}"/>

<gfe:pool id="serverPool">
  <gfe:server host="${geode.server.host}" port="${geode.server.port}"/>

<gfe:client-region id="Example" pool-name="serverPool" shortcut="PROXY"/>

<gfe:client-region id="AnotherExample" shortcut="CACHING_PROXY"/>

<gfe:client-region id="YetAnotherExample" shortcut="LOCAL"/>

In this setup, the GemFire client cache’s "DEFAULT" Pool is initialized from "locatorPool" as specified with the pool-name attribute. There is no Spring Data GemFire-defined gemfirePool since both Pools were explicitly identified (named) "locatorPool" and "serverPool", respectively.

The "Example" Region explicitly refers to and uses the "serverPool" exclusively. The "AnotherExample" Region uses GemFire’s "DEFAULT" Pool, which was configured from the "locatorPool" based on the client cache bean definition’s pool-name attribute.

Finally, the "YetAnotherExample" Region will not use a Pool since it is LOCAL.

The "AnotherExample" Region would first look for a Pool bean named gemfirePool, but that would require the definition of an anonymous Pool bean (i.e. <gfe:pool/>) or a Pool bean explicitly named gemfirePool (e.g. <gfe:pool id="gemfirePool"/>).
We could have either named "locatorPool", "gemfirePool", or made the Pool bean definition anonymous and it would have the same effect as the above configuration.

5.5. Configuring a Region

A Region is required to store and retrieve data from the cache. org.apache.geode.cache.Region is an interface extending java.util.Map and enables basic data access using familiar key-value semantics. The Region interface is wired into application classes that require it so the actual Region type is decoupled from the programming model. Typically, each Region is associated with one domain object, similar to a table in a relational database.

GemFire implements the following types of Regions:

  • REPLICATE - Data is replicated across all cache members that define the Region. This provides very high read performance but writes take longer to perform the replication.

  • PARTITION - Data is partitioned into buckets (sharded) among cache members that define the Region. This provides high read and write performance and is suitable for large data sets that are too big for a single node.

  • LOCAL - Data only exists on the local node.

  • Client - Technically, a client Region is a LOCAL Region that acts as a PROXY to a REPLICATE or PARTITION Region hosted on cache servers in a cluster. It may hold data created or fetched locally. Alternately, it can be empty. Local updates are synchronized to the cache server. Also, a client Region may subscribe to events in order to stay up-to-date (synchronized) with changes originating from remote processes that access the same server Region.

For more information about the various Region types and their capabilities as well as configuration options, please refer to Pivotal GemFire’s documentation on Region Types.

5.5.1. Using an externally configured Region

To reference Regions already configured in a GemFire native cache.xml file, use the lookup-region element. Simply declare the target Region name with the name attribute. For example, to declare a bean definition identified as ordersRegion for an existing Region named Orders, you can use the following bean definition:

<gfe:lookup-region id="ordersRegion" name="Orders"/>

If name is not specified, the bean’s id will be used as the name of the Region. The example above becomes:

<!-- lookup for a Region called 'Orders' -->
<gfe:lookup-region id="Orders"/>
If the Region does not exist, an initialization exception will be thrown. To configure new Regions, proceed to the appropriate sections below.

In the previous examples, since no cache name was explicitly defined, the default naming convention (gemfireCache) was used. Alternately, one can reference the cache bean with the cache-ref attribute:

<gfe:cache id="myCache"/>
<gfe:lookup-region id="ordersRegion" name="Orders" cache-ref="myCache"/>

lookup-region provides a simple way of retrieving existing, pre-configured Regions without exposing the Region semantics or setup infrastructure.

5.5.2. Auto Region Lookup

"auto-lookup" allows all Regions defined in a GemFire native cache.xml file to be imported into a Spring application context when using the`cache-xml-location` attribute on the <gfe:cache> element.

For instance, given a cache.xml file of…​

<?xml version="1.0" encoding="UTF-8"?>
<cache xmlns="http://geode.apache.org/schema/cache"
    xsi:schemaLocation="http://geode.apache.org/schema/cache http://geode.apache.org/schema/cache/cache-1.0.xsd"

  <region name="Parent" refid="REPLICATE">
    <region name="Child" refid="REPLICATE"/>


A developer may import the cache.xml file as follows…​

<gfe:cache cache-xml-location="cache.xml"/>

The developer may then use the <gfe:lookup-region> element (e.g. <gfe:lookup-region id="Parent"/>) to reference specific Regions as beans in the Spring context, or the user may choose to import all Regions defined in cache.xml with:


Spring Data GemFire will automatically create beans for all GemFire Regions defined in cache.xml that have not been explicitly added to the Spring context with explicit <gfe:lookup-region> bean declarations.

It is important to realize that Spring Data GemFire uses a Spring BeanPostProcessor to post process the cache after it is both created and initialized to determine the Regions defined in GemFire to add as beans in the Spring application context.

You may inject these "auto-looked-up" Regions like any other bean defined in the Spring application context with 1 exception; you may need to define a depends-on association with the ‘gemfireCache’ bean as follows…​

package example;

import ...

public class ApplicationDao extends DaoSupport {

    @Resource(name = "Parent")
    private Region<?, ?> parent;

    @Resource(name = "/Parent/Child")
    private Region<?, ?> child;


The example above is applicable when using Spring’s component-scan functionality.

If you are declaring your components using Spring XML config, then you would do…​

<bean class="example.ApplicationDao" depends-on="gemfireCache"/>

This ensures the GemFire cache and all the Regions defined in cache.xml get created before any components with auto-wire references when using the new <gfe:auto-region-lookup> element.

5.5.3. Configuring Regions

Spring Data GemFire provides comprehensive support for configuring any type of Region via the following elements:

  • LOCAL Region: <local-region>

  • PARTITION Region: <partitioned-region>

  • REPLICATE Region: <replicated-region>

  • Client Region: <client-region>

Please see the Pivotal GemFire documentation for a comprehensive description of Region Types.

Common Region Attributes

The following table lists attributes available for all Region types:

Table 1. Common Region Attributes
Name Values Description


GemFire Cache bean reference

The name of the bean defining the GemFire Cache (by default 'gemfireCache').


boolean, default:false

When true, the updates are applied to a clone of the value and then the clone is saved to the cache. When false, the value is modified in place in the cache.


boolean, default:false

Determines whether the Region should be closed at shutdown.


boolean, default:true

Determines whether members perform checks to provide consistent handling for concurrent or out-of-order updates to distributed Regions.


See GemFire’s Data Policy

The Region’s Data Policy. Note, not all Data Policies are supported for every Region type.


boolean, default:false

Determines whether the Region should be destroyed at shutdown.


The name of a configured Disk Store.

A reference to a bean created via the disk-store element.


boolean, default:true

Determines whether Disk Store writes are synchronous.


Any valid bean name.

Will be the Region name by default if no name attribute is specified.


boolean, default:false

Ignores this bean definition if the Region already exists in the cache, resulting in a lookup instead.


boolean, default:false

Determines whether this Region will participate in JTA transactions.


synchronous or asynchronous, default:synchronous

Determines whether Indices will be updated synchronously or asynchronously on entry creation.


integer, default:16

The initial memory allocation for the number of Region entries.


Any valid, fully-qualified Java class name.

Expected key type.


float, default:.75

Sets the initial parameters on the underlying java.util.ConcurrentHashMap used for storing Region entries.


Any valid Region name.

The name of the Region. If not specified, it will assume the value of the id attribute (a.k.a. bean name).


*boolean, default:false

Determines whether the Region will persist entries to local disk (Disk Store).


See http://geode.apache.org/releases/latest/javadoc/org/apache/geode/cache/RegionShortcut.html

The RegionShortcut for this Region. Allows easy initialization of the Region based on pre-defined defaults.


boolean, default:false

Determines whether the Region reports statistics.


The name of a Region Template.

A reference to a bean created via one of the *region-template elements.


Any valid, fully-qualified Java class name.

Expected value type.


CacheListeners are registered with a Region to handle Region events such as when entries are created, updated, destroyed and so on. A CacheListener can be any bean that implements the CacheListener interface. A Region may have multiple listeners, declared using the cache-listener element nested in the containing *-region element.

In the example below, there are two CacheListener’s declared. The first references a named, top-level Spring bean; the second is an anonymous inner bean definition.

<gfe:replicated-region id="regionWithListeners">
    <!-- nested CacheListener bean reference -->
    <ref bean="myListener"/>
    <!-- nested CacheListener bean definition -->
    <bean class="org.example.app.geode.cache.AnotherSimpleCacheListener"/>

  <bean id="myListener" class="org.example.app.geode.cache.SimpleCacheListener"/>

The following example uses an alternate form of the cache-listener element with the ref attribute. This allows for more concise configuration when defining a single CacheListener. Note, the namespace only allows a single cache-listener element so either the style above or below must be used.

Using ref and a nested declaration in the cache-listener element is illegal. The two options are mutually exclusive and using both in the same element will result in an exception.
  <gfe:replicated-region id="exampleReplicateRegionWithCacheListener">
    <gfe:cache-listener ref="myListener"/>

  <bean id="myListener" class="example.CacheListener"/>
Bean Reference Conventions

The cache-listener element is an example of a common pattern used in the namespace anywhere GemFire provides a callback interface to be implemented in order to invoke custom code in response to Cache or Region events. Using Spring’s IoC container, the implementation is a standard Spring bean. In order to simplify the configuration, the schema allows a single occurrence of the cache-listener element, but it may contain nested bean references and inner bean definitions in any combination if multiple instances are permitted. The convention is to use the singular form (i.e., cache-listener vs cache-listeners) reflecting that the most common scenario will in fact be a single instance. We have already seen examples of this pattern in the advanced cache configuration example.

CacheLoaders and CacheWriters

Similar to cache-listener, the namespace provides cache-loader and cache-writer elements to register these GemFire components respectively for a Region.

A CacheLoader is invoked on a cache miss to allow an entry to be loaded from an external data source, such as a database. A CacheWriter is invoked before an entry is created or updated, intended for synchronizing to an external data source. The difference is GemFire only supports at most a single instance CacheLoader and CacheWriter per Region. However, either declaration style may be used.


  <gfe:replicated-region id="exampleReplicateRegionWithCacheLoaderAndCacheWriter">
    <gfe:cache-loader ref="myLoader"/>
      <bean class="example.CacheWriter"/>

  <bean id="myLoader" class="example.CacheLoader">
    <property name="dataSource" ref="mySqlDataSource"/>

  <!-- DataSource bean definition -->

See CacheLoader and CacheWriter in the Pivotal GemFire documentation for more details.

5.5.4. Compression

GemFire Regions may also be compressed in order to reduce JVM memory consumption and pressure to possibly avoid stop the world GCs. When you enable compression for a Region, all values stored in the Region, in-memory are compressed while keys and indexes remain uncompressed. New values are compressed when put into Region and all values are decompressed automatically when read back from the Region. Values are not compressed when persisted to disk or when sent over the wire to other peer members or clients.


  <gfe:replicated-region id="exampleReplicateRegionWithCompression">
      <bean class="org.apache.geode.compression.SnappyCompressor"/>

Please refer to Pivotal GemFire’s documentation for more information on Region Compression.

5.5.5. Subregions

Spring Data GemFire also supports Subregions, allowing Regions to be arranged in a hierarchical relationship.

For example, GemFire allows for a /Customer/Address Region and a different /Employee/Address Region. Additionally, a Subregion may have it’s own Subregions and its own configuration. A Subregion does not inherit attributes from the parent Region. Regions types may be mixed and matched subject to GemFire constraints. A Subregion is naturally declared as a child element of a Region. The Subregion’s name attribute is the simple name. The above example might be configured as:

  <gfe:replicated-region name="Customer">
    <gfe:replicated-region name="Address"/>

  <gfe:replicated-region name="Employee">
    <gfe:replicated-region name="Address"/>

Note that the Monospaced ([id]) attribute is not permitted for a Subregion. The Subregions will be created with bean names /Customer/Address and /Employee/Address, respectively. So they may be injected using the full path name into other application beans that need them, such as GemfireTemplate. The full path should also be used in OQL query strings.

5.5.6. Region Templates

Spring Data GemFire also supports Region Templates. This feature allows developers to define common Region configuration settings and attributes once and reuse the configuration among many Region bean definitions declared in the Spring application context.

Spring Data GemFire includes 5 Region template tags in namespace:

Table 2. Region Template Tags
Tag Name Description


Defines common, generic Region attributes; extends regionType in the namespace.


Defines common, 'Local' Region attributes; extends localRegionType in the namespace.


Defines common, 'PARTITION' Region attributes; extends partitionedRegionType in the namespace.


Defines common, 'REPLICATE' Region attributes; extends replicatedRegionType in the namespace.


Defines common, 'Client' Region attributes; extends clientRegionType in the namespace.

In addition to the tags, concrete <gfe:*-region> elements along with the abstract <gfe:*-region-template> elements have a template attribute used to define the Region Template from which the Region will inherit its configuration. Region Templates may even inherit from other Region Templates.

Here is an example of 1 possible configuration…​

  <gfe:async-event-queue id="AEQ" persistent="false" parallel="false" dispatcher-threads="4">
      <bean class="example.AeqListener"/>

  <gfe:region-template id="BaseRegionTemplate" initial-capacity="51" load-factor="0.85" persistent="false" statistics="true"
      key-constraint="java.lang.Long" value-constraint="java.lang.String">
      <bean class="example.CacheListenerOne"/>
      <bean class="example.CacheListenerTwo"/>
    <gfe:entry-ttl timeout="600" action="DESTROY"/>
    <gfe:entry-tti timeout="300 action="INVLIDATE"/>

  <gfe:region-template id="ExtendedRegionTemplate" template="BaseRegionTemplate" load-factor="0.55">
      <bean class="example.CacheLoader"/>
      <bean class="example.CacheWriter"/>
    <gfe:async-event-queue-ref bean="AEQ"/>

  <gfe:partitioned-region-template id="PartitionRegionTemplate" template="ExtendedRegionTemplate"
      copies="1" load-factor="0.70" local-max-memory="1024" total-max-memory="16384" value-constraint="java.lang.Object">
      <bean class="example.PartitionResolver"/>
    <gfe:eviction type="ENTRY_COUNT" threshold="8192000" action="OVERFLOW_TO_DISK"/>

  <gfe:partitioned-region id="TemplateBasedPartitionRegion" template="PartitionRegionTemplate"
      copies="2" local-max-memory="8192" persistent="true" total-buckets="91"/>

Region Templates work for Subregions as well. Notice that 'TemplateBasedPartitionRegion' extends 'PartitionRegionTemplate', which extends 'ExtendedRegionTemplate' that extends 'BaseRegionTemplate'. Attributes and sub-elements defined in subsequent, inherited Region bean definitions override what is in the parent.

How Templating Works

Spring Data GemFire applies Region Templates when the Spring application context configuration meta-data is parsed, and therefore, must be declared in the order of inheritance. In other words, parent templates must be defined before children. This ensures the proper configuration is applied, especially when element attributes or sub-elements are "overridden".

It is equally important to remember the Region types must only inherit from other similar typed Regions. For instance, it is not possible for a <gfe:replicated-region> to inherit from a <gfe:partitioned-region-template>.
Region Templates are single-inheritance.
Caution concerning Regions, Subregions and Lookups

Previously, one of the underlying properties of the replicated-region, partitioned-region, local-region and client-region elements in the Spring Data GemFire XML namespace was to perform a lookup first before attempting to create a Region. This was done in case the Region already existed, which would be the case if the Region was defined in an imported GemFire native cache.xml configuration file. Therefore, the lookup was performed first to avoid any errors. This was by design and subject to change.

This behavior has been altered and the default behavior is now to create the Region first. If the Region already exists, then the creation logic fails-fast and an appropriate exception is thrown. However, much like the CREATE TABLE IF NOT EXISTS …​ DDL syntax, the Spring Data GemFire <*-region> namespace elements now includes a ignore-if-exists attribute, which re-instates the old behavior by performing a lookup of an existing Region identified by name, first. If an existing Region by name is found and ignore-if-exists is set to true, then the Region bean definition defined in Spring config is ignored.

The Spring team highly recommends that the replicated-region, partitioned-region, local-region and client-region namespace elements be strictly used for defining new Regions only. One problem that could arise if the Regions defined by these elements already existed and the Region elements performed a lookup first is if the developer defined different Region semantics and behaviors for eviction, expiration, subscription, etc in his/her application config, then the Region definition may not match and could exhibit contrary behaviors to those required by the application. Even worse, the application developer may want to define the Region as a distributed Region (e.g. PARTITION) but in fact the existing Region definition is LOCAL.
Recommended Practice - Only use replicated-region, partitioned-region, local-region and client-region namespace elements to define new Regions.

Consider the following native GemFire cache.xml configuration file…​

<?xml version="1.0" encoding="UTF-8"?>
<cache xmlns="http://geode.apache.org/schema/cache"
    xsi:schemaLocation="http://geode.apache.org/schema/cache http://geode.apache.org/schema/cache/cache-1.0.xsd"

  <region name="Customers" refid="REPLICATE">
    <region name="Accounts" refid="REPLICATE">
      <region name="Orders" refid="REPLICATE">
        <region name="Items" refid="REPLICATE"/>


Also consider that you may have defined an application DAO as follows…​

public class CustomerAccountDao extends GemDaoSupport {

    @Resource(name = "Customers/Accounts")
    private Region customersAccounts;


Here, we are injecting a reference to the Customers/Accounts Region in our application DAO. As such, it is not uncommon for a developer to define beans for all or even some of these Regions in Spring XML configuration meta-data as follows…​

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
         http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
         http://www.springframework.org/schema/geode http://www.springframework.org/schema/gemfire/spring-geode.xsd

  <gfe:cache cache-xml-location="classpath:cache.xml"/>

  <gfe:lookup-region name="Customers/Accounts"/>
  <gfe:lookup-region name="Customers/Accounts/Orders"/>


The Customers/Accounts and Customers/Accounts/Orders Regions are referenced as beans in the Spring application context as "Customers/Accounts" and "Customers/Accounts/Orders", respectively. The nice thing about using the lookup-region element and the corresponding syntax above is that it allows a developer to reference a Subregion directly without unnecessarily defining a bean for the parent Region (i.e. Customers).

However, if now the developer changes his/her configuration meta-data syntax to using the nested format, like so…​

<gfe:lookup-region name="Customers">
  <gfe:lookup-region name="Accounts">
    <gfe:lookup-region name="Orders"/>

Or, perhaps the developer erroneously chooses to use the top-level replicated-region element along with the ignore-if-exists attribute set to perform a lookup first, as in…​

<gfe:replicated-region name="Customers" persistent="true" ignore-if-exists="true">
  <gfe:replicated-region name="Accounts" persistent="true" ignore-if-exists="true">
    <gfe:replicated-region name="Orders" persistent="true" ignore-if-exists="true"/>

Then the Region beans defined in the Spring application context will consist of the following: { "Customers", "/Customers/Accounts", "/Customers/Accounts/Orders" }. This means the dependency injected reference above (i.e. @Resource(name = "Customers/Accounts")) is now broken since no bean with name "Customers/Accounts" is actually defined.

GemFire is flexible in referencing both parent Regions and Subregions with or without the leading forward slash. For example, the parent can be referenced as "/Customers" or "Customers" and the child as "/Customers/Accounts" or just "Customers/Accounts". However, _Spring Data _GemFire is very specific when it comes to naming beans after Regions, typically always using the forward slash (/) to represent Subregions (e.g. "/Customers/Accounts").

Therefore, it is recommended that users either use the nested lookup-region syntax as shown above, or define direct references with a leading forward slash (/) like so…​

<gfe:lookup-region name="/Customers/Accounts"/>
<gfe:lookup-region name="/Customers/Accounts/Orders"/>

The example above where the nested replicated-region elements were used to reference the Subregions serves to illustrate the problem stated earlier. Are the Customers, Accounts and Orders Regions/Subregions persistent or not? Not, since the Regions were defined in the native GemFire cache.xml configuration file as REPLICATES and will exist by the time the cache is initialized, or once the <gfe:cache> bean is processed.

5.5.7. Data Eviction (with Overflow)

Based on various constraints, each Region can have an eviction policy in place for evicting data from memory. Currently, in GemFire, eviction applies to the Least Recently Used entry (also known as LRU). Evicted entries are either destroyed or paged to disk (referred to as overflow to disk).

Spring Data GemFire supports all eviction policies (entry count, memory and heap usage) for PARTITION Regions, REPLICATE Regions and client, local Regions using the nested eviction element.

For example, to configure a PARTITION Region to overflow to disk if the memory size exceeds more than 512 MB, a developer would specify the following configuration:

<gfe:partitioned-region id="examplePartitionRegionWithEviction">
  <gfe:eviction type="MEMORY_SIZE" threshold="512" action="OVERFLOW_TO_DISK"/>
Replicas cannot use local destroy eviction since that would invalidate them. See the GemFire docs for more information.

When configuring Regions for overflow, it is recommended to configure the storage through the disk-store element for maximum efficiency.

For a detailed description of eviction policies, please refer to the GemFire documentation on Eviction.

5.5.8. Data Expiration

Pivotal GemFire allows you to control how long entries exist in the cache. Expiration is driven by elapsed time, as opposed to Eviction, which is driven by the entry count or heap/memory usage. Once an entry expires it may no longer be accessed from the cache.

GemFire supports the following Expiration types:

  • Time-to-Live (TTL) - The amount of time in seconds that an object may remain in the cache after the last creation or update. For entries, the counter is set to zero for create and put operations. Region counters are reset when the Region is created and when an entry has its counter reset.

  • Idle Timeout (TTI) - The amount of time in seconds that an object may remain in the cache after the last access. The Idle Timeout counter for an object is reset any time its TTL counter is reset. In addition, an entry’s Idle Timeout counter is reset any time the entry is accessed through a get operation or a netSearch. The Idle Timeout counter for a Region is reset whenever the Idle Timeout is reset for one of its entries.

Each of these may be applied to the Region itself or entries in the Region. Spring Data GemFire provides <region-ttl>, <region-tti>, <entry-ttl> and <entry-tti> Region child elements to specify timeout values and expiration actions.

For example:

<gfe:partitioned-region id="examplePartitionRegionWithExpiration">
  <gfe:region-ttl timeout="30000" action="INVALIDATE"/>
  <gfe:entry-tti timeout="600" action="LOCAL_DESTROY"/>

For a detailed description of expiration policies, please refer to the GemFire documentation on Expiration.

Annotation-based Data Expiration

With Spring Data GemFire, a developer has the ability to define Expiration policies and settings on individual Region Entry values, or rather, application domain objects directly. For instance, a developer might define Expiration settings on a Session-based application domain object like so…​

@Expiration(timeout = "1800", action = "INVALIDATE")
public class SessionBasedApplicationDomainObject {

In addition, a developer may also specify Expiration type specific settings on Region Entries using @IdleTimeoutExpiration and @TimeToLiveExpiration annotations for Idle Timeout (TTI) and Time-to-Live (TTL) Expiration, respectively…​

@TimeToLiveExpiration(timeout = "3600", action = "LOCAL_DESTROY")
@IdleTimeoutExpiration(timeout = "1800", action = "LOCAL_INVALIDATE")
@Expiration(timeout = "1800", action = "INVALIDATE")
public class AnotherSessionBasedApplicationDomainObject {

Both @IdleTimeoutExpiration and @TimeToLiveExpiration take precedence over the generic @Expiration annotation when more than one Expiration annotation type is specified, as shown above. Though, neither @IdleTimeoutExpiration nor @TimeToLiveExpiration overrides the other; rather they may compliment each other when different Region Entry Expiration types, such as TTL and TTI, are configured.

All @Expiration-based annotations apply only to Region Entry values. Expiration for a "Region" is not covered by Spring Data GemFire’s Expiration annotation support. However, Pivotal GemFire and Spring Data GemFire do allow you to set Region Expiration using the SDG XML namespace, like so…​

<gfe:*-region id="Example" persistent="false">
  <gfe:region-ttl timeout="600" action="DESTROY"/>
  <gfe:region-tti timeout="300" action="INVALIDATE"/>

Spring Data GemFire’s @Expiration annotation support is implemented with GemFire’s CustomExpiry interface. Refer to GemFire’s documentation on Configuring Data Expiration for more details

The Spring Data GemFire AnnotationBasedExpiration class (and CustomExpiry implementation) is responsible for processing the SDG @Expiration annotations and applying the Expiration policy and settings appropriately for Region Entry Expiration on request.

To use Spring Data GemFire to configure specific GemFire Regions to appropriately apply the Expiration policy and settings applied to your application domain objects annotated with @Expiration-based annotations, you must…​

  1. Define a bean in the Spring ApplicationContext of type AnnotationBasedExpiration using the appropriate constructor or one of the convenient factory methods. When configuring Expiration for a specific Expiration type, such as Idle Timeout or Time-to-Live, then you should use one of the factory methods in the AnnotationBasedExpiration class, like so…​

    <bean id="ttlExpiration" class="org.springframework.data.gemfire.expiration.AnnotationBasedExpiration"
    <gfe:partitioned-region id="Example" persistent="false">
        <gfe:custom-entry-ttl ref="ttlExpiration"/>

    To configure Idle Timeout (TTI) Expiration instead, then you would of course use the forIdleTimeout factory method along with the <gfe:custom-entry-tti ref="ttiExpiration"/> element to set TTI.

  2. (optional) Annotate your application domain objects that will be stored in the Region with Expiration policies and custom settings using one of Spring Data GemFire’s @Expiration annotations: @Expiration, @IdleTimeoutExpiration and/or @TimeToLiveExpiration

  3. (optional) In cases where particular application domain objects have not been annotated with Spring Data GemFire’s @Expiration annotations at all, but the GemFire Region is configured to use SDG’s custom AnnotationBasedExpiration class to determine the Expiration policy and settings for objects stored in the Region, then it is possible to set "default" Expiration attributes on the AnnotationBasedExpiration bean by doing the following…​

<bean id="defaultExpirationAttributes" class="org.apache.geode.cache.ExpirationAttributes">
    <constructor-arg value="600"/>
    <constructor-arg value="#{T(org.apache.geode.cache.ExpirationAction).DESTROY}"/>

<bean id="ttiExpiration" class="org.springframework.data.gemfire.expiration.AnnotationBasedExpiration"
    <constructor-arg ref="defaultExpirationAttributes"/>

<gfe:partitioned-region id="Example" persistent="false">
    <gfe:custom-entry-tti ref="ttiExpiration"/>

You may have noticed that Spring Data GemFire’s @Expiration annotations use a String as the attributes type rather than, and perhaps more appropriately, being strongly typed, i.e. int for 'timeout' and SDG’S ExpirationActionType for 'action'. Why is that?

Well, enter one of Spring Data GemFire’s other features, leveraging Spring’s core infrastructure for configuration convenience: Property Placeholders and Spring Expression Language (SpEL).

For instance, a developer can specify both the Expiration 'timeout' and 'action' using Property Placeholders in the @Expiration annotation attributes…​

@TimeToLiveExpiration(timeout = "${geode.region.entry.expiration.ttl.timeout}"
    action = "${geode.region.entry.expiration.ttl.action}")
public class ExampleApplicationDomainObject {

Then, in your Spring XML config or in JavaConfig, you would declare the following beans…​

<util:properties id="expirationSettings">
  <prop key="geode.region.entry.expiration.ttl.timeout">600</prop>
  <prop key="geode.region.entry.expiration.ttl.action">INVALIDATE</prop>

<context:property-placeholder properties-ref="expirationProperties"/>

This is both convenient when multiple application domain objects might share similar Expiration policies and settings, or when you wish to externalize the configuration.

However, a developer may want more dynamic Expiration configuration determined by the state of the running system. This is where the power of SpEL comes in and is the recommended approach, actually. Not only can you refer to beans in the Spring context and access bean properties, invoke methods, etc, the values for Expiration 'timeout' and 'action' can be strongly typed. For example (building on the example above)…​

<util:properties id="expirationSettings">
  <prop key="geode.region.entry.expiration.ttl.timeout">600</prop>
  <prop key="geode.region.entry.expiration.ttl.action">#{T(org.springframework.data.gemfire.expiration.ExpirationActionType).DESTROY}</prop>
  <prop key="geode.region.entry.expiration.tti.action">#{T(org.apache.geode.cache.ExpirationAction).INVALIDATE}</prop>

<context:property-placeholder properties-ref="expirationProperties"/>

Then, on your application domain object…​

@TimeToLiveExpiration(timeout = "@expirationSettings['geode.region.entry.expiration.ttl.timeout']"
    action = "@expirationSetting['geode.region.entry.expiration.ttl.action']")
public class ExampleApplicationDomainObject {

You can imagine that the 'expirationSettings' bean could be a more interesting and useful object rather than a simple instance of java.util.Properties. In this example, even the Properties (expirationSettings) uses SpEL to base the action value on the actual Expiration action enumerated type leading to more quickly identified failures if the types ever change.

All of this has been demonstrated and tested in the Spring Data GemFire test suite, by way of example. See the source for further details.

5.5.9. Data Persistence

Regions can be persistent. GemFire ensures that all the data you put into a Region that is configured for persistence will be written to disk in a way that is recoverable the next time you recreate the Region. This allows data to be recovered after machine or process failure, or even after an orderly shutdown and subsequent restart of the GemFire data node.

To enable persistence with Spring Data GemFire, simply set the persistent attribute to true on any of the <*-region> elements. For example…​

<gfe:partitioned-region id="examplePersitentPartitionRegion" persistent="true"/>

Persistence may also be configured using the data-policy attribute; set the attribute’s value to one of GemFire’s DataPolicy settings. For example…​

<gfe:partitioned-region id="anotherExamplePersistentPartitionRegion" data-policy="PERSISTENT_PARTITION"/>

The DataPolicy must match the Region type and must also agree with the persistent attribute if also explicitly set. An initialization exception will be thrown if the persistent attribute is set to false yet a persistent DataPolicy was specified (e.g. PERSISTENT_REPLICATE, PERSISTENT_PARTITION).

When persisting Regions, it is recommended to configure the storage through the disk-store element for maximum efficiency. The DiskStore is referenced using the disk-store-ref attribute. Additionally, the Region may perform disk writes synchronously or asynchronously:

<gfe:partitioned-region id="yetAnotherExamplePersistentPartitionRegion" persistent="true"
    disk-store-ref="myDiskStore" disk-synchronous="true"/>

This is discussed further in Configuring a DiskStore

5.5.10. Subscription Policy

GemFire allows configuration of peer-to-peer (P2P) event messaging to control the entry events that the Region will receive. Spring Data GemFire provides the <gfe:subscription/> sub-element to set the subscription policy on REPLICATE and PARTITION Regions to either ALL or CACHE_CONTENT.

<gfe:partitioned-region id="examplePartitionRegionWithCustomSubscription">
  <gfe:subscription type="CACHE_CONTENT"/>

5.5.11. Local Region

Spring Data GemFire offers a dedicated local-region element for creating local Regions. Local Regions, as the name implies, are standalone, meaning they do not share data with any other distributed system member. Other than that, all common Region configuration options apply.

A minimal declaration looks as follows (again, the example relies on the Spring Data GemFire namespace naming conventions to wire the cache):

<gfe:local-region id="exampleLocalRegion"/>

Here, a local Region is created (if one doesn’t exist already). The name of the Region is the same as the bean id (exampleLocalRegion) and the bean assumes the existence of a GemFire cache named gemfireCache.

5.5.12. Replicated Region

One of the common Region types is a REPLICATE Region or replica. In short, when a Region is configured to be a REPLICATE, every member that hosts the Region stores a copy of the Region’s entries locally. Any update to a REPLICATE Region is distributed to all copies of the Region. When a replica is created, it goes through an initialization stage in which it discovers other replicas and automatically copies all the entries. While one replica is initializing you can still continue to use the other replica.

Spring Data GemFire offers a replicated-region element. A minimal declaration looks as follows. All common configuration options are available for REPLICATE Regions.

<gfe:replicated-region id="exampleReplica"/>

Refer to GemFire’s documentation on Distributed and Replicated Regions for more details.

5.5.13. Partitioned Region

Another Region type supported out-of-the-box by the Spring Data GemFire namespace is the PARTITION Region.

To quote the GemFire docs:

"A partitioned region is a region where data is divided between peer servers hosting the region so that each peer stores a subset of the data. When using a partitioned region, applications are presented with a logical view of the region that looks like a single map containing all of the data in the region. Reads or writes to this map are transparently routed to the peer that hosts the entry that is the target of the operation. GemFire divides the domain of hashcodes into buckets. Each bucket is assigned to a specific peer, but may be relocated at any time to another peer in order to improve the utilization of resources across the cluster."

A partition is created using the partitioned-region element. Its configuration options are similar to that of the replicated-region plus the partition specific features such as the number of redundant copies, total maximum memory, number of buckets, partition resolver and so on.

Below is a quick example on setting up a PARTITION Region with 2 redundant copies:

<gfe:partitioned-region id="examplePartitionRegion" copies="2" total-buckets="17">
    <bean class="example.PartitionResolver"/>

Refer to GemFire’s documentation on Partitioned Regions for more details.

Partitioned Region Attributes

The following table offers a quick overview of configuration options specific to PARTITION Regions. These are in addition to the common Region configuration options described above.

Table 3. partitioned-region attributes
Name Values Description



The number of copies for each partition for high-availability. By default, no copies are created meaning there is no redundancy. Each copy provides extra backup at the expense of extra storage.


valid region name

The name of the PARTITION Region with which this newly created PARTITION Region is collocated.


positive integer

The maximum amount of memory in megabytes used by the Region in this process.


any integer value

The maximum amount of memory in megabytes used by the Region in all processes.


bean name

The name of the PartitionListener used by this Region, for handling partition events.


bean name

The name of the PartitionResolver used by this Region, for custom partitioning.


any long value

The delay in milliseconds that existing members will wait before satisfying redundancy after another member crashes. -1 (the default) indicates that redundancy will not be recovered after a failure.


any long value

The delay in milliseconds that new members will wait before satisfying redundancy. -1 indicates that adding new members will not trigger redundancy recovery. The default is to recover redundancy immediately when a new member is added.

5.5.14. Client Region

Pivotal GemFire supports various deployment topologies for managing and distributing data. GemFire topologies is outside the scope of this documentation. However, to quickly recap, GemFire’s supported topologies can be classified in short as: peer-to-peer (p2p), client-server, and wide area network (WAN). In the last two configurations, it is common to declare client Regions which connect to a cache server.

Spring Data GemFire offers dedicated support for such configuration through client-cache, client-region and pool elements. As the names imply, the former defines a client Region while the latter defines a Pool of connections to be used/shared by the various client Regions.

Below is a typical client Region configuration:

<bean id="myListener" class="example.CacheListener"/>

<!-- client Region using the default SDG gemfirePool Pool -->
<gfe:client-region id="Example">
  <gfe:cache-listener ref="myListener"/>

<!-- client Region using its own dedicated Pool -->
<gfe:client-region id="AnotherExample" pool-name="myPool">
  <gfe:cache-listener ref="myListener"/>

<!-- Pool definition -->
<gfe:pool id="myPool" subscription-enabled="true">
  <gfe:locator host="remoteHost" port="12345"/>

As with the other Region types, client-region supports CacheListener``s as well as a CacheLoader and CacheWriter. It also requires a connection Pool for connecting to either a set of Locators or Servers. Each client Region can have its own Pool or they can share the same one.

In the above example, the Pool is configured with locator. A Locator is a separate process used to discover cache servers and peer data members in the distributed system and are recommended for production systems. It is also possible to configure the Pool to connect directly to one or more cache servers using the server element.

For a full list of options to set on the client and especially on the Pool, please refer to the Spring Data GemFire schema (Spring Data GemFire Schema) and GemFire’s documentation on Client/Server Configuration.

Client Interests

To minimize network traffic, each client can separately define its own 'interests' policies, indicating to GemFire the data it actually requires. In Spring Data GemFire, 'interests' can be defined for each client Region separately. Both Key-based and Regular Expression-based interest types are supported.

For example:

<gfe:client-region id="Example" pool-name="myPool">
    <gfe:key-interest durable="true" result-policy="KEYS">
        <bean id="key" class="java.lang.String">
             <constructor-arg value="someKey"/>
    <gfe:regex-interest pattern=".*" receive-values="false"/>

A special key, ALL_KEYS, means 'interest' is registered for all keys. The same can be accomplished using a regex of ".\*".

The <gfe:*-interest> Key and Regular Expression elements support 3 attributes: durable, receive-values and result-policy.

durable indicates whether the 'interest' policy and subscription queue created for the client when the client connects to 1 or more servers in the cluster is maintained across client sessions. If the client goes away and comes back, a "durable" subscription queue on the server(s) for the client is maintained while the client is disconnected, and when the client reconnects, the client will receive any events that occurred while the client was disconnected from the servers(s) in the cluster.

A subscription queue on the servers in the cluster is maintained for each Pool of connections defined in the client where subscription has also been "enabled" for that Pool. The subscription queue is used to store, and possibly conflate, events sent to the client. If the subscription queue is durable, it persists between client sessions (i.e. connections), potentially up to a specified timeout (if the client does not return within a given time frame in order to reduce resource consumption on servers in the cluster). If the subscription queue is not "durable", then it will be destroyed when the client disconnects. All you need to decide is, for your application use case, is it important for the cache client to receive events while it is disconnected, or is it only important for the application (cache client) to receive the "latest" events after it reconnects.

The receive-values attribute indicates whether or not the entry values are received for create and update events. If true, values are received; if false, only invalidation events are received.

And finally, the 'result-policy` is an enumeration of: KEYS, KEYS_VALUE and NONE. The default is KEYS_VALUES. The result-policy controls the initial dump when the client first connects to initialize the local cache, essentially seeding the client with events for all the entries that match the interest policy.

Client-side interests registration does not do much good without enabling subscription on the Pool as mentioned above. In fact, it is an error to attempt interests registration without subscription enabled. To do so, you simply…​

<gfe:pool ... subscription-enabled="true">

In addition to subscription-enabled, can you also set subscription-ack-interval, subscription-message-tracking-timeout and subscription-redundancy. subscription-redundancy is used to control how many copies of the subscription queue should be maintained by the servers in the cluster. If redundancy is greater than 1, and the "primary" subscription queue (i.e. server) goes down, then a "secondary" subscription queue will take over, keeping the client from missing events in a HA scenario.

In addition to the Pool settings, the server-side Regions use an additional attribute, enable-subscription-conflation, to control the conflation of events that will be sent to the clients. This can also help further minimize network traffic and is useful in situations where the application only cares about the latest value of an entry. However, in cases where the application is keeping a time series of events that occurred, conflation is going to hinder that use case. The default value is false. An example Region configuration on the server for which the client contains a corresponding client [CACHING_]PROXY Region with interests in Keys in this server Region, would look like…​

<gfe:partitioned-region name="ServerSideRegion" enable-subscription-conflation="true">

To control the amount of time in seconds that "durable" subscription queue is maintained after a client is disconnected from the server(s) in the cluster, set the durable-client-timeout attribute on the <gfe:client-cache> element like so…​

<gfe:client-cache durable-client-timeout="600">

A full, in-depth discussion of how client interests work and capabilities is beyond the scope of this document.

Please refer to Pivotal GemFire’s documentation on Client-to-Server Event Distribution for more details.

5.5.15. JSON Support

Pivotal GemFire has support for caching JSON documents in Regions along with the ability to query stored JSON documents using the GemFire OQL. JSON documents are stored internally as PdxInstance types using the JSONFormatter class to perform conversion to and from JSON documents (as a String).

Spring Data GemFire provides the <gfe-data:json-region-autoproxy/> element to enable a AOP, Spring component to advise appropriate, proxied Region operations, which effectively encapsulates the JSONFormatter, thereby allowing your applications to work directly with JSON Strings.

In addition, Java objects written to JSON configured Regions will be automatically converted to JSON using Jackson’s ObjectMapper. Reading these values back will be returned as a JSON String.

By default, <gfe-data:json-region-autoproxy/> performs the conversion for all Regions. To apply this feature to selected Regions, provide a comma delimited list of Region bean ids via the region-refs attribute. Other attributes include a pretty-print flag (defaults to false) and convert-returned-collections.

Also by default, the results of the getAll() and values() Region operations will be converted for configured Regions. This is done by creating a parallel data structure in local memory. This can incur significant overhead for large collections, so set the convert-returned-collections to false if you would like to disable automatic conversion for these Region operations.

Certain Region operations, specifically those that use GemFire’s proprietary Region.Entry such as: entries(boolean), entrySet(boolean) and getEntry() type are not targeted for AOP advice. In addition, the entrySet() method which returns a Set<java.util.Map.Entry<?, ?>> is also not affected.

Example configuration:

<gfe-data:json-region-autoproxy region-refs="myJsonRegion" pretty-print="true" convert-returned-collections="false"/>

This feature also works seamlessly with GemfireTemplate operations, provided that the template is declared as a Spring bean. Currently, the native QueryService operations are not supported.

5.6. Configuring an Index

Pivotal GemFire allows Indexes (or Indices) to be created on Region data to improve the performance of OQL queries.

In Spring Data GemFire (SDG), Indexes are declared with the index element:

<gfe:index id="myIndex" expression="someField" from="/SomeRegion" type="HASH"/>

In Spring Data GemFire’s XML schema (a.k.a. SDG namespace), Index bean declarations are not bound to a Region, unlike GemFire’s native cache.xml. Rather, they are top-level elements just like <gfe:cache>. This allows a developer to declare any number of Indexes on any Region whether they were just created or already exist, a significant improvement over GemFire’s native cache.xml format.

An Index must have a name. A developer may give the Index an explicit name using the name attribute, otherwise the bean name (i.e. value of the id attribute) of the Index bean definition is used as the Index name.

The expression and from clause form the main components of an Index, identifying the data to index (i.e. the Region identified in the from clause) along with what criteria (i.e. expression) is used to index the data. The expression should be based on what application domain object fields are used in the predicate of application-defined OQL queries used to query and lookup the objects stored in the Region.

For example, if I have a Customer that has a lastName property…​

class Customer {

  Long id;

  String lastName;
  String firstName;


And, I also have an application defined SD[G] Repository to query for Customers…​

interface CustomerRepository extends GemfireRepository<Customer, Long> {

  Customer findByLastName(String lastName);


Then, the SD[G] Repository finder/query method would result in the following OQL statement being executed…​

SELECT * FROM /Customers c WHERE c.lastName = '$1'

Therefore, I might want to create an Index like so…​

<gfe:index id="myIndex" name="CustomersLastNameIndex" expression="lastName" from="/Customers" type="HASH"/>

The from clause must refer to a valid, existing Region and is how an Index gets applied to a Region. This is not Sprig Data GemFire specific; this is a feature of Pivotal GemFire.

The Index type maybe 1 of 3 enumerated values defined by Spring Data GemFire’s IndexType enumeration: FUNCTIONAL, HASH and PRIMARY_KEY.

Each of the enumerated values correspond to one of the QueryService create[|Key|Hash]Index methods invoked when the actual Index is to be created (or "defined"; more on "defining" Indexes below). For instance, if the IndexType is PRIMARY_KEY, then the QueryService.createKeyIndex(..) is invoked to create a KEY Index.

The default is FUNCTIONAL and results in one of the QueryService.createIndex(..) methods being invoked.

See the Spring Data GemFire XML schema for a full set of options.

For more information on Indexing in Pivotal GemFire, see Working with Indexes in Pivotal GemFire’s User Guide.

5.6.1. Defining Indexes

In addition to creating Indexes upfront as Index bean definitions are processed by Spring Data GemFire on Spring container initialization, you may also define all of your application Indexes prior to creating them by using the define attribute, like so…​

<gfe:index id="myDefinedIndex" expression="someField" from="/SomeRegion" define="true"/>

When define is set to true (defaults to false), this will not actually create the Index right then and there. All "defined" Indexes are created all at once, when the Spring ApplicationContext is "refreshed", or, that is, when a ContextRefreshedEvent is published by the Spring container. Spring Data GemFire registers itself as an ApplicationListener listening for the ContextRefreshedEvent. When fired, Spring Data GemFire will call QueryService.createDefinedIndexes().

Defining Indexes and creating them all at once helps promote speed and efficiency when creating Indexes.

See Creating Multiple Indexes at Once for more details.

5.6.2. IgnoreIfExists and Override

Two Spring Data GemFire Index configuration options warrant special mention here: ignoreIfExists and override.

These options correspond to the ignore-if-exists and override attributes on the <gfe:index> element in Spring Data GemFire’s XML schema, respectively.

Make sure you absolutely understand what you are doing before using either of these options. These options can affect the performance and/or resources (e.g. memory) consumed by your application at runtime. As such, both of these options are disabled (i.e. set to false) in SDG by default.
These options are only available in Spring Data GemFire and exist to workaround known limitations with Pivotal GemFire; there are no equivalent options or functionality available in GemFire itself.

Each option significantly differs in behavior and entirely depends on the type of GemFire Index Exception thrown. This also means that neither option has any effect if a GemFire Index-type Exception is not thrown. These options are meant to specifically handle GemFire IndexExistsExceptions and IndexNameConflictExceptions, which can occur for various, sometimes obscure reasons. But, in general…​

  • An IndexExistsException is thrown when there exists another Index with the same definition but different name when attempting to create an Index.

  • An IndexNameConflictException is thrown when there exists another Index with the same name but possibly different definition when attempting to create an Index.

Spring Data GemFire’s default behavior is to fail-fast, always! So, neither Index Exception will be "handled" by default; these Index Exceptions are simply wrapped in a SDG GemfireIndexException and rethrown. If you wish for Spring Data GemFire to handle them for you, then you can set either of these Index bean definition options.

IgnoreIfExists always takes precedence over Override, primarily because it uses less resources given it returns the "existing" Index in both exceptional cases.

IgnoreIfExists Behavior

When an IndexExistsException is thrown and ignoreIfExists is set to true (or <gfe:index ignore-if-exists="true">), then the Index that would have been created by this Index bean definition / declaration will be "ignored", and the "existing" Index will be returned.

There is very little consequence in returning the "existing" Index since the Index "definition" is the same, as deemed by GemFire itself, not SDG.

However, this also means that no Index with the “name” specified in your Index bean definition / declaration will "actually" exist from GemFire’s perspective either (i.e. with QueryService.getIndexes()). Therefore, you should be careful when writing OQL query statements that use Query Hints, especially Hints that refer to the application Index being "ignored". Those Query Hints will need to be changed.

Now, when an IndexNameConflictException is thrown and ignoreIfExists is set to true (or <gfe:index ignore-if-exists="true">), then the Index that would have been created by this Index bean definition / declaration will also be "ignored", and the "existing" Index will be returned, just like when an IndexExistsException is thrown.

However, there is more risk in returning the "existing" Index and "ignoring" the application’s definition of the Index when an IndexNameConflictException is thrown since, for a IndexNameConflictException, while the "names" of the conflicting Indexes are the same, the "definitions" could very well be different! This obviously could have implications for OQL queries specific to the application, where you would presume the Indexes were defined specifically with the application data access patterns and queries in mind. However, if like named Indexes differ in definition, this might not be the case. So, make sure you verify.

SDG makes a best effort to inform the user when the Index being ignored is significantly different in its definition from the "existing" Index. However, in order for SDG to accomplish this, it must be able to "find" the existing Index, which is looked up using the GemFire API (the only means available).
Override Behavior

When an IndexExistsException is thrown and override is set to true (or <gfe:index override="true">), then the Index is effectively "renamed". Remember, IndexExistsExceptions are thrown when multiple Indexes exist, all having the same "definition" but different "names".

Spring Data GemFire can only accomplish this using GemFire’s API, by first "removing" the "existing" Index and then "recreating" the Index with the new name. It is possible that either the remove or subsequent create invocation could fail. There is no way to execute both actions atomically and rollback this joint operation if either fails.

However, if it succeeds, then you have the same problem as before with the "ignoreIfExists" option. Any existing OQL query statement using "Query Hints" referring to the old Index by name must be changed.

Now, when an IndexNameConflictException is thrown and override is set to true (or <gfe:index override="true">), then potentially the "existing" Index will be "re-defined". I say "potentially", because it is possible for the "like-named", "existing" Index to have exactly the same definition and name when an IndexNameConflictException is thrown.

If so, SDG is smart and will just return the "existing" Index as is, even on override. There is no harm in this since both the "name" and the "definition" are exactly the same. Of course, SDG can only accomplish this when SDG is able to "find" the "existing" Index, which is dependent on GemFire’s APIs. If it cannot find it, nothing happens and a SDG GemfireIndexException is thrown wrapping the IndexNameConflictException.

However, when the "definition" of the "existing" Index is different, then SDG will attempt to "recreate" the Index using the Index definition specified in the Index bean definition /declaration. Make sure this is what you want and make sure the Index definition matches your expectations and application requirements.

How does IndexNameConflictExceptions actually happen?

It is probably not all that uncommon for IndexExistsExceptions to be thrown, especially when multiple configuration sources are used to configure GemFire (e.g. Spring Data GemFire, GemFire Cluster Config, maybe GemFire native cache.xml, the API, etc, etc). You should definitely prefer 1 configuration method here and stick with it.

However, when does an IndexNameConflictException get thrown?

One particular case is an Index defined on a PARTITION Region (PR). When an Index is defined on a PARTITION Region (e.g. "X"), GemFire distributes the Index definition (and name) to other peer members in the cluster that also host the same PARTITION Region (i.e. "X"). The distribution of this Index definition to and subsequent creation of this Index by peer members on a "need-to-know" basis (i.e. those hosting the same PR) is performed asynchronously.

During this window of time, it is possible that these "pending" PR Indexes will not be identifiable by GemFire, such as with a call to QueryService.getIndexes() or with QueryService.getIndexes(:Region), or even with QueryService.getIndex(:Region, indexName:String).

As such, the only way for SDG or other GemFire cache client applications (not involving Spring) to know for sure, is to just attempt to create the Index. If it fails with either an IndexNameConflictException, or even an IndexExistsException, then you will know. This is because the QueryService Index creation waits on "pending" Index definitions, where as the other GemFire API calls do not.

In any case, SDG makes a best effort and attempts to inform the user what has or is happening along with the corrective action. Given all GemFire QueryService.createIndex(..) methods are synchronous, "blocking" operations, then the state of GemFire should be consistent and accessible after either of these Index-type Exceptions are thrown, in which case, SDG can inspect the state of the system and respond/act accordingly, based on the user’s desired configuration.

In all other cases, SDG will simply fail-fast!

5.7. Configuring a DiskStore

Spring Data GemFire supports DiskStore configuration via the disk-store element.

For example:

<gfe:disk-store id="diskStore1" queue-size="50" auto-compact="true"
        max-oplog-size="10" time-interval="9999">
        <gfe:disk-dir location="/gemfire/store1/" max-size="20"/>
        <gfe:disk-dir location="/gemfire/store2/" max-size="20"/>

DiskStores are used by Regions for file system persistent backup and overflow of evicted entries as well as persistent backup of WAN Gateways. Multiple GemFire components may share the same DiskStore. Additionally, multiple file system directories may be defined for a single DiskStore.

Please refer to Pivotal GemFire’s documentation for a complete explanation of the configuration options.

5.8. Configuring the Snapshot Service

Spring Data GemFire supports Cache and Region snapshots using Pivotal GemFire’s Snapshot Service. The out-of-the-box Snapshot Service support offers several convenient features to simplify the use of GemFire’s Cache and Region Snapshot Service APIs.

As the Pivotal GemFire documentation describes, snapshots allow you to save and subsequently reload the cached data later, which can be useful for moving data between environments, such as from production to a staging or test environment in order to reproduce data-related issues in a controlled context. You can imagine combining Spring Data GemFire’s Snapshot Service support with Spring’s bean definition profiles to load snapshot data specific to the environment as necessary.

Spring Data GemFire’s support for Pivotal GemFire’s Snapshot Service begins with the <gfe-data:snapshot-service> element from the <gfe-data> namespace.

For example, I might want to define Cache-wide snapshots to be loaded as well as saved using a couple snapshot imports and a data export definition as follows:

<gfe-data:snapshot-service id="gemfireCacheSnapshotService">
  <gfe-data:snapshot-import location="/absolute/filesystem/path/to/import/fileOne.snapshot"/>
  <gfe-data:snapshot-import location="relative/filesystem/path/to/import/fileTwo.snapshot"/>

You can define as many imports and/or exports as you like. You can define just imports or just exports. The file locations and directory paths can be absolute, or relative to the Spring Data GemFire application, JVM process’s working directory.

This is a pretty simple example and the Snapshot Service defined in this case refers to the GemFire Cache with the default name of gemfireCache (as described in Configuring a Cache). If you name your cache bean definition something other than the default, than you can use the cache-ref attribute to refer to the cache bean by name:

<gfe:cache id="myCache"/>
<gfe-data:snapshot-service id="mySnapshotService" cache-ref="myCache">

It is also straightforward to define a Snapshot Service for a particular GemFire Region by specifying the region-ref attribute:

<gfe:partitioned-region id="Example" persistent="false" .../>
<gfe-data:snapshot-service id="gemfireCacheRegionSnapshotService" region-ref="Example">
  <gfe-data:snapshot-import location="relative/path/to/import/example.snapshot/>
  <gfe-data:snapshot-export location="/absolute/path/to/export/example.snapshot/>

When the region-ref attribute is specified, Spring Data GemFire’s SnapshotServiceFactoryBean resolves the region-ref attribute value to a Region bean defined in the Spring context and proceeds to create a RegionSnapshotService. The snapshot import and export definitions function the same way, however, the location must refer to a file on export.

GemFire is strict about imported snapshot files actually existing before they are referenced. For exports, GemFire will create the snapshot file if it does not already exist. If the snapshot file for export already exists, the data will be overwritten.
Spring Data GemFire includes a suppress-import-on-init attribute on the <gfe-data:snapshot-service> element to suppress the configured Snapshot Service from trying to import data into the Cache or Region on initialization. This is useful when data exported from 1 Region is used to feed the import of another Region, for example.

5.8.1. Snapshot Location

For a Cache-based Snapshot Service (i.e. CacheSnapshotService) a developer would typically pass it a directory containing all the snapshot files to load rather than individual snapshot files, as the overloaded load method in the CacheSnapshotService API indicates.

Of course, a developer may use the other, overloaded load(:File[], :SnapshotFormat, :SnapshotOptions) method variant to get specific about which snapshot files are to be loaded into the GemFire Cache.

However, Spring Data GemFire recognizes that a typical developer workflow might be to extract and export data from one environment into several snapshot files, zip all of them up, and then conveniently move the ZIP file to another environment for import.

Therefore, Spring Data GemFire enables the developer to specify a JAR or ZIP file on import for a Cache-based Snapshot Service as follows:

  <gfe-data:snapshot-service id="cacheBasedSnapshotService" cache-ref="gemfireCache">
    <gfe-data:snapshot-import location="/path/to/snapshots.zip"/>

Spring Data GemFire will conveniently extract the provided ZIP file and treat it like a directory import (load).

5.8.2. Snapshot Filters

The real power of defining multiple snapshot imports and exports is realized through the use of snapshot filters. Snapshot filters implement Pivotal GemFire’s SnapshotFilter interface and are used to filter Region entries for inclusion into the Region on import and for inclusion into the snapshot on export.

Spring Data GemFire makes it brain dead simple to utilize snapshot filters on import and export using the filter-ref attribute or an anonymous, nested bean definition:


<gfe:partitioned-region id="Admins" persistent="false"/>
<gfe:partitioned-region id="Guests" persistent="false"/>

<bean id="activeUsersFilter" class="example.gemfire.snapshot.filter.ActiveUsersFilter/>

<gfe-data:snapshot-service id="adminsSnapshotService" region-ref="Admins">
  <gfe-data:snapshot-import location="/path/to/import/users.snapshot">
    <bean class="example.gemfire.snapshot.filter.AdminsFilter/>
  <gfe-data:snapshot-export location="/path/to/export/active/admins.snapshot" filter-ref="activeUsersFilter"/>

<gfe-data:snapshot-service id="guestsSnapshotService" region-ref="Guests">
  <gfe-data:snapshot-import location="/path/to/import/users.snapshot">
    <bean class="example.gemfire.snapshot.filter.GuestsFilter/>
  <gfe-data:snapshot-export location="/path/to/export/active/guests.snapshot" filter-ref="activeUsersFilter"/>

In addition, more complex snapshot filters can be expressed with the ComposableSnapshotFilter Spring Data GemFire provided class. This class implements GemFire’s SnapshotFilter interface as well as the Composite software design pattern.

In a nutshell, the Composite software design pattern allows developers to compose multiple objects of the same type and treat the aggregate as single instance of the object type, a very powerful and useful abstraction.

ComposableSnapshotFilter has two factory methods, 'and' and 'or', allowing developers to logically combine individual snapshot filters using the AND and OR logical operators, respectively. The factory methods take a list of SnapshotFilters.

In this case, the developer is only limited by his/her imagination to leverage this powerful construct.

For instance:

<bean id="activeUsersSinceFilter" class="org.springframework.data.gemfire.snapshot.filter.ComposableSnapshotFilter"
  <constructor-arg index="0">
      <bean class="org.example.app.gemfire.snapshot.filter.ActiveUsersFilter"/>
      <bean class="org.example.app.gemfire.snapshot.filter.UsersSinceFilter"

The developer could then go onto combine the activesUsersSinceFilter with another filter using 'or' like so:

<bean id="covertOrActiveUsersSinceFilter" class="org.springframework.data.gemfire.snapshot.filter.ComposableSnapshotFilter"
  <constructor-arg index="0">
      <ref bean="activeUsersSinceFilter"/>
      <bean class="example.gemfire.snapshot.filter.CovertUsersFilter"/>

5.8.3. Snapshot Events

By default, Spring Data GemFire uses Pivotal GemFire’s Snapshot Services on startup to import data and shutdown to export data. However, you may want to trigger periodic, event-based snapshots, for either import or export from within your Spring application.

For this purpose, Spring Data GemFire defines two additional Spring application events, extending Spring’s ApplicationEvent class for imports and exports, respectively: ImportSnapshotApplicationEvent and ExportSnapshotApplicationEvent.

The two application events can be targeted at the entire GemFire Cache, or individual GemFire Regions. The constructors in these classes accept an optional Region pathname (e.g. "/Example") as well as 0 or more SnapshotMetadata instances.

The array of SnapshotMetadata is used to override the snapshot meta-data defined by <gfe-data:snapshot-import> and <gfe-data:snapshot-export> sub-elements in XML, which will be used in cases where snapshot application events do not explicitly provide SnapshotMetadata. Each individual SnapshotMetadata instance can define it’s own location and filters properties.

Import/export snapshot application events are received by all snapshot service beans defined in the Spring ApplicationContext. However, import/export events are only processed by "matching" Snapshot Service beans.

A Region-based [Import|Export]SnapshotApplicationEvent matches if the Snapshot Service bean defined is a RegionSnapshotService and it’s Region reference (as determined by the region-ref attribute) matches the Region’s pathname specified by the snapshot application event.

A Cache-based [Import|Export]SnapshotApplicationEvent (i.e. a snapshot application event without a Region pathname) triggers all Snapshot Service beans, including any RegionSnapshotService beans, to perform either an import or export, respectively.

It is very easy to use Spring’s ApplicationEventPublisher interface to fire import and/or export snapshot application events from your application like so:

public class ExampleApplicationComponent {

  private ApplicationEventPublisher eventPublisher;

  @Resource(name = "Example")
  private Region<?, ?> example;

  public void someMethod() {

    SnapshotFilter myFilter = ...;

    SnapshotMetadata exportSnapshotMetadata = new SnapshotMetadata(new File(System.getProperty("user.dir"),
      "/path/to/export/data.snapshot"), myFilter, null);

    eventPublisher.publishEvent(new ExportSnapshotApplicationEvent(this, example.getFullPath(), exportSnapshotMetadata);


In this particular example, only the "/Example" Region’s Snapshot Service bean will pick up and handle the export event, saving the filtered, "/Example" Region’s data to the "data.snapshot" file in a sub-direcrtory of the application’s working directory.

Using Spring application events and messaging subsystem is a good way to keep your application loosely coupled. It is also not difficult to imagine that the snapshot application events could be fired on a periodic basis using Spring’s Scheduling services.

5.9. Configuring the Function Service

Spring Data GemFire provides annotation support for implementing and registering Pivotal GemFire Functions.

Spring Data GemFire also provides namespace support for registering Pivotal GemFire Functions for remote Function execution.

Please refer to Pivotal GemFire' documentation for more information on the Function execution framework.

GemFire Functions are declared as Spring beans and must implement the org.apache.geode.cache.execute.Function interface or extend org.apache.geode.cache.execute.FunctionAdapter.

The namespace uses a familiar pattern to declare functions:

      <bean class="example.FunctionOne"/>
      <ref bean="function2"/>

<bean id="function2" class="example.FunctionTwo"/>

5.10. Configuring WAN Gateways

WAN Gateways provide a way to synchronize Pivotal GemFire Distributed Systems across geographic areas. Spring Data GemFire provides namespace support for configuring WAN Gateways as illustrated in the following examples.

5.10.1. WAN Configuration in GemFire 7.0

In the example below, GatewaySenders are configured for a PARTITION Region by adding child elements to the Region (gateway-sender and gateway-sender-ref).

A GatewaySender may register EventFilters and TransportFilters. Also shown below is an example configuration of an AsyncEventQueue which must also be wired into a Region (not shown).

<gfe:partitioned-region id="region-with-inner-gateway-sender" >
    <gfe:gateway-sender remote-distributed-system-id="1">
	        <bean class="org.springframework.data.gemfire.example.SomeEventFilter"/>
	        <bean class="org.springframework.data.gemfire.example.SomeTransportFilter"/>
    <gfe:gateway-sender-ref bean="gateway-sender"/>

<gfe:async-event-queue id="async-event-queue" batch-size="10" persistent="true" disk-store-ref="diskstore"
        <bean class="example.AsyncEventListener"/>

<gfe:gateway-sender id="gateway-sender" remote-distributed-system-id="2">
        <ref bean="event-filter"/>
        <bean class="org.springframework.data.gemfire.example.SomeEventFilter"/>
        <ref bean="transport-filter"/>
        <bean class="org.springframework.data.gemfire.example.SomeTransportFilter"/>

<bean id="event-filter" class="org.springframework.data.gemfire.example.AnotherEventFilter"/>
<bean id="transport-filter" class="org.springframework.data.gemfire.example.AnotherTransportFilter"/>

On the other end of a GatewaySender is a corresponding GatewayReceiver to receive Gateway events. The GatewayReceiver may also be configured with EventFilters and TransportFilters.

<gfe:gateway-receiver id="gateway-receiver" start-port="12345" end-port="23456" bind-address="">
        <bean class="org.springframework.data.gemfire.example.SomeTransportFilter"/>

Please refer to the Pivotal GemFire documentation for a detailed explanation of all the configuration options.

6. Bootstrapping Pivotal GemFire using Spring Annotations

Spring Data GemFire (SDG) 2.0 introduces a new Annotation-based configuration model to bootstrap Pivotal GemFire with the Spring container.

The primary motivation for introducing an Annotation-based approach to the configuration of Pivotal GemFire in a Spring context is to enable application developers to get up and running as quickly and as easily as possible.

6.1. Introduction

Pivotal GemFire can be very difficult to setup and use successfully given all the configuration properties, configuration options (cache.xml, Gfsh + Cluster Configuration, Spring XML/Java-based configuration) along with different supported topologies (client/server, P2P, WAN) and Distributed System Design Patterns (e.g. shared-nothing architecture). The Annotation-based configuration model aims to simplify all this plus more.

The Annotation-based configuration model is an alternative to XML-based configuration using Spring Data GemFire’s XML Namespace. With XML, an application developer would use the spring-gemfire (gfe) schema for configuration and the spring-data-gemfire (gfe-data) schema for data access related concerns. See Bootstrapping Pivotal GemFire with the Spring Container for more details.

As of SDG 2.0, the new Annotation-based configuration model does not yet have configuration support for Pivotal GemFire’s WAN components and topology.

Like Spring Boot, Spring Data GemFire’s Annotation-based configuration model was designed as an opinionated, convention over configuration approach for using Pivotal GemFire. Indeed, the Annotation-based configuration model was inspired by Spring Boot as well as several other Spring and Spring Data projects.

By following convention, all Annotations provide reasonable and sensible defaults for all the attributes out-of-the-box. The default value for a given Annotation attribute directly corresponds to the default value provided in Pivotal GemFire for the same configuration property or setting.

The intention is to let an application developer enable an Pivotal GemFire feature or an embedded service by simply declaring the Annotation on his/her Spring @Configuration or @SpringBootApplcation class without needing to unnecessarily configure a large number of attributes or properties just to use the feature.

Again, getting up and running as quickly and as easily as possible is the primary objective.

However, the option to customize the configuration meta-data and behavior of Pivotal GemFire is there should an application developer need it and Spring Data GemFire’s Annotation-based configuration will quietly back away. The application developer simply just needs to specify the configuration attributes s/he wishes to adjust. And, as we will see below, there are several ways to configure an Pivotal GemFire feature or embedded service using Annotations.

All the new SDG Annotations can be found in the org.springframework.data.gemfire.config.annotation package.

6.2. Bootstrapping Pivotal GemFire applications with Spring

Like all Spring Boot applications that begin by annotating the application class with @SpringBootApplication, a Spring Boot application can easily become an Pivotal GemFire cache application simply by declaring 1 of 3 main Annotations:

  1. @ClientCacheApplication

  2. @PeerCacheApplication

  3. @CacheServerApplication

These 3 Annotations are the Spring/Pivotal GemFire application developer’s starting point.

To realize the intent behind these Annotations, a user must understand that there are 2 types of cache instances that can be created with Pivotal GemFire: a client or a peer.

A Spring Boot application can be configured as an Pivotal GemFire cache client (i.e. with an instance of ClientCache), which communicates with an existing, standalone cluster of Pivotal GemFire servers used to manage the application’s data. The client/server topology is the most typical system architecture employed when using Pivotal GemFire and the user can make her Spring Boot application a cache client simply by annotating it with @ClientCacheApplication.

Alternatively, a Spring Boot application may be a peer member of an Pivotal GemFire cluster. That is, the application itself is just another server in the cluster of servers that will manage data. The application creates an "embedded" peer Cache instance when a developer annotates his/her application class with @PeerCacheApplication.

By extension, the application may also serve as a CacheServer for cache clients, allowing clients to connect and perform data access operations on the server. This is accomplished by annotating the application class with @CacheServerApplication in place of @PeerCacheApplication, which will create a peer Cache instance along with the CacheServer.

An Pivotal GemFire Server is not necessarily a "Cache Server" by default. That is, it is not necessarily setup to service cache clients just because it is a "server". A GemFire Server can just be a peer member/data node of the cluster that manages data without servicing any clients while other peer members in the cluster are setup to service clients in addition to managing data. It also possible to setup certain peer members as non-data node, data accessors that can service clients as CacheServers as well, but is well beyond the scope of this document.

By way of example, if I wanted to create a Spring Boot, Pivotal GemFire cache client application, I would start with…​

Spring-based Pivotal GemFire ClientCache application
class ClientApplication { .. }

And, if I wanted to create a Spring Boot application with an embedded peer Cache instance, where my application will be a server and peer member of a cluster, or distributed system formed by Pivotal GemFire, then I would start with…​

Spring-based Pivotal GemFire embedded peer Cache application
class ServerApplication { .. }

Alternatively, a user may use the @CacheServerApplication annotation in place of @PeerCacheApplication, which will create both an "embedded" peer Cache instance along with a CacheServer running on "localhost", listening on the default cache server port, 40404…​

Spring-based Pivotal GemFire embedded CacheServer Application
class ServerApplication { .. }

6.3. Going in-detail on client/server applications

There are multiple ways that a client can connect to and communicate with servers in a GemFire cluster. The most common and recommended approach is to use Pivotal GemFire Locators.

A cache client can connect to 1 or more Locators in the GemFire cluster instead of directly to a CacheServer. The advantage of using Locators over direct CacheServer connections is that Locators provide meta-data about the cluster to which clients are connected. This meta-data includes information like which servers have the least amount of load, or which servers contain the data of interests to the client. A Locator also provides fail-over capabilities in case a CacheServer goes down. By enabling the PR single-hop capability in the client Pool, the client is routed directly to the server containing the data the client needs access to.
Locators are also peer members in a cluster. Locators actually constitute what makes up a cluster of GemFire nodes; i.e. all nodes connected by a Locator make up a cluster of peers and new members use Locators to join a cluster and find other members.

Since Pivotal GemFire sets up a "DEFAULT" Pool connected to a CacheServer running on "localhost", listening on port 40404 by default when a ClientCache instance is created, there is nothing special a user need do to utilize the client/server topology. Simply annotate your server-side Spring Boot application with @CacheServerApplication and your client-side Spring Boot application with @ClientCacheApplication and you are all set.

You can even start your servers using Gfsh’s start server command if you prefer. Your Spring Boot @ClientCacheApplication will still connect to the server regardless of how it is started. Although, we think you will prefer to configure and start your servers using the Spring Data GemFire approach, with Annotations.

However, as an application developer, you will no doubt want to customize the "DEFAULT" Pool setup by Pivotal GemFire to possibly connect to 1 or more Locators, for instance…​

Spring-based Pivotal GemFire ClientCache application using Locators
@ClientCacheApplication(locators = {
    @Locator(host = "boombox" port = 11235),
    @Locator(host = "skullbox", port = 12480)
class ClientApplication { .. }

Along with the locators attribute, the @ClientCacheApplication annotation has a servers attribute that can be used to specify 1 or more nested @Server annotations that enable the cache client to connect directly to 1 or more servers.

You can only use either the locators or servers attribute, but not both, which is enforced by Pivotal GemFire.

A user may also configure additional Pools, other than the "DEFAULT" Pool provided by Pivotal GemFire when a ClientCache instance is created with the @ClientCacheApplication annotation, by using the @EnablePool and @EnablePools annotations.

@EnablePools is a composite annotation that aggregates several nested @EnablePool annotations on a single class. Java 8 and earlier does not allow more than 1 annotation of the same type to be declared on a class.
Spring-based Pivotal GemFire ClientCache application using multiple named Pools
@ClientCacheApplication(logLevel = "info")
@EnablePool(name = "VenusPool", servers = @Server(host = "venus", port = 48484),
    min-connections = 50, max-connections = 200, ping-internal = 15000,
    prSingleHopEnabled = true, readTimeout = 20000, retryAttempts = 1,
    subscription-enable = true)
@EnablePools(pools = {
    @EnablePool(name = "SaturnPool", locators = @Locator(host="skullbox", port=20668),
        subsription-enabled = true),
    @EnablePool(name = "NeptunePool", severs = {
            @Server(host = "saturn", port = 41414),
            @Server(host = "neptune", port = 42424)
        }, min-connections = 25))
class ClientApplication { .. }

The name attribute is the only required attribute of the @EnablePool annotation. As we will see below, the value of name corresponds to both the name of the Pool bean created in the Spring context as well as the name used to reference the corresponding configuration properties. It is also the name of the Pool registered and used in Pivotal GemFire.

Similarly, on the server, a user can configure multiple CacheServers that a client can connect to…​

Spring-based Pivotal GemFire CacheServer application using multiple named CacheServers
@CacheSeverApplication(logLevel = "info", autoStartup = true, maxConnections = 100)
@EnableCacheServer(name = "Venus", autoStartup = true,
    hostnameForClients = "venus", port = 48484)
@EnableCacheServers(servers = {
    @EnableCacheServer(name = "Saturn", hostnameForClients = "saturn", port = 41414),
    @EnableCacheServer(name = "Neptune", hostnameForClients = "neptune", port = 42424)
class ServerApplication { .. }
Like @EnablePools, @EnableCacheServers is a composite annotation for aggregating multiple @EnableCacheServer annotations on a single class.

One thing an observant reader may have noticed is, in all cases, the user is specifying hard-coded values for hostnames, ports as well other configuration-oriented Annotation attributes. This is not ideal when a user’s application gets promoted and deployed to different environments, such as from DEV to QA to STAGING to PROD.

How does an application developer handle dynamic configuration determined at runtime?

6.4. Runtime configuration using Configurers

Another goal when designing the Annotation-based configuration model was to preserve Type-Safety in the Annotation attributes. For example, if an attribute could be expressed as an int, like a port number, then the attribute’s type should be an int.

Unfortunately, this is not conducive to dynamic and resolvable configuration at runtime.

One of the finer features of Spring is the ability to use property placeholders or SpEL expressions in properties or attributes of the configuration meta-data when configuring beans in a Spring context. Although, this would require all Annotation attributes be Strings thereby giving up Type-Safety; not acceptable!

So, Spring Data GemFire borrows from another commonly used pattern in Spring, Configurers. Many different Configurer interfaces are provided out-of-the-box in Spring Web MVC, such as the org.springframework.web.servlet.config.annotation.ContentNegotiationConfigurer.

Configurers are a way to allow application developers to receive a callback and customize the configuration of a component on startup. The framework calls back to user-provided code to adjust the configuration at runtime. One of the more common uses of this pattern is to supply conditional configuration based on the application’s runtime environment.

Spring Data GemFire provides several Configurer callback interfaces to customize different aspects of Annotation-based configuration meta-data at runtime, before the Sring managed beans that the Annotations create are initialized:

  • ClientCacheConfigurer

  • PeerCacheConfigurer

  • CacheServerConfigurer

  • ContinuousQueryListenerContainerConfigurer

  • DiskStoreConfigurer

  • IndexConfigurer

  • PoolConfigurer

  • RegionConfigurer

For example, we can use the CacheServerConfigurer and ClientCacheConfigurer to customize the port numbers used by our CacheServer and ClientCache applications, respectively.

First, in our server application…​

Customizing a Spring Boot CacheServer application with a CacheServerConfigurer
@CacheServerApplication(name = "SpringApplication", logLevel = "info")
class ServerApplication {

  CacheServerConfigurer cacheServerPortConfigurer(
          @Value("${gemfire.cache.server.host:localhost}") String cacheServerHost
          @Value("${gemfire.cache.server.port:40404}") int cacheServerPort) {

      return (beanName, cacheServerFactoryBean) -> {

Then, in our client application…​

Customizing a Spring Boot ClientCache application with a ClientCacheConfigurer
@ClientCacheApplication(logLevel = "info")
class ClientApplication {

  ClientCacheConfigurer clientCachePoolPortConfigurer(
          @Value("${gemfire.cache.server.host:localhost}") String cacheServerHost
          @Value("${gemfire.cache.server.port:40404}") int cacheServerPort) {

      return (beanName, clientCacheFactoryBean) ->
              new ConnectionEndpoint(cacheServerHost, cacheServerPort)));

By using the provided Configurers, a user is able to receive a callback in order to further customize the configuration that is enabled by the associated Annotation.

In addition, when the Configurer is declared as a bean in the Spring context, the bean definition can take advantage of other Spring container features, such as property placeholders, or SpEL expressions in @Value annotations on factory method parameters, and so on.

All Spring Data GemFire-provided Configurers take 2 bits of information in the callback: the name of the bean created in the Spring context by the Annotation along with a reference to the FactoryBean used by the Annotation to configure the GemFire component (e.g. a ClientCache instance with SDG’s ClientCacheFactoryBean).

SDG FactoryBeans are part of the SDG public API and are what an application developer would use in Spring’s Java-based container configuration if this new Annotation-based configuration model were not provided. Indeed, the Annotations themselves are using these very same FactoryBeans for their configuration.

Given a Configurer can be declared as a regular bean definition like any other, it is not difficult to imagine a user combining different Spring configuration options, such as the use of Spring Profiles with Conditions as well as other features to create even more sophisticated and flexible configuration.

However, Configurers are not the only option.

6.5. Runtime configuration using Properties

In addition to Configurers, each Annotation attribute in the Annotation-based configuration model is associated with a corresponding configuration property, prefixed with spring.data.gemfire., that can be declared in Spring Boot application.properties.

Building on our examples above, the client’s application.properties would define…​

Client application.properties

And, the server’s application.properties would define…​

Server application.properties

Then, we can simplify the @ClientCacheApplication class to…​

Spring @ClientCacheApplication class
@EnablePools(pools = {
    @EnablePool(name = "VenusPool"),
    @EnablePool(name = "SaturnPool"),
    @EnablePool(name = "NeptunePool")
class ClientApplication { .. }

And, the @CacheServerApplication class as…​

Spring @CacheServerApplication class
@CacheServerApplication(name = "SpringApplication")
@EnableCacheServers(servers = {
    @EnableCacheServer(name = "Venus"),
    @EnableCacheServer(name = "Saturn"),
    @EnableCacheServer(name = "Neptune")
class ServerApplication { .. }

The example above illustrates why it is import to "name" your Annotation-based beans (other than it is required in certain cases). Doing so makes it possible to reference the bean in a Spring context from XML, properties and even Java. It is even possible to inject Annotation-defined beans into an application class, for whatever purpose; for example…​

class MyApplicationComponent {

  @Resource(name = "Saturn")
  CacheServer saturnCacheServer;


Likewise, naming a Annotated-defined bean allows you to code a Configurer to customize a specific, "named" bean since the beanName is 1 of 2 arguments passed to the callback.

Often times, an associated Annotation attribute property takes 2 forms: a "named" property along with an "unnamed" property.

For example…​


While there are 3 named CacheServers above, there is 1 unnamed CacheServer property that serves as the default value for any unspecified value for that property even for "named" CacheServers. So, while "Venus" sets and overrides its own bind-address, "Saturn" and "Neptune" inherit from the unnamed spring.data.gemfire.cache.server.bind-address property.

Refer to an Annotation’s Javadoc for which Annotation attributes support property-based configuration, and whether they support "named" properties over just "default", unnamed properties.

6.5.1. Properties of Properties

Of course, in Spring fashion, you can even express Properties in terms of other Properties, whether that is using a Spring Boot application.properties file or by using the @Value annotation in your Java class…​

Properties of Properties

Or, in Java…​

  CacheServerConfigurer cacheServerPortConfigurer(
          @Value("${gemfire.cache.server.port:${some.other.property:40404}}") int cacheServerPort) {


Property placeholder nesting can be arbitrarily deep.

6.6. Configuring embedded services

Pivotal GemFire provides the ability to start many different embedded services required by an application depending on the use case.

6.6.1. Configuring an embedded Locator

As mentioned previously, Pivotal GemFire Locators are used by clients to connect with and find servers in a cluster as well as by new members joining an existing cluster to find other peers.

It is often convenient for application developers as they are developing their Spring Boot, Spring Data GemFire applications to startup up a small cluster of 2 or 3 Pivotal GemFire servers. Rather than starting a separate Locator process, a user can simply annotate her @CacheServerApplication class with @EnableLocator.

Spring, Pivotal GemFire CacheServer application running an embedded Locator
class ServerApplication { .. }

The @EnableLocator annotation starts and embedded Locator in the Spring, Pivotal GemFire CacheServer application process running on "localhost", listening on the default Locator port 10334. It is possible to customize the host (a.k.a bind address) and port that the embedded Locator binds to using the corresponding Annotation attributes.

Then, it is possible to start other Spring Boot, @CacheServerApplication enabled applications connecting to this Locator with…​

Spring, Pivotal GemFire CacheServer application connecting to a Locator
@CacheServerApplication(locators = "localhost[10334]")
class ServerApplication { .. }

You may even combine both application classes shown above into a single class and use your IDE feature to create different run profile configurations to create and run different instances of the same class with slightly modified configuration using Java System Properties…​

Spring CacheServer application running an embedded Locator and connecting to the Locator
@CacheServerApplication(locators = "localhost[10334]")
public class ServerApplication {

  public static void main(String[] args) {

  static class Configuration {

Then, for each run profile, a user simply sets and changes the following System properties…​

IDE run profile configuration

Only 1 of the run profiles for the ServerApplication class should be set with the -Dspring.profiles.active=embedded-locator Java System Property. Then, simply change the name and cache.server.port for each of the other run profiles and you’ll have yourself a small cluster/distributed system of GemFire Servers.

The @EnableLocator annotation was meant to be a development-time annotation only and not something an application developer should use in production. It is recommended that Locators be stand-alone, independent processes in the cluster.

More details on how Pivotal GemFire Locators work can be found here.

6.6.2. Configuring an embedded Manager

An Pivotal GemFire Manager is another peer member/node in the cluster that is responsible for "management" activities. Management activities include things like creating Regions, Indexes, DiskStores, etc. The Manager allows a JMX-enabled client (e.g. Gfsh shell tool) to connect to the manager to manage the cluster. It is also possible to connect to a Manager with JDK provided tools like JConsole or JVisualVM, given these are both JMX-enabled clients as well.

Perhaps we would also like to make our Spring @CacheServerApplication shown above a Manager as well. Simply annotate your Spring @Configurtion or @SpringBootApplication class with @EnableManager and you are done. By default, the Manager binds to "localhost" listening on the default Pivotal GemFire Manager port 1099. Several aspects of the Manager can be configured with the Annotation attributes or corresponding properties.

Spring CacheServer application running an embedded Manager
@CacheServerApplication(locators = "localhost[10334]")
public class ServerApplication {

  public static void main(String[] args) {

  static class Configuration {

With the above class, you can even use Gfsh to connect to this server and manage it.

$ gfsh
    _________________________     __
   / _____/ ______/ ______/ /____/ /
  / /  __/ /___  /_____  / _____  /
 / /__/ / ____/  _____/ / /    / /
/______/_/      /______/_/    /_/    1.2.1

Monitor and Manage Pivotal GemFire

Connecting to Locator at [host=localhost, port=10334] ..
Connecting to Manager at [host=, port=1099] ..
Successfully connected to: [host=, port=1099]

gfsh>list members
         Name          | Id
---------------------- | ----------------------------------------------------
SpringCacheServerOne   |<ec><v0>:1024
SpringCacheServerTwo   |<v1>:1025
SpringCacheServerThree |<v2>:1026

Because we also have the embedded Locator enabled, we were able to connect indirectly to the Manager through the Locator. The Locator allows JMX clients to connect and find a Manager node in the cluster. If none exist, the Locator will assume the role of the Manager. However, if no Locator existed, then we would need to connect directly to the Manager using…​

Gfsh connect command connecting directly to the Manager
gfsh>connect --jmx-manager=localhost[1099]
Like the @EnableLocator annotation, the @EnableManager annotation was also meant to be a development-time only and not something an application developer should use in production. It is recommended that Managers, like Locators, be stand-alone, independent processes in the cluster.

More details on Pivotal GemFire Management and Monitoring can be found here.

6.6.3. Configuring the embedded HTTP Server

Pivotal GemFire is also capable of running an embedded HTTP server. The current implementation is backed by Eclipse Jetty.

The embedded HTTP server is used to host Pivotal GemFire’s Management (Admin) REST API (not a publicly advertised API), the Developer REST API and the Pulse Monitoring Web Application.

However, to use any of these Pivotal GemFire provided Web applications, you must have a full installation of Pivotal GemFire installed on your system, and you must set the GEMFIRE environment variable to your installation directory.

To enable the embedded HTTP server, simply add the @EnableHttpService annotation to any @PeerCacheApplication or @CacheServerApplication annotated class…​

Spring CacheServer application running an embedded HTTP server
public class ServerApplication { .. }

By default, the embedded HTTP server listens on port 7070 for HTTP client requests. Of course, you can use the Annotation attributes or corresponding configuration properties to adjust the configuration as needed.

Follow the links above for more details on HTTP support.

6.6.4. Configuring the embedded Memcached Server (Gemcached)

Pivotal GemFire also implements the Memcached protocol with the ability to service Memcached clients. That is Memcached clients can connect to an Pivotal GemFire cluster and perform Memcached operations as if the Pivotal GemFire Servers in the cluster were actual Memcached Servers.

To enable the embedded Memcached Service, simply add the @EnableMemcachedServer annotation to any @PeerCacheApplication or @CacheServerApplication annotated class…​

Spring CacheServer application running an embedded Memcached Server
public class ServerApplication { .. }

More details on Pivotal GemFire’s Gemcached service can be found here.

6.6.5. Configuring the embedded Redis Server

Pivotal GemFire also implements the Redis Server protocol, which enables Redis clients to communicate with a cluster of Pivotal GemFire Servers and issue Redis commands. As of this writing, the Redis Server protocol support in Pivotal GemFire is still experimental.

To enable the embedded Redis Service, simply add the @EnableRedisServer annotation to any @PeerCacheApplication or @CacheServerApplication annotated class…​

Spring CacheServer application running an embedded Redis Server
public class ServerApplication { .. }

More details on Pivotal GemFire’s Redis Adapter can be found here.

6.7. Configuring Logging

Often times it is necessary to turn up logging in order to understand exactly what Pivotal GemFire is doing and when.

To enable Logging, simply annotate your application class with @EnableLogging and set the appropriate attributes or associated properties…​

Spring ClientCache application with Logging enabled
@EnableLogging(logLevel="info", logFile="/absolute/file/system/path/to/application.log)
public class ClientApplication { .. }

While the logLevel attribute can be specified with all the cache-based application annotations (e.g. @ClientCacheApplication(logLevel="info")), it is easier to customize logging behavior with the @EnableLogging annotation.

See the @EnableLogging annotation Javadoc for more details.

6.8. Configuring Statistics

To gain even deeper insight into Pivotal GemFire during runtime, an application developer can enable Statistics. Gathering statistical data facilitates system analysis and troubleshooting when complex problems occur, which are often distributed in nature and timing is a factor.

When Statistics are enabled, a user can use Pivotal GemFire’s VSD (Visual Statistics Display) tool to analyze the statistical data that is collected.

To enable Statistics, simply annotate your application class with @EnableStatistics…​

Spring ClientCache application with Statistics enabled
public class ClientApplication { .. }

Enabling Statistics on a server is particularly valuable when evaluating performance, which is as simple as annotating your @PeerCacheApplication or @CacheServerApplication class with @EnableStatistics.

Use the @EnableStatistics annotation attributes or associated properties to customize the Statistics gathering and collection process.

See the @EnableStatistics annotation Javadoc for more details.

More details on Pivotal GemFire’s Statistics can be found here.

6.9. Configuring PDX

One of the more powerful features of Pivotal GemFire is PDX Serialization. While a complete discussion on PDX is well beyond the scope of this document, serialization using PDX is a much better alternative to Java Serialization, with the following benefits…​

  1. PDX uses a centralized Type Registry to keep the serialized bytes of an object more compact.

  2. PDX is a neutral serialization format allowing both Java and Native Clients to operate on the same data set.

  3. PDX supports versioning and allows object fields to be added or removed with affecting applications using either older or newer versions of the PDX serialized, application domain object types that change, without data loss.

  4. PDX allows object fields to be accessed individually or in OQL query projections and predicates without the object needing to be de-serialized first.

In general, serialization in Pivotal GemFire is needed anytime data is transferred to/from clients and servers or between peers in a cluster for normal distribution and replication processes as well as when data is overflowed or persisted to disk.

To enable PDX, simply annotate your application class with @EnablePdx…​

Spring ClientCache application with PDX enabled
public class ClientApplication { .. }

Typically, an application’s domain object types will either implement the org.apache.geode.pdx.PdxSerializable interface, or an application developer will choose to implement and register the non-invasive org.apache.geode.pdx.PdxSerializer interface to handle the application domain object types that need to be serialized.

Unfortunately, Pivotal GemFire only allows one PdxSerializer to be registered, which suggests that all application domain object types should be handled by a "single" PdxSerializer instance. But, that is a serious anti-pattern and foolish practice to be sure.

Even though only a single PdxSerializer instance can be registered with Pivotal GemFire, it makes sense to create a PdxSerializer implementation per application domain object type.

By using the Composite Software Design Pattern, the application developer can provide an implementation of the PdxSerializer interface that aggregates of all application domain object type-specific PdxSerializer instances but acts as a single PdxSerializer instance, and register it.

You can declare this Composite PdxSerializer as a managed bean in the Spring context and refer to this Composite PdxSerializer by bean name in the @EnablePdx annotation using the serializerBeanName attribute. Spring Data GemFire will take care of registering it with Pivotal GemFire on the user’s behalf.

Spring ClientCache application with PDX enabled, using a custom, composite PdxSerializer
@EnablePdx(serializerBeanName = "compositePdxSerializer")
public class ClientApplication {

  PdxSerializer compositePdxSerializer() {
      return new CompositePdxSerializerBuilder()...

It is also possible to declare Pivotal GemFire’s org.apache.geode.pdx.ReflectionBasedAutoSerializer as a bean definition in a Spring context. Alternatively, you can use Spring Data GemFire’s more robust, org.springframework.data.gemfire.mapping.MappingPdxSerializer, which uses Spring Data mapping meta-data and infrastructure applied to the serialization process for more efficient handling than reflection alone.

Many other aspects and features of PDX can be adjusted with the @EnablePdx annotation attributes or associated configuration properties.

See the @EnablePdx annotation Javadoc for more details.

6.10. Configuring SSL

Equally important to serializing data to be transferred over-the-wire is securing the data while in transit. Of course, the common way to accomplish this in Java is using the Secure Sockets Extension (SSE) and Transport Layer Security (TLS).

To enable SSL, simply annotate your application class with @EnableSsl and set the necessary SSL configuration attributes (e.g. keystores, usernames/passwords, etc)…​

Spring ClientCache application with SSL enabled
public class ClientApplication { .. }

Different Pivotal GemFire components: GATEWAY, HTTP, JMX, LOCATOR, SERVER can be individually configured with SSL, or they can all be collectively configured all at once to use SSL using the CLUSTER enumerated value.

It is easy to specify which Pivotal GemFire components that the SSL configuration settings should applied to using the nested @EnableSsl annotation Component enum…​

Spring ClientCache application with SSL enabled by Aache GemFire component
@EnableSsl(components = { GATEWAY, LOCATOR, SERVER })
public class ClientApplication { .. }

In addition component-level SSL configuration, ciphers, protocols and keystore/truststore information can also be specified using the corresponding Annotation attribute or associated configuration properties.

See the @EnableSsl annotation Javadoc for more details.

More details on Pivotal GemFire SSL support can be found here.

6.11. Configuring GemFire Properties

While many of the gemfire.properties are conveniently encapsulated in and abstracted with an Annotation in the SDG Annotation-based configuration model, the less commonly used GemFire Properties are still accessible from the @EnableGemFireProperties annotation.

Using the @EnableGemFireProperties annotation on your application class is convenient and a nice alternative to creating a gemfire.properties file or setting GemFire Properties as Java System properties on the command-line when launching your application.

It is recommended that these GemFire Properties be set in a gemfire.properties file when deploying your application to production. But, at development-time, it can be convenient to set these properties individually, as needed, for prototyping and/or testing purposes.

A few examples of some of the less common GemFire Properties that a user usually need not worry about include, but are not limited to: ack-wait-threshold, disable-tcp, socket-buffer-size, etc.

To individually set any GemFire Property, simply annotate your application class with @EnableGemFireProperties and set the GemFire Properties you want to change from the default, out-of-the-box value set by Pivotal GemFire…​

Spring ClientCache application with specific GemFire Properties set
@EnableGemFireProperties(conflateEvents = true, socketBufferSize = 16384)
public class ClientApplication { .. }

Keep in mind, some of the GemFire Properties are client specific (e.g. conflateEvents) while others are server specific (e.g. distributedSystemId, enableNetworkPartitionDetection, enforceUniqueHost, memberTimeout, redundancyZone).

More details on Pivotal GemFire properties can be found here.

6.12. Configuring Regions

So far, outside of PDX, our discussion has centered around configuring Pivotal GemFire’s more administrative functions: creating a cache instance, starting embedded services, enabling Logging, Statistics and SSL, using gemfire.properties to affect very low-level configuration and behavior. While all these configuration options are important, none of them relate directly to the application. In other words, we still need some place to store our application data and make it generally available and accessible.

Pivotal GemFire organizes data in a cache into Regions. You can think of a Region as a table in a relational database. Generally, a Region should only store a single type of object making it more conducive for building effective Indexes. We will talk about Indexing later.

Previously, Spring Data GemFire users needed to explicitly define and declare the Regions used in their applications to store data by writing very verbose Spring configuration meta-data, whether a user was using SDG’s FactoryBeans from the API in Spring’s Java-based container configuration…​

Example Region bean definition using Spring Java-based container configuration
class GemFireConfiguration {

  PartitionedRegionFactoryBean exampleRegion(GemFireCache gemfireCache) {

      PartitionedRegionFactoryBean<Long, Example> exampleRegion =
          new PartitionedRegionFactoryBean<>();


      return exampleRegion;


Or, using XML…​

Example Region bean definition using the SDG XML Namespace
  <gfe:partitioned-region id="exampleRegion" name="Example" persistent="true">

While neither Java nor XML configuration is hard to do, it is cumbersome, especially if an application has a large number of Regions that need to be defined. Many relational database-based applications can literally have 100s or even 1000s of tables.


Now users can define and configure Regions based on their application domain objects (entities). No longer does a user need to explicitly define Region bean definitions in Spring configuration meta-data, unless finer-grained control is required.

To simplify Region creation, Spring Data GemFire combines the use of SD Repositories with the expressive power of Annotation-based configuration using the new @EnableEntityDefinedRegions annotation.

Most Spring Data application developers should already be familiar with the Spring Data Repository abstraction and Spring Data GemFire’s implementation/extension of the SD Repository abstraction, which has been specifically customized to optimize data access operations for Pivotal GemFire.

First, an application developer starts by defining the application domain objects…​

Application domain object type modeling a Book
class Book {

  private ISBN isbn;

  private Author author;

  private Category category;

  private LocalDate releaseDate;

  private Publisher publisher;

  private String title;


Next, the application developer would define a basic Repository for Books by extending Spring Data Commons org.springframework.data.repository.CrudRepository interface…​

Repository for Books
interface BookRepository extends CrudRepository<Book, ISBN> { .. }

The org.springframe.data.repository.CrudRepository is a Data Access Object (DAO) providing basic data access operations (CRUD) along with support for simple queries (e.g. findById(..)). The user can define additional, more sophisticated queries simply by declaring query methods on the Repository interface (e.g. List<BooK> findByAuthor(Author author);).

Under-the-hood, Spring Data GemFire provides an implementation of this interface when the Spring container is bootstrapped. SDG will even implement the query methods defined by the user so long as the user follows simple conventions.

Now, when a user defined the Book class, she also specified the Region in which instances of Book will be mapped and stored by declaring the Spring Data GemFire mapping annotation, @Region on the entity’s type. Of course, if the entity type (i.e. Book) referenced in the type parameter of the Repository interface (i.e. BookRepository) is not annotated with @Region, the name is derived from the simple class name of the entity type (i.e. "Book").

Spring Data GemFire uses the mapping context containing mapping meta-data for all the entities defined in your application to determine all the Regions that will be needed at runtime.

To enable and use this feature, simply annotate the application class with @EnableEntityDefinedRegions…​

Entity-defined Region Configuration
@EnableEntityDefinedRegions(basePackages = "example.app.domain")
class ClientApplication { .. }
Creating Regions from entity classes is the most useful when using Spring Data Repositories in your application. Spring Data GemFire’s Repository support is enabled with the @EnableGemfireRepositories annotation.

By default, the @EnableEntityDefinedRegions annotation will scan for entity classes recursively starting from the package of the configuration class on which the @EnableEntityDefinedRegions annotation is defined.

However, it is common to limit the search during the scan by setting the basePackages attribute with the package names containing your application entity classes.

Alternatively, a user can use the more type-safe basePackageClasses attribute for specifying the package to scan by setting the attribute to an entity type in the package containing the entity’s class, or by using a non-entity placeholder class in the package specifically created for identifying the package to scan. For example…​

Entity-defined Region Configuration using the Entity class type
@EnableEntityDefinedRegions(basePackageClasses = {
class ClientApplication { .. }

In addition to specifying the location where to begin the scan, like Spring’s @ComponentScan annotation, a user can specify include and exclude filters with all the same semantics of the org.springframework.context.annotation.ComponentScan.Filter annotation.

See the @EnableEntityDefinedRegion annotation Javadoc for more details.

6.12.1. Configuring Type-specific Regions

Pivotal GemFire supports many different types of Regions. Each type corresponds to the Region’s DataPolicy, which determines exactly how the data in the Region will be managed (e.g. distributed/replicated, etc).

Other configuration settings also can affect how data is managed like the Region’s scope. See Storage and Distribution Options in the Pivotal GemFire User Guide for more details.

When user annotate their application domain object types with the generic @Region mapping annotation, Spring Data GemFire will decide which type of Region to create. SDG’s default strategy takes the cache type into consideration when determining the type of Region to create.

For example, if the application was declared as a ClientCache using the @ClientCacheApplication annotation, then SDG would create a client PROXY Region. Or, if the application was declared as a peer Cache using either the @PeerCacheApplication or @CacheServerApplication annotations, then SDG would create a server PARTITION Region.

Of course, an application developer is always able to override the default when necessary. To override the default applied by Spring Data GemFire, 4 new Region mapping annotations have been introduced:

  • ClientRegion

  • LocalRegion

  • PartitionRegion

  • ReplicateRegion

The ClientRegion mapping annotation is specific to client applications. All other Region mapping annotations listed above can only be used in server applications.

It is sometimes necessary for client applications to create and use "local-only" Regions, perhaps to aggregate data from other Regions in order to analyze the data locally and carry out some function performed by the application for the user. In this case, the data may not necessarily need to be distributed back to the server, not unless other applications need access to the results. This Region might even be temporary and discarded after use, which could be accomplished with Idle-Timeout (TTI) and Time-To-Live (TTL) expiration policies on the Region itself (NOTE: this is independent of and different from "entry" TTI/TTL expiration policies).

In any case, if a user wanted to create a local-only, client Region where the data is not gong to be distributed to a corresponding Region with the same name on the server, the user would specify the @ClientRegion mapping annotation and set the shortcut attribute to ClientRegionShortcut.LOCAL…​

Spring ClientCache application with a local-only, client Region
@ClientRegion(shortcut = ClientRegionShortcut.LOCAL)
class ClientLocalEntityType { .. }

All Region type-specific annotations provide additional attributes that are both common across Region types as well as specific to only that type of Region (e.g. the collocatedWith and redundantCopies attributes in the PartitionRegion annotation apply to PARTITION Regions only).

More details on Pivotal GemFire Region Types can be found here.

6.12.2. Configuring Eviction

Managing data with Pivotal GemFire is an active task. More than likely, tuning will be required and a combination of features (e.g. both Eviction and Expiration) will need to be employed to effectively manage your data in memory with Pivotal GemFire.

Given that Pivotal GemFire is an In-Memory Data Grid (IMDG), data is managed in "memory" and distributed to other nodes that participate in a cluster in order to minimize latency, maximize throughput and ensure that data is highly available. Since not all of an application’s data is going to typically fit in memory, even across an entire cluster of nodes, much less on a single node, capacity can be increased by adding new nodes to the cluster. This is commonly referred to as linear scale-out (rather than scaling up, which means to add more memory, more CPU, more disk, more network bandwidth, basically more of every system resource in order to handle the load).

Still, even with a cluster of nodes, it is usually imperative that only the most important data be kept in memory. Running out-of-memory, or even nearing full capacity, is rarely, if ever, a good thing. Stop-the-world GCs or worse, OutOfMemoryErrors, will bring your application to a screaming halt.

So, to help manage memory and keep the most important data around, Pivotal GemFire supports LRU-based Eviction. That is, Pivotal GemFire evicts Region entries based on when those entries were last accessed by using the Least Recently Used algorithm.

To enable Eviction, simply annotate the application class with @EnableEviction…​

Spring application with Eviction enabled
@EnableEviction(policies = {
    @EvictionPolicy(regionNames = "Books", action = EvictionActionType.INVALIDATE),
    @EvictionPolicy(regionNames = { "Customers", "Orders" }, maximum = 90,
        action = EvictionActionType.OVERFLOW_TO_DISK,
        type = EvictonPolicyType.HEAP_PERCENTAGE)
class ServerApplication { .. }

Eviction policies are usually set on the Regions in the server(s).

As shown above, the policies attribute can specify 1 or more nested @EvictionPolicy annotations, each 1 individually catered to 1 or more Regions where the Eviction policy needs to be applied.

Additionally, a user can reference a custom implementation of Pivotal GemFire’s org.apache.geode.cache.util.ObjectSizer interface defined as a bean in the Spring context and referenced by name using the objectSizerName attribute.

An ObjectSizer allows the user to define the criteria used to evaluate and determine the the size of objects stored in the Region.

See the @EnableEviction annotation Javadoc for a complete list of Eviction configuration options.

More details on Pivotal GemFire Eviction can be found here.

6.12.3. Configuring Expiration

Along with Eviction, Expiration can also be used to manage memory by allowing entries stored in a Region to expire. Both Time-to-Live (TTL) and Idle-Timeout (TTI) based entry expiration policies are supported in Pivotal GemFire.

Spring Data GemFire’s Annotation-based Expiration configuration is based on earlier, existing entry expiration annotation support that was added in Spring Data GemFire many releases ago.

Essentially, Spring Data GemFire’s Expiration annotation support is based on a provided, custom implementation of Pivotal GemFire’s org.apache.geode.cache.CustomExpiry interface. This custom implementation inspects the user’s application domain objects stored in a Region for the presence of type-level Expiration annotations.

Spring Data GemFire provides the following Expiration annotations used on application domain object types, out-of-the-box…​

  • Expiration

  • IdleTimeoutExpiration

  • TimeToLiveExpiration

An application domain object type can be annotated with 1 or more of the Expiration annotations, like so…​

Applicaton domain object specific Expiration policy
@TimeToLiveExpiration(timeout = 30000, action = "INVALIDATE")
class Book { .. }

To enable Expiration, simply annotate the application class with @EnableExpiration…​

Spring application with Expiration enabled
class ServerApplication { .. }

In addition to application domain object type-level Expiration policies, individual Expiration policies on a Region-by-Region basis can be configured directly with the @EnableExpiration annotation as well.

Spring application with global Expiration policies
@EnableExpiration(policies = {
    @ExpirationPolicy(regionNames = "Books", types = ExpirationType.TIME_TO_LIVE),
    @ExpirationPolicy(regionNames = { "Customers", "Orders" }, timeout = 30000,
        action = ExpirationActionType.LOCAL_DESTROY)
class ServerApplication { .. }

Expiration policies are usually set on the Regions in the server(s).

See the @EnableExpiration annotation Javadoc for a complete list of Expiration configuration options.

More details on Pivotal GemFire Expiration can be found here.

6.12.4. Configuring Compression

In addition to Eviction and Expiration a user may also configure her data Regions to use Compression in order to reduce memory consumption.

Pivotal GemFire allows users to compress in-memory Region values using pluggable Compressors, or different compression codecs. Out-of-the-box, Pivotal GemFire uses Google’s Snappy library.

To enable Compression support, simply annotate the application class with @EnableCompression…​

Spring application with Compression enabled
@EnableCompression(compressorBeanName = "MyCompressor", regionNames = { "Customers", "Orders" })
class ClientApplication { .. }
Neither the compressorBeanName nor the regionNames attributes are required.

The compressorBeanName defaults to “SnappyCompressor” enabling Pivotal GemFire’s provided SnappyCompressor by default.

The regionNames attribute is an array of Region names specifying the Regions that will have compression enabled. By default, all Regions will compress values if the regionNames attribute is not explicitly set.

Alternatively, a user may use the spring.data.gemfire.cache.compression.compressor-bean-name and spring.data.gemfire.cache.compression.region-names properties in the application.properties file to set and configure the values of these @EnableCompression annotation attributes.
To use Pivotal GemFire’s Region Compression feature, you must include the org.iq80.snappy:snappy dependency in your Maven pom.xml or build.gradle file when using Gradle. This is only necessary if you use Pivotal GemFire’s default, out-of-the-box support for Region Compression, which uses the SnappyCompressor by default. Of course, if you are using another compression library, you will need to include dependencies for that compression library on your application’s classpath. Additionally, you will need to implement Pivotal GemFire’s Compressors to adapt your compression library of choice, define it as a bean in the Spring context, and then set the compressorBeanName to this custom bean definition.

See the @EnableCompression annotation Javadoc for more details.

More details on Pivotal GemFire Compression can be found here.

6.12.5. Configuring Off-Heap

Another effective means for reducing pressure on the JVM’s Heap memory and minimize GC activity is to use Pivotal GemFire’s Off-Heap memory support. Rather than storing Region entries on the JVM Heap, entries are stored in the system’s main memory.

To enable Off-Heap support, simple annotate the application class with @EnableOffHeap…​

Spring application with Off-Heap enabled
@EnableOffHeap(memorySize = 8192m regionNames = { "Customers", "Orders" })
class ServerApplication { .. }

The memorySize attribute is required. The value for the memorySize attribute specifies the amount of main memory a Region is allowed to use in either megabytes (m) or gigabytes (g).

The regionNames attribute is an array of Region names specifying the Regions that will store entries in main memory. By default, all Regions will use main memory if the regionNames attribute is not explicitly set.

Alternatively, a user may use the spring.data.gemfire.cache.off-heap.memory-size and spring.data.gemfire.cache.off-heap.region-names properties in the application.properties file to set and configure the values of these @EnableOffHeap annotation attributes.

See the @EnableOffHeap annotation Javadoc for more details.

6.12.6. Configuring Indexes

There is not much use in storing data in Regions unless the data can be retrieved.

In addition to Region.get(key) operations, particularly when the key of the value of interest is known in advance, data is commonly retrieved by executing queries on the Regions containing the data. With Pivotal GemFire, queries are written using the Object Query Language (OQL), and the specific data set that a client wishes to access is expressed in the query’s predicate (e.g. SELECT * FROM /Books b WHERE b.author.name = 'Jon Doe').

Generally, querying without Indexes is not very efficient. When executing queries without an Index, Pivotal GemFire performs the equivalent of a full table scan.

Indexes are created and maintained for fields on objects used in query predicates to match the data of interests, expressed by the query’s projection. Different types of Indexes can be created, such as Key and Hash Indexes.

Spring Data GemFire makes it very easy to create Indexes on Regions where the data is stored and accessed. Rather than explicitly declaring Index bean definitions using Spring config as before…​

Index bean definition using Java config
IndexFactoryBean bookIsbnIndex(GemFireCache gemfireCache) {

    IndexFactoryBean bookIsbnIndex = new IndexFactoryBean();


    return bookIsbnIndex;

Or, in XML…​

Index bean definition using XML
  <gfe:index id="BooksIsbnIndex" expression="isbn" from="/Books" type="KEY"/>

Indexes can now be created directly from the fields defined on application domain object types that a user knows will be used in query predicates to speedup those queries. Indexes will be applied even for OQL queries generated from user-defined query methods on an application’s Repository interfaces.

Re-using the example Book class from above, we can annotate the fields on Book that we know will be used in queries we define with query methods in the BookRepository interface…​

Application domain object type modeling a Book using Indexes
class Book {

  private ISBN isbn;

  private Author author;

  private Category category;

  private LocalDate releaseDate;

  private Publisher publisher;

  private String title;


In our new Book class definition, we annotated the author field with @Indexed and the title field with @LuceneIndexed. Also, the isbn field had previously been annotated with Spring Data’s @Id annotation, which identifies the field containing the unique identifier for Book instances, and in Spring Data GemFire, the @Id annotated field or property is used as the key in the Region when storing the entry.

  • @Id annotated fields/properties result in the creation of an Pivotal GemFire KEY Index.

  • @Indexed annotated fields/properties result in the creation of an Pivotal GemFire HASH Index (default).

  • @LuceneIndexed annotated fields/properties result in the creation of an Pivotal GemFire Lucene Index, used in text-based searches with Pivotal GemFire’s Lucene Integration and support.

When the @Indexed annotation is used without setting any attributes, the Index name, expression, and fromClause are derived from the field/property of the object on which the @Indexed annotation has been added. The expression is exactly the name of the field or property. The fromClause is derived from the @Region annotation on the object’s class (or the simple name of the domain object class if the @Region annotation was not specified).

Of course, any of the @Indexed annotation attributes may be explicitly set to override the default values provided by Spring Data GemFire.

Application domain object type modeling a Book using cutomized Indexes
class Book {

  private ISBN isbn;

  @Indexed(name = "BookAuthorNameIndex", expression = "author.name", type = "FUNCTIONAL")
  private Author author;

  private Category category;

  private LocalDate releaseDate;

  private Publisher publisher;

  @LuceneIndexed(name = "BookTitleIndex", destory = true)
  private String title;


The name of the Index, which is auto-generated when not explicitly set, is also used as the name of the bean registered in the Spring context for the Index. If necessary, this Index bean could even be injected by name into another application component.

The generated name of the Index follows the pattern: <Region Name><Field/Property Name><Index Type>Idx. For example, the name of the author Index would be, “BooksAuthorHashIdx”.

To enable Indexing, simply annotate the application class with @EnableIndexing…​

Spring application with Indexing enabled
class ServerApplication { .. }
The @EnablingIndexing annotation has no effect unless the @EnableEntityDefinedRegions is also declared. Essentially, Indexes are defined from entity class types, and entity classes must be scanned in order to inspect the entity’s fields and properties for the presence of Index annotations. Without this scan, Index annotations would not be found. It is also imperative that you limit the scope of the scan.

While Lucene queries are not supported on Spring Data GemFire Repositories (yet), SDG does provide comprehensive support for Pivotal GemFire Lucene queries using the familiar Spring Template pattern.

Finally, we close with a few extra things to keep in mind when using Indexes:

  1. While OQL Indexes are not required to execute OQL Queries, Lucene Indexes are required to execute Lucene, text-based searches.

  2. In addition, OQL Indexes are not persisted to disk; they are maintained only in memory. So, when an Pivotal GemFire node is restarted, the Index must be rebuilt.

  3. You also need to be aware of the overhead associated in maintaining Indexes, particularly since an Index is stored exclusively in memory, and especially when Region entries are updated. Index "maintenance" can be configured as an asynchronous task.

Another optimization that can be utilized when re-starting your Spring application where Indexes have to be rebuilt is to first define all the Indexes upfront and then create them all at once, which, in Spring Data GemFire, happens when the Spring context is refreshed.

Indexes can be defined upfront then created all at once by setting the define attribute on the @EnableIndexing annotation to true.

See Creating Multiple Indexes at Once in Pivotal GemFire’s User Guide for more details.

Creating sensible Indexes is an important task since it is possible for an Index to do more harm than good if not properly designed.

See both the @Indexed annotation and @LuceneIndexed annotation Javadoc for complete list of configuration options.

More details on Pivotal GemFire OQL Queries can be found here.

More details on Pivotal GemFire Indexes can be found here.

More details on Pivotal GemFire Lucene Queries can be found here.

6.12.7. Configuring Disk Stores

Regions can be configured to persist data to disk. Regions can also be configured to overflow data to disk when Region entries are evicted. In both cases, a DiskStore is required to persist or overflow the data. When an explicit DiskStore has not been set on a Region with persistence or overflow configured, then Pivotal GemFire will use the "DEFAULT" DiskStore.

However, it is possible and recommended to define Region-specific DiskStores when persisting or overflowing data to disk.

Spring Data GemFire provides Annotation support for defining and creating application Region DiskStores by annotating the application class with the @EnableDiskStore and @EnableDiskStores annotations.

@EnableDiskStores is a composite annotation for aggregating 1 or more @EnableDiskStore annotations.

For example, while Book product information might mostly consist of reference data, from some external data source (e.g. Amazon), Order data is most likely going to be transactional in nature and something the application is going to need to retain, maybe even overflow to disk if the transaction volume is high enough, or so any Book publisher hopes, anyway.

Using the @EnableDiskStore annotation, I can define and create a DiskStore as follows…​

Spring application defining a DiskStore
@EnableDiskStore(name = "OrdersDiskStore", autoCompact = true, compactionThreshold = 70,
    maxOplogSize = 512, diskDirectories = @DiskDiretory(location = "/absolute/path/to/order/disk/files"))
class ServerApplication { .. }

Again, more than 1 DiskStore can be defined using the composite, @EnableDiskStores annotation.

Like other Annotations in Spring Data GemFire’s Annotation-based configuration model, both @EnableDiskStore and @EnableDiskStores have many attributes along with associated configuration properties to apply additional configuration to DiskStores created at runtime.

Additionally, the @EnableDiskStores annotation defines certain common DiskStore attributes that apply to all DiskStores created from @EnableDiskStore annotations composed with the @EnableDiskStores annotation itself. Individual DiskStore configuration can override a particular global setting, but the @EnableDiskStores annotation defines common configuration attributes for all DiskStores out of convenience.

Spring Data GemFire also provides the DiskStoreConfigurer callback interface that can be declared in Java config and used instead of configuration properties to customize a DiskStore at runtime…​

Spring application with custom DiskStore configuration
@EnableDiskStore(name = "OrdersDiskStore", autoCompact = true, compactionThreshold = 70,
    maxOplogSize = 512, diskDirectories = @DiskDiretory(location = "/absolute/path/to/order/disk/files"))
class ServerApplication {

  DiskStoreConfigurer ordersDiskStoreDiretoryConfigurer(
          @Value("${orders.disk.store.location}") String location) {

      return (beanName, diskStoreFactoryBean) -> {

          if ("OrdersDiskStore".equals(beanName) {
              diskStoreFactoryBean.setDiskDirs(Collections.singletonList(new DiskDir(location));

See the @EnableDiskStore and @EnableDiskStores annotation Javadoc for more details on the available attributes as well as associated configuration properties.

More details on Pivotal GemFire Region Persistence and Overflow (using Disk Stores) can be found here.

6.13. Configuring Continuous Queries

Another very important and useful feature of Pivotal GemFire is Continuous Querying.

In a world of Internet-enabled things, events and streams of data are coming in from everywhere. Being able to handle and process a large stream of data and react to events in real-time, as they happen, is becoming an increasingly important requirement for many applications. One example is self-driving vehicles. Being able to receive, filter, transform, analyze and act on data in real-time is a key differentiator and characteristic of real-time enabled applications.

Fortunately, Pivotal GemFire was ahead of its time in this regard. Using Continuous Queries (CQ) a client application can express the data, or events it is interested in and register listeners to handle and process the events as they arrive. The data that a client application may be interested in is expressed in a OQL query, where the query predicate is used to filter, or identify the data of interests. When data is changed or added and it matches the criteria defined in the query predicate of the registered CQ, the client application is notified.

Spring Data GemFire makes defining and registering CQs along with an associated listener to handler and process CQ events without all the cruft of Pivotal GemFire’s plumbing, a non-event (no pun intended). SDG’s new Annotation-based configuration for CQs builds on the already existing Continuous Query support in the Continuous Query Listener Container.

For instance, say a Book publisher wants to register interests in and receive notification anytime orders (demand) for a Book exceeds the current inventory (supply), then the publisher’s print application might register the following CQ…​

Spring ClientCache application with registered CQ and Listener.
@ClientCacheApplication(subcriptionEnabled = true)
class PublisherPrintApplication {

    @ContinuousQuery(name = "DemandExceedsSupply", query =
       "SELECT book.* FROM /Books book, /Inventory inventory
        WHERE book.title = 'How to crush it in the Book business like Amazon"
        AND inventory.isbn = book.isbn
        AND inventory.available < (
            SELECT sum(order.lineItems.quantity)
            FROM /Orders order
            WHERE order.status = 'pending'
            AND order.lineItems.isbn = book.isbn
    void handleSupplyProblem(CqEvent event) {
        // start printing more Books, fast!

To enable Continuous Queries, simply annotate your application class with @EnableContinuousQueries.

Defining Continuous Queries is as simple as annotating any Spring @Component annotated POJO class methods with the @ContinuousQuery annotation, in similar fashion to SDG’s Function annotated POJO methods. A method defined with a CQ using the @ContinuousQuery annotation will be called anytime data matching the query predicate is added or changed.

Additionally, the POJO method signature should adhere to the requirements outlined in the section on ContinuousQueryListener and ContinuousQueryListenerAdapter.

See the @EnableContinuousQueries and @ContinuousQuery annotation Javadoc for more details on available attributes and configuration settings.

More details on Spring Data GemFire’s Continuous Query support can be found here.

More details on Pivotal GemFire’s Continuous Queries can be found here.

6.14. Configuring Spring’s Cache Abstraction

With Spring Data GemFire, Pivotal GemFire can be used as a caching provider in Spring’s Cache Abstraction.

In Spring’s Cache Abstraction, the caching annotations (e.g. @Cacheable) identify the cache on which a cache lookup is performed before invoking a potentially expensive operation, or where the results of an application service method are cached after the operation is invoked.

In Spring Data GemFire, a Spring Cache corresponds directly to a Region. The Region must exist before any @Cacheable application service method is called. This is true for any of Spring’s caching annotations (i.e. @Cacheable, @CachePut and @CacheEvict) that identify the cache to use in the operation.

For instance, our publisher’s Point-of-Sale (POS) application might have a feature to determine, or lookup the Price of a Book during a sales transaction.

class PointOfSaleService

  Price runPriceCheckFor(Book book) {

  Receipt checkout(Order order) {


To make the application developer’s life easier when using Spring Data GemFire and Pivotal GemFire with Spring’s Cache Abstraction, 2 new features have been added to the new Annotation-based configuration model.

Given the following Spring caching configuration…​

Enabling Caching using Pivotal GemFire with Spring Data GemFire
class CachingConfiguration {

  GemfireCacheManager cacheManager(GemFireCache gemfireCache) {

      GemfireCacheManager cacheManager = new GemfireCacheManager();


      return cacheManager;

  PartitionedRegionFactoryBean<Book, Price> bookPricesRegion(GemFireCache gemfireCache) {

    PartitionedRegionFactoryBean<Book, Price> bookPricesRegion =
        new PartitionedRegionFactoryBean<>();


    return bookPricesRegion;

  PointOfSaleService pointOfSaleService(..) {
      return new PointOfSaleService(..);

Using Spring Data GemFire’s new features, the same caching configuration can be simplified to…​

Enabling GemFire Caching
class CachingConfiguration {

  PointOfSaleService pointOfSaleService(..) {
      return new PointOfSaleService(..);

First, the @EnableGemfireCaching annotation replaces both the Spring EnableCaching annotation along with the need to declare an explicit cacheManager bean definition in the Spring config.

Second, the @EnableCachingDefinedRegions annotation, like the @EnableEntityDefinedRegions annotation described in Configuring Regions, inspects all the Spring caching annotated application service components to identify all the caches that will be needed by the application at runtime and creates Regions in Pivotal GemFire for these caches on application startup.

The Region created is local to the application process that created the Region. If the application is a peer Cache, then the Region will only exist on the application node. If the application is a ClientCache, then SDG creates a client PROXY Region and expects that a Region with the same name already exists on the servers in the cluster.

SDG cannot determine the cache required by a service method using a Spring CacheResolver to resolve the cache used in the operation at runtime.
SDG does not currently identify JCache, JSR-107 cache annotations used on application service components. Refer to the core Spring Framework Reference Guide for the equivalent Spring caching annotation to use in place of JCache, JSR-107 caching annotations.

Refer to the section, Support for the Spring Cache Abstraction for more details on using Pivotal GemFire as a caching provider in Spring’s Cache Abstraction.

More details on Spring’s Cache Abstraction can be found here.

6.15. Configuring Cluster Configuration Push

This may be the most exciting new feature in Spring Data GemFire.

When a client application class is annotated with @EnableClusterConfiguration, any Regions or Indexes defined and declared as beans in the Spring context by the client application are "pushed" to the cluster of servers to which the client is connected. Not only that, but this "push" is performed in such a way that Pivotal GemFire will remember the configuration pushed by the client. If all the nodes in the cluster go down, they will come back up with the same configuration as before.

In a sense, this feature is not much different than if a user were to use Gfsh to create the Regions and Indexes on all the servers in the cluster. Except now, with Spring Data GemFire, users does not need to use Gfsh to create Regions and Indexes. The user’s Spring Boot application, enabled with the power of Spring Data GemFire, already contains all the configuration meta-data SDG needs to create Regions and Indexes for the user.

When users are using the Spring Data Repository abstraction, we know all the Regions (e.g. @Region annotated entity types) and Indexes (e.g. @Indexed annotated entity fields and properties) that the users' application will need. When users are using Spring’s Cache Abstraction, we also know all the Regions for all the caches identified in the caching annotations that the application is going to need. Essentially, the user is already telling us everything we need to know just by developing her application with the entire Spring Framework and all of its provided services, infrastructure, etc, whether expressed in Annotation meta-data, Java, XML or otherwise, and whether for configuration, for mapping, or whatever purpose.

The user can focus on her application business logic along with using the framework provided services and supporting infrastructure (e.g. Spring Data Repositories, Spring’s Transaction Management, Spring Caching, etc) and Spring Data GemFire will take care of all the Pivotal GemFire plumbing required by those framework services on the user’s behalf.

Pushing configuration from the client to the servers in the cluster and having the cluster remember it is made possible in part by the use of Pivotal GemFire’s Cluster Configuration service. Pivotal GemFire’s Cluster Configuration service is also the same service used by Gfsh to record schema-related changes issued by the user to the cluster from the shell.

Of course, since the cluster "remembers" the prior configuration pushed by a client from a previous run perhaps, Spring Data GemFire is careful not to stomp on any existing Regions and Indexes already defined in the servers. This is especially important when Regions already contain data.

Currently there is no option to overwrite any existing Region or Index definitions. To recreate a Region or Index, the user must use Gfsh to destroy the Region or Index first and then restart the client application so that configuration will be pushed up to the server again. Alternatively a user can just use Gfsh to (re-)define the Regions and Indexes manually.
Unlike Gfsh, Spring Data GemFire only supports the creation of Regions and Indexes on the servers from a client. For advanced configuration and use cases, Gfsh should be used to manage the cluster.

For a moment, imagine the power expressed in the following configuration…​

Spring ClientCache application
class ClientApplication { .. }

An application developer instantly gets a Spring Boot, Pivotal GemFire ClientCache application using Spring Data Repositories with Spring’s Cache Abstraction, using Pivotal GemFire as the caching provider, where Regions and Indexes are not only created on the client, but pushed to the servers in the cluster.

All the application developer need do is define the application’s domain model objects annotated with mapping and Index annotations, define Repository interfaces supporting basic data access operations and querying for each of the entity types, define the service components containing the business logic manipulating the entities, declare the appropriate annotations on service methods that require caching, transactional behavior, etc, and the developer is in business. Nothing the user did in this case pertains to infrastructure and plumbing required in the application’s back-end services (e.g. Pivotal GemFire). Database users have similar features, no Spring, Pivotal GemFire developers can too.

When combined with a couple more Spring Data GemFire Annotations…​

  • @EnableContinuousQueries

  • @EnableGemfireFunctionExecutions

  • @EnableGemfireCacheTransactions

Then, the application is really going to start to take flight.

See the @EnableClusterConfiguration annotation Javadoc for more details.

6.16. Configuring Security

Without a doubt, application Security is extremely important and Spring Data GemFire provides comprehensive support for securing both Pivotal GemFire clients and servers.

Recently, Pivotal GemFire introduced a new Integrated Security framework, replacing its old Authentication and Authorization Security model, for handling authentication and authorization. One of the main features and benefits of this new Security framework is that it integrates with Apache Shiro and can therefore delegate both authentication and authorization requests to Apache Shiro when enforcing security.

The following demonstrates how Spring Data GemFire can simplify Pivotal GemFire’s Security story even further.

6.16.1. Configuring Server Security

There are several different ways in which a user can configure Security for servers in an Pivotal GemFire cluster.

  1. Implement the Pivotal GemFire org.apache.geode.security.SecurityManager interface and set Pivotal GemFire’s security-manager property to refer to your application SecurityManager implementation by the FQCN. Alternatively, users can construct and initialize an instance of their SecurityManager implementation and set it with CacheFactory.setSecurityManager(:SecurityManager) method when creating an instance of an Pivotal GemFire peer Cache.

  2. Create an Apache Shiro shiro.ini file with the users, roles and permissions defined for your application, then set the Pivotal GemFire security-shiro-init property to refer to this shiro.ini file, which must be available in the CLASSPATH.

  3. Using just Apache Shiro, annotate your Spring Boot application class with Spring Data GemFire’s new @EnableSecurity annotation and define 1 or more Apache Shiro Realms (as needed) as beans in the Spring context for accessing your application’s Security meta-data (i.e. authorized users, roles and permissions), and your done!

The problem with the first approach is that a user must implement his/her own SecurityManager, which can be quite tedious and error prone. Implementing a custom SecurityManager does afford a user some flexibility in accessing Security meta-data from whatever data source stores the meta-data, such as LDAP or even a proprietary, internal data source, but then that is a problem already solved by configuring and using Apache Shiro Realms, which is more universally known and non-Pivotal GemFire specific.

See Pivotal GemFire’s Security examples for Authentication and Authorization as 1 possible way to implement your own custom, application specific SecurityManager.

The second approach using an Apache Shiro INI file is marginally better, but a user still needs to be familiar with the INI file format in the first place. Additionally, an INI file is static and not easily updatable at runtime.

The third approach is the most ideal since it adheres to widely known and industry accepted concepts (i.e. Apache Shiro’s Security framework) and is easy to setup…​

Spring server application using Apache Shiro
class ServerApplication {

  PropertiesRealm shiroRealm() {
      PropertiesRealm propertiesRealm = new PropertiesRealm();
      propertiesRealm.setPermissionResolver(new GemFirePermissionResolver());
      return propertiesRealm;
The configured Realm shown in the example above could have easily been any of Apache Shiro’s supported Realms out-of-the-box (ActiveDirectory, JDBC, JNDI, LDAP, or even a Realm supporting the INI format) or even a custom implementation of an Apache Shiro Realm implemented by the user. See Apache Shiro’s documentation on Realms for more details.

When Apache Shiro is on the CLASSPATH of the servers in the cluster and 1 or more Apache Shiro Realms have been defined as beans in the Spring context, Spring Data GemFire will detect this configuration and use Apache Shiro as the Security provider to secure your Pivotal GemFire servers when the @EnableSecurity annotation is used.

Earlier, information was posted on Spring Data GemFire’s support for Pivotal GemFire’s new Integrated Security framework using Apache Shiro in this spring.io blob post.

See the @EnableSecurity annotation Javadoc for more details on available attributes and associated configuration properties.

More details on Pivotal GemFire Security can be found here.

6.16.2. Configuring Client Security

The Security story would not be complete without discussing how to secure Spring-based, Pivotal GemFire cache client applications.

Pivotal GemFire’s process to securing a client application is, well, rather involved. In a nutshell, a user essentially needs to…​

  1. Provide an implementation of the org.apache.geode.security.AuthInitialize interface.

  2. Set the Pivotal GemFire security-client-auth-init (System) property to refer to the custom, application-provided AuthInitialize interface.

  3. And finally, a user would typically specify the user credentials in a proprietary, Pivotal GemFire gfsecurity.properties file.

Spring Data GemFire simplifies all of that using the same @EnableSecurity annotation as applied to server applications. In other words, the same @EnableSecurity annotation handles Security for both client and server applications. This makes it easier for users when they decide to switch their applications from an embedded peer Cache application to a ClientCache application, for instance. Simply change the SDG annotation from @PeerCacheApplication or @CacheServerApplication to @ClientCacheApplication and you are done.

Effectively, all a user need do on the client is…​

Spring client application using @EnableSecurity
class ClientApplication { .. }

Then define the familiar Spring Boot application.properties file containing the required username and password Security properties and you are all set.

Spring Boot application.properties file with the required Security credentials
[email protected][email protected]

That was easy!

By default, Spring Boot can find an application.properties file when placed in the root of the application’s CLASSPATH. Of course, Spring supports may ways to to locate resources using its Resource abstraction.

See the @EnableSecurity annotation Javadoc for more details on available attributes and associated configuration properties.

More details on Pivotal GemFire Security can be found here.

6.17. Configuration Tips

The following tips will help users get the most out of using the new Annotation-based configuration model.

6.18. Configuration Organization

As we saw in the section on Configuring Cluster Configuration Push, when many Pivotal GemFire and/or Spring Data GemFire features are enabled using Annotations, we start to stack a lot of Annotations on the Spring @Configuration or @SpringBootApplication class. In this situation, it makes sense to start compartmentalizing the configuration a bit.

For instance, given…​

Spring ClientCache application with the kitcken sink to boot
class ClientApplication { .. }

We could break this configuration down by concern. For example…​

Spring ClientCache application with the kitcken sink to boot
@Import({ CachingConfiguration.class, GemFireConfiguration.class,
    QueriesAndFunctionsConfiguration.class, RepositoryConfiguration.class })
class ClientApplication { .. }

class CachingConfiguration { .. }

class GemFireConfiguration { .. }

class QueriesAndFunctionsConfiguration {

   void processCqEvent(CqEvent event) {

class RepositoryConfiguration { .. }

Spring does not care. Organize your application configuration as you see fit.

6.19. Additional Configuration-based Annotations

SDG Annotations you never heard of…​

The following SDG Annotations were not discussed in this reference documentation either because the Annotation supports a deprecated feature of Pivotal GemFire, or there are better, alternative ways to accomplishing the function that the Annotation provides.

  • @EnableAuth - enable Pivotal GemFire’s old Authentication/Authorization Security model. (Deprecated; use Pivotal GemFire’s new Integrated Security framework discussed here).

  • @EnableAutoRegionLookup - Not recommended. Essentially, this Annotation supports finding Regions defined in external configuration meta-data (e.g. cache.xml, or Cluster Configuration when applied to a server) and registers those Regions as beans in the Spring context automatically. Users should generally prefer Spring config when using Spring and Spring Data GemFire. See Configuring Regions and Configuring Cluster Configuration Push instead.

  • @EnableBeanFactoryLocator - enables the SDG GemfireBeanFactoryLocator feature, which is only useful, again, when using external configuration meta-data (e.g. cache.xml). For example, if a user defines a CacheLoader on a Region defined in cache.xml, the user can still auto-wire this CacheLoader with say, a relational database DataSource bean defined in Spring confif. This Annotation takes advantage of this SDG feature and might be useful for users who have a large amount of legacy configuration meta-data, like cache.xml files.

  • @EnableGemFireAsLastResource - is actually discussed in Global - JTA Transaction Management with Pivotal GemFire.

  • @EnableMcast - enables Pivotal GemFire’s old peer discovery mechanism using UDP-based Multi-cast Networking. (Deprecated; users should be using Pivotal GemFire Locators instead; see Configuring Locators.

  • @EnableRegionDataAccessTracing - is useful for debugging purposes; the Annotation enables tracing for all data access operations performed on a Region by registering an AOP Aspect that proxies all Regions declared as beans in the Spring context, intercepting the Region op and logging the event.

6.20. Conclusion

As we learned in the previous sections, there is a tremendous amount of power provided by Spring Data GemFire's new Annotation-based configuration model. Hopefully, it lives up to its goal of making it easier for users to get started quickly when using Pivotal GemFire with Spring.

Keep in mind when using the new Annotations that it does not preclude you, the application developer, from using Java config, or even XML, if you prefer. You can even combine all 3 approaches by using Spring’s @Import and @ImportResource annotations on a Spring @Configuration or @SpringBootApplication class, if you like. The moment you explicitly provide a bean definition that would otherwise be provided by Spring Data GemFire using an Annotation, the Annotation-based configuration backs away.

In certain cases you may even need to fallback to Java config, as in the Configurers case, to handle more complex or conditional configuration logic that is not easily expressed in or cannot be accomplished using Annotations. Do not be alarmed; this is to be expected.

For example, another case where Java config or XML will be needed is when configuring Pivotal GemFire WAN components, which currently do not have any Annotation configuration support. However, defining and registering WAN components is as simple as using the org.springframework.data.gemfire.wan.GatewaReceiverFactoryBean and org.springframework.data.gemfire.wan.GatewaySenderFactoryBean API classes in Java configuration on your Spring @Configuration or @SpringBootApplication classes (recommended).

The Annotations were not meant to handle every situation; the Annotations were meant to help application developers get up and running as quickly and as easily as possible, especially during development.

7. Working with Pivotal GemFire APIs

Once the Pivotal GemFire Cache and Regions have been configured, they can be injected and used inside application objects. This chapter describes the integration with Spring’s Transaction Management functionality and DAO exception hierarchy. This chapter also covers support for dependency injection of GemFire managed objects.

7.1. GemfireTemplate

As with many other high-level abstractions provided by Spring projects, Spring Data GemFire provides a template to simplify GemFire data access. The class provides several one-liner methods containing common Region operations, but also has the ability to execute code against the native GemFire API without having to deal with GemFire checked exceptions by using a GemfireCallback.

The template class requires a GemFire Region instance, and once configured, is thread-safe and can be reused across multiple application classes:

<bean id="gemfireTemplate" class="org.springframework.data.gemfire.GemfireTemplate" p:region-ref="SomeRegion"/>

Once the template is configured, a developer can use it alongside GemfireCallback to work directly with the GemFire Region without having to deal with checked exceptions, threading or resource management concerns:

template.execute(new GemfireCallback<Iterable<String>>() {
	public Iterable<String> doInGemfire(Region region) throws GemFireCheckedException, GemFireException {
		Region<String, String> localRegion = (Region<String, String>) region;

		localRegion.put("1", "one");
		localRegion.put("3", "three");

		return localRegion.query("length < 5");

For accessing the full power of the Pivotal GemFire query language, a developer can use the find and findUnique methods, which, as opposed to the query method, can execute queries across multiple Regions, execute projections, and the like.

The find method should be used when the query selects multiple items (through`SelectResults`) and the latter, findUnique, as the name suggests, when only one object is returned.

7.2. Exception Translation

Using a new data access technology requires not only accommodating a new API but also handling exceptions specific to that technology.

To accommodate the exception handling case, the Spring Framework provides a technology agnostic and consistent exception hierarchy that abstracts the application from proprietary, and usually "checked", exceptions to a set of focused runtime exceptions.

As mentioned in Spring Framework’s documentation, Exception translation can be applied transparently to your Data Access Objects (DAO) through the use of the @Repository annotation and AOP by defining a PersistenceExceptionTranslationPostProcessor bean. The same exception translation functionality is enabled when using GemFire as long as the CacheFactoryBean is declared, e.g. using either a <gfe:cache/> or <gfe:client-cache> declaration, which acts as an exception translator and is automatically detected by the Spring infrastructure and used accordingly.

7.3. Local, Cache Transaction Management

One of the most popular features of the Spring Framework is Transaction Management.

If you are not familiar with Spring’s transaction abstraction then we strongly recommend reading about Spring’s Transaction Management infrastructure as it offers a consistent programming model that works transparently across multiple APIs and can be configured either programmatically or declaratively (the most popular choice).

For Pivotal GemFire, Spring Data GemFire provides a dedicated, per-cache, PlatformTransactionManager that, once declared, allows Region operations to be executed atomically through Spring:

<gfe:transaction-manager id="txManager" cache-ref="myCache"/>
The example above can be simplified even further by eliminating the cache-ref attribute if the GemFire cache is defined under the default name, gemfireCache. As with the other Spring Data GemFire namespace elements, if the cache bean name is not configured, the aforementioned naming convention will be used. Additionally, the transaction manager name is “gemfireTransactionManager” if not explicitly specified.

Currently, Pivotal GemFire supports optimistic transactions with read committed isolation. Furthermore, to guarantee this isolation, developers should avoid making in-place changes that manually modify values present in the cache. To prevent this from happening, the transaction manager configures the cache to use copy on read semantics by default, meaning a clone of the actual value is created each time a read is performed. This behavior can be disabled if needed through the copyOnRead property.

For more information on the semantics and behavior of the underlying Geode transaction manager, please refer to the Geode CacheTransactionManager Javadoc as well as the documentation.

7.4. Global, JTA Transaction Management

It is also possible for Pivotal GemFire to participate in a Global, JTA based transaction, such as a transaction managed by an Java EE Application Server (e.g. WebSphere Application Server, a.k.a. WAS) using Container Managed Transactions (CMT) along with other JTA resources.

However, unlike many other JTA "compliant" resources (e.g. JMS Message Brokers like ActiveMQ), Pivotal GemFire is not an XA compliant resource. Therefore, Pivotal GemFire must be positioned as the "Last Resource" in a JTA transaction (prepare phase) since it does not implement the 2-phase commit protocol, or rather does not handle distributed transactions.

Many managed environments with CMT maintain support for "Last Resource", non-XA compliant resources in JTA transactions though it is not actually required in the JTA spec. More information on what a non-XA compliant, "Last Resource" means can be found in Red Hat’s documentation. In fact, Red Hat’s JBoss project, Narayana is one such LGPL Open Source implementation. Narayana refers to this as "Last Resource Commit Optimization" (LRCO). More details can be found here.

However, whether you are using Pivotal GemFire in a standalone environment with an Open Source JTA Transaction Management implementation that supports "Last Resource", or a managed environment (e.g. Java EE AS such as WAS), Spring Data Geode has you covered.

There are a series of steps you must complete to properly use Pivotal GemFire as a "Last Resource" in a JTA transaction involving more than 1 transactional resource. Additionally, there can only be 1 non-XA compliant resource (e.g. Pivotal GemFire) in such an arrangement.

1) First, you must complete Steps 1-4 in GemFire’s documentation here.

#1 above is independent of your Spring [Boot] and/or [Data GemFire] application and must be completed successfully.

2) Referring to Step 5 in GemFire’s documentation, Spring Data GemFire’s Annotation support will attempt to set the GemFireCache, copyOnRead property for you when using the @EnableGemFireAsLastResource annotation.

However, if SDG’s auto-configuration is unsuccessful then you must explicitly set the copy-on-read attribute on the <gfe:cache> or <gfe:client-cache> element in XML or the copyOnRead property of the SDG CacheFactoryBean class in JavaConfig to true. For example…​

Peer Cache XML:

  <gfe:cache ... copy-on-read="true"/>

Peer Cache JavaConfig:

  CacheFacatoryBean gemfireCache() {

    CacheFactoryBean gemfireCache = new CacheFactoryBean();


    return gemfireCache;

Client Cache XML:

  <gfe:client-cache ... copy-on-read="true"/>

Client Cache JavaConfig:

  ClientCacheFacatoryBean gemfireCache() {

    ClientCacheFactoryBean gemfireCache = new ClientCacheFactoryBean();


    return gemfireCache;
explicitly setting the copy-on-read attribute or optionally the copyOnRead property really should not be necessary.

3) At this point, you skip Steps 6-8 in GemFire’s documentation and let Spring Data Geode work its magic. All you need do is annotate your Spring @Configuration class with Spring Data GemFire’s new @EnableGemFireAsLastResource annotation and a combination of Spring’s Transaction Management infrastructure and Spring Data GemFire’s @EnableGemFireAsLastResource configuration does the trick.

The configuration looks like this…​

@EnableTransactionManagement(order = 1)
class GeodeConfiguration {


The only requirements are…​

3.1) The @EnableGemFireAsLastResource annotation must be declared on the same Spring @Configuration class where Spring’s @EnableTransactionManagement annotation is also specified.

3.2) The order attribute of the @EnableTransactionManagement annotation must be explicitly set to an integer value that is not Integer.MAX_VALUE or Integer.MIN_VALUE (defaults to Integer.MAX_VALUE).

Of course, hopefully you are aware that you also need to configure Spring’s JtaTransactionManager when using JTA Transactions like so..

public JtaTransactionManager transactionManager(UserTransaction userTransaction) {

   JtaTransactionManager transactionManager = new JtaTransactionManager();


   return transactionManager;
The configuration in section Local, Cache Transaction Management does not apply here. The use of Spring Data GemFire’s GemfireTransactionManager is applicable only in "Local", Cache Transactions, not "Global", JTA Transactions. Therefore, you do not configure the SDG GemfireTransactionManager in this case. You configure Spring’s JtaTransactionManager as shown above.

For more details on using Spring’s Transaction Management with JTA, see here.

Effectively, Spring Data GemFire’s @EnableGemFireAsLastResource annotation imports configuration containing 2 Aspect bean definitions that handles the GemFire o.a.g.ra.GFConnectionFactory.getConnection() and o.a.g.ra.GFConnection.close() operations at the appropriate points during the transactional operation.

Specifically, the correct sequence of events are…​

  1. jtaTransation.begin()

  2. GFConnectionFactory.getConnection()

  3. Call the application’s @Transactional service method

  4. Either jtaTransaction.commit() or jtaTransaction.rollback()

  5. Finally, GFConnection.close()

This is consistent with how you, as the application developer, would code this manually if you had to use the JTA API + GemFire API yourself, as shown in the GemFire example.

Thankfully, Spring does the heavy lifting for you and all you need do after applying the appropriate configuration (shown above) is…​

class MyTransactionalService ... {

  public <Return-Type> someTransactionalMethod() {
    // perform business logic interacting with and accessing multiple JTA resources atomically, here


#1 & #4 above are appropriately handled for you by Spring’s JTA based PlatformTransactionManager once the @Transactional boundary is entered by your application (i.e. when the MyTransactionSerivce.someTransactionalMethod() is called).

#2 & #3 are handled by Spring Data GemFire’s new Aspects enabled with the @EnableGemFireAsLastResource annotation.

#3 of course is the responsibility of your application.

Indeed, with the appropriate logging configured, you will see the correct sequence of events…​

2017-Jun-22 11:11:37 TRACE TransactionInterceptor - Getting transaction for [example.app.service.MessageService.send]

2017-Jun-22 11:11:37 TRACE GemFireAsLastResourceConnectionAcquiringAspect - Acquiring GemFire Connection
from GemFire JCA ResourceAdapter registered at [gfe/jca]

2017-Jun-22 11:11:37 TRACE MessageService - PRODUCER [ Message :
[{ @type = example.app.domain.Message, id= MSG0000000000, message = SENT }],
JSON : [{"id":"MSG0000000000","message":"SENT"}] ]

2017-Jun-22 11:11:37 TRACE TransactionInterceptor - Completing transaction for [example.app.service.MessageService.send]

2017-Jun-22 11:11:37 TRACE GemFireAsLastResourceConnectionClosingAspect - Closed GemFire Connection @ [Reference [...]]

For more details on using Pivotal GemFire in JTA transactions, see here.

For more details on configuring Pivotal GemFire as a "Last Resource", see here.

7.5. Continuous Query (CQ)

A powerful functionality offered by Pivotal GemFire is Continuous Query (or CQ). In short, CQ allows one to create and register an OQL query, and then automatically be notified when new data that gets added to GemFire matches the query predicate. Spring Data GemFire provides dedicated support for CQs through the org.springframework.data.gemfire.listener package and its listener container; very similar in functionality and naming to the JMS integration in the Spring Framework; in fact, users familiar with the JMS support in Spring, should feel right at home.

Basically Spring Data GemFire allows methods on POJOs to become end-points for CQ. Simply define the query and indicate the method that should be called to be notified when there is a match. Spring Data GemFire takes care of the rest. This is very similar to Java EE’s message-driven bean style, but without any requirement for base class or interface implementations, based on Pivotal GemFire.

Currently, Continuous Query is only supported in GemFire’s client/server topology. Additionally, the client Pool used is required to have the subscription enabled. Please refer to the GemFire documentation for more information.

7.5.1. Continuous Query Listener Container

Spring Data GemFire simplifies creation, registration, life-cycle and dispatch of CQ events by taking care of the infrastructure around CQ with the use of SDG’s ContinuousQueryListenerContainer, which does all the heavy lifting on behalf of the user. Users familiar with EJB and JMS should find the concepts familiar as it is designed as close as possible to the support provided in the Spring Framework with its Message-driven POJOs (MDPs).

The SDG ContinuousQueryListenerContainer acts as an event (or message) listener container; it is used to receive the events from the registered CQs and invoke the POJOs that are injected into it. The listener container is responsible for all threading of message reception and dispatches into the listener for processing. It acts as the intermediary between an EDP (Event-driven POJO) and the event provider and takes care of creation and registration of CQs (to receive events), resource acquisition and release, exception conversion and the like. This allows you, as an application developer, to write the (possibly complex) business logic associated with receiving an event (and reacting to it), and delegate the boilerplate GemFire infrastructure concerns to the framework.

The listener container is fully customizable. A developer can chose either to use the CQ thread to perform the dispatch (synchronous delivery) or a new thread (from an existing pool) for an asynchronous approach by defining the suitable java.util.concurrent.Executor (or Spring’s TaskExecutor). Depending on the load, the number of listeners or the runtime environment, the developer should change or tweak the executor to better serve her needs. In particular, in managed environments (such as app servers), it is highly recommended to pick a proper TaskExecutor to take advantage of its runtime.

7.5.2. The ContinuousQueryListener and ContinuousQueryListenerAdapter

The ContinuousQueryListenerAdapter class is the final component in Spring Data GemFire CQ support. In a nutshell, class allows you to expose almost any implementing class as an EDP with minimal constraints. ContinuousQueryListenerAdapter implements the ContinuousQueryListener interface, a simple listener interface similar to GemFire’s CqListener.

Consider the following interface definition. Notice the various event handling methods and their parameters:

public interface EventDelegate {
     void handleEvent(CqEvent event);
     void handleEvent(Operation baseOp);
     void handleEvent(Object key);
     void handleEvent(Object key, Object newValue);
     void handleEvent(Throwable throwable);
     void handleQuery(CqQuery cq);
     void handleEvent(CqEvent event, Operation baseOp, byte[] deltaValue);
     void handleEvent(CqEvent event, Operation baseOp, Operation queryOp, Object key, Object newValue);
package example;

class DefaultEventDelegate implements EventDelegate {
    // implementation elided for clarity...

In particular, note how the above implementation of the EventDelegate interface has no GemFire dependencies at all. It truly is a POJO that we can and will make into an EDP via the following configuration.

the class does not have to implement an interface; an interface is only used to better showcase the decoupling between the contract and the implementation.
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
      http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
      http://www.springframework.org/schema/gemfire http://www.springframework.org/schema/gemfire/spring-gemfire.xsd


	<gfe:pool subscription-enabled="true">
	   <gfe:server host="localhost" port="40404"/>

	   <!-- default handle method -->
	   <gfe:listener ref="listener" query="SELECT * FROM /SomeRegion"/>
	   <gfe:listener ref="another-listener" query="SELECT * FROM /AnotherRegion" name="myQuery" method="handleQuery"/>

	<bean id="listener" class="example.DefaultMessageDelegate"/>
	<bean id="another-listener" class="example.DefaultMessageDelegate"/>
The example above shows a few of the various forms that a listener can have; at its minimum, the listener reference and the actual query definition are required. It’s possible, however, to specify a name for the resulting Continuous Query (useful for monitoring) but also the name of the method (the default is handleEvent). The specified method can have various argument types, the EventDelegate interface lists the allowed types.

The example above uses the Spring Data GemFire namespace to declare the event listener container and automatically register the listeners. The full blown, beans definition is displayed below:

<!-- this is the Event Driven POJO (MDP) -->
<bean id="eventListener" class="org.springframework.data.gemfire.listener.adapter.ContinuousQueryListenerAdapter">
        <bean class="gemfireexample.DefaultEventDelegate"/>

<!-- and this is the event listener container... -->
<bean id="gemfireListenerContainer" class="org.springframework.data.gemfire.listener.ContinuousQueryListenerContainer">
    <property name="cache" ref="gemfireCache"/>
    <property name="queryListeners">
      <!-- set of CQ listeners -->
        <bean class="org.springframework.data.gemfire.listener.ContinuousQueryDefinition" >
               <constructor-arg value="SELECT * FROM /SomeRegion" />
               <constructor-arg ref="eventListener"/>

Each time an event is received, the adapter automatically performs type translation between the GemFire event and the required method argument(s) transparently. Any exception caused by the method invocation is caught and handled by the container (by default, being logged).

7.6. Wiring Declarable Components

Pivotal GemFire XML configuration (usually referred to as cache.xml) allows user objects to be declared as part of the configuration. Usually these objects are CacheLoaders or other pluggable callback components supported by GemFire. Using native GemFire configuration, each user type declared through XML must implement the Declarable interface, which allows arbitrary parameters to be passed to the declared class through a Properties instance.

In this section, we describe how you can configure these pluggable components when defined in cache.xml using Spring while keeping your Cache/Region configuration defined in cache.xml. This allows your pluggable components to focus on the application logic and not the location or creation of DataSources or other collaborators.

However, if you are starting a green field project, it is recommended that you configure Cache, Region, and other pluggable GemFire components directly in Spring. This avoids inheriting from the Declarable interface or the base class presented in this section.

See the following sidebar for more information on this approach.

Eliminate Declarable components

A developer can configure custom types entirely through Spring as mentioned in Configuring a Region. That way, a developer does not have to implement the Declarable interface, and also benefits from all the features of the Spring IoC container (not just dependency injection but also life-cycle and instance management).

As an example of configuring a Declarable component using Spring, consider the following declaration (taken from the Declarable Javadoc):

   <parameter name="URL">

To simplify the task of parsing, converting the parameters and initializing the object, Spring Data GemFire offers a base class (WiringDeclarableSupport) that allows GemFire user objects to be wired through a template bean definition or, in case that is missing, perform auto-wiring through the Spring IoC container. To take advantage of this feature, the user objects need to extend WiringDeclarableSupport, which automatically locates the declaring BeanFactory and performs wiring as part of the initialization process.

Why is a base class needed?

In the current GemFire release there is no concept of an object factory and the types declared are instantiated and used as is. In other words, there is no easy way to manage object creation outside Pivotal GemFire.

7.6.1. Configuration using template bean definitions

When used, WiringDeclarableSupport tries to first locate an existing bean definition and use that as the wiring template. Unless specified, the component class name will be used as an implicit bean definition name.

Let’s see how our DBLoader declaration would look in that case:

class DBLoader extends WiringDeclarableSupport implements CacheLoader {

  private DataSource dataSource;

  public void setDataSource(DataSource dataSource){
    this.dataSource = dataSource;

  public Object load(LoaderHelper helper) { ... }
   <!-- no parameter is passed (use the bean's implicit name, which is the class name) -->
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
         http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd

  <bean id="dataSource" ... />

  <!-- template bean definition -->
  <bean id="com.company.app.DBLoader" abstract="true" p:dataSource-ref="dataSource"/>

In the scenario above, as no parameter was specified, a bean with the id/name com.company.app.DBLoader was used as a template for wiring the instance created by GemFire. For cases where the bean name uses a different convention, one can pass in the bean-name parameter in the GemFire configuration:

   <!-- pass the bean definition template name as parameter -->
   <parameter name="bean-name">
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
         http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd

  <bean id="dataSource" ... />

   <!-- template bean definition -->
   <bean id="template-bean" abstract="true" p:dataSource-ref="dataSource"/>

The template bean definitions do not have to be declared in XML. Any format is allowed (Groovy, annotations, etc).

7.6.2. Configuration using auto-wiring and annotations

By default, if no bean definition is found, WiringDeclarableSupport will autowire the declaring instance. This means that unless any dependency injection metadata is offered by the instance, the container will find the object setters and try to automatically satisfy these dependencies. However, a developer can also use JDK 5 annotations to provide additional information to the auto-wiring process.

We strongly recommend reading the dedicated chapter in the Spring documentation for more information on the supported annotations and enabling factors.

For example, the hypothetical DBLoader declaration above can be injected with a Spring-configured DataSource in the following way:

class DBLoader extends WiringDeclarableSupport implements CacheLoader {

  // use annotations to 'mark' the needed dependencies
  private DataSource dataSource;

  public Object load(LoaderHelper helper) { ... }
   <!-- no need to declare any parameters since the class is auto-wired -->
<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
         http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
         http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd

     <!-- enable annotation processing -->


By using the JSR-330 annotations, the CacheLoader code has been simplified since the location and creation of the DataSource has been externalized and the user code is concerned only with the loading process. The DataSource might be transactional, created lazily, shared between multiple objects or retrieved from JNDI. These aspects can easily be configured and changed through the Spring container without touching the DBLoader code.

7.7. Support for the Spring Cache Abstraction

Spring Data GemFire provides an implementation of the Spring Cache Abstraction to position Pivotal GemFire as a caching provider in Spring’s caching infrastructure.

To use Pivotal GemFire as a backing implementation, a "caching provider" in Spring’s Cache Abstraction, simply add GemfireCacheManager to your configuration:

<beans xmlns="http://www.springframework.org/schema/beans"
  xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/gemfire http://www.springframework.org/schema/gemfire/spring-gemfire.xsd
    http://www.springframework.org/schema/cache http://www.springframework.org/schema/cache/spring-cache.xsd">

  <!-- enable declarative caching -->

  <gfe:cache id="gemfire-cache"/>

  <!-- declare GemfireCacheManager; must have a bean ID of 'cacheManager' -->
  <bean id="cacheManager" class="org.springframework.data.gemfire.cache.GemfireCacheManager"

The cache-ref attribute on the CacheManager bean definition is not necessary if the default cache bean name is used (i.e. "gemfireCache"), i.e. <gfe:cache> without an explicit ID.

When the GemfireCacheManager (Singleton) bean instance is declared and declarative caching is enabled (either in XML with <cache:annotation-driven/> or in JavaConfig with Spring’s @EnableCaching annotation), the Spring caching annotations (e.g. @Cacheable) identify the "caches" that will cache data in-memory using GemFire Regions.

These caches (i.e. Regions) must exist before the caching annotations that use them otherwise an error will occur.

By way of example, suppose you have a Customer Service application with a CustomerService application component that performs caching…​

class CustomerService {

@Cacheable(cacheNames="Accounts", key="#customer.id")
Account createAccount(Customer customer) {

Then you will need the following config.


<beans xmlns="http://www.springframework.org/schema/beans"
  xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/gemfire http://www.springframework.org/schema/gemfire/spring-gemfire.xsd
    http://www.springframework.org/schema/cache http://www.springframework.org/schema/cache/spring-cache.xsd">

  <!-- enable declarative caching -->

  <bean id="cacheManager" class="org.springframework.data.gemfire.cache.GemfireCacheManager">


  <gfe:partitioned-region id="accountsRegion" name="Accounts" persistent="true" ...>


class ApplicationConfiguration {

  CacheFactoryBean gemfireCache() {
    return new CacheFactoryBean();

  GemfireCacheManager cacheManager() {
    GemfireCacheManager cacheManager = GemfireCacheManager();
    return cacheManager;

  PartitionedRegionFactoryBean accountsRegion() {
    PartitionedRegionFactoryBean accounts = new PartitionedRegionFactoryBean();


    return accounts;

Of course, you are free to choose whatever Region type you like (e.g. REPLICATE, PARTITION, LOCAL, etc).

For more details on Spring’s Cache Abstraction, again, please refer to the documentation.

8. Working with Pivotal GemFire Serialization

To improve overall performance of the Pivotal GemFire In-memory Data Grid, GemFire supports a dedicated serialization protocol, called PDX, that is both faster and offers more compact results over standard Java serialization in addition to works transparently across various language platforms (Java, C++, .NET). Please refer to PDX Serialization Features and PDX Serialization Internals for more details.

This chapter discusses the various ways in which Spring Data GemFire simplifies and improves GemFire’s custom serialization in Java.

8.1. Wiring deserialized instances

It is fairly common for serialized objects to have transient data. Transient data is often dependent on the system or environment where it lives at a certain point in time. For instance, a DataSource is environment specific. Serializing such information is useless, and potentially even dangerous, since it is local to a certain VM/machine. For such cases, Spring Data GemFire offers a special Instantiator that performs wiring for each new instance created by GemFire during deserialization.

Through such a mechanism, one can rely on the Spring container to inject and manage certain dependencies making it easy to split transient from persistent data and have rich domain objects in a transparent manner.

Spring users might find this approach similar to that of @Configurable). The WiringInstantiator works just like WiringDeclarableSupport, trying to first locate a bean definition as a wiring template and falling back to autowiring otherwise.

Please refer to the previous section (Wiring Declarable Components) for more details on wiring functionality.

To use this SDG Instantiator, simply declare it as a bean:

<bean id="instantiator" class="org.springframework.data.gemfire.serialization.WiringInstantiator">
  <!-- DataSerializable type -->
  <!-- type id -->

During the Spring container startup, once it is being initialized, the Instantiator will, by default, register itself with the GemFire serialization system and perform wiring on all instances of SomeDataSerializableClass created by GemFire during deserialization.

8.2. Auto-generating custom Instantiators

For data intensive applications, a large number of instances might be created on each machine as data flows in. Out-of-the-box, GemFire uses reflection to create new types, but for some scenarios, this might prove to be expensive. As always, it is good to perform profiling to quantify whether this is the case or not. For such cases, Spring Data GemFire allows the automatic generation of Instatiator classes which instantiate a new type (using the default constructor) without the use of reflection:

<bean id="instantiatorFactory" class="org.springframework.data.gemfire.serialization.InstantiatorFactoryBean">
  <property name="customTypes">
      <entry key="org.pkg.CustomTypeA" value="1025"/>
      <entry key="org.pkg.CustomTypeB" value="1026"/>

The definition above, automatically generates two Instantiators for two classes, namely CustomTypeA and CustomTypeB and registers them with GemFire, under user id 1025 and 1026. The two Instantiators avoid the use of reflection and create the instances directly through Java code.

9. POJO mapping

9.1. Entity Mapping

Spring Data GemFire provides support to map entities that will be stored in a Region in the GemFire In-Memory Data Grid. The mapping metadata is defined using annotations on application domain classes just like this:

Example 1. Mapping a domain class to a GemFire Region
public class Person {

  @Id Long id;

  String firstname;
  String lastname;

  public Person(String firstname, String lastname) {
    // …


The first thing you notice here is the @Region annotation that can be used to customize the Region in which an instance of the Person class is stored. The @Id annotation can be used to annotate the property that shall be used as the cache (Region) key, identifying the Region entry. The @PersistenceConstructor annotation helps to disambiguate multiple, potentially available constructors taking parameters and explicitly marking the constructor annotated as the constructor to be used to construct entities. In an application domain class with no or only a single constructor you can omit the annotation.

In addition to storing entities in top-level Regions, entities can be stored in Sub-Regions as well.

For instance:

public class Admin extends User {

public class Guest extends User {

Be sure to use the full-path of the GemFire Region, as defined with the Spring Data GemFire XML namespace using the id or name attributes of the <*-region> element.

9.1.1. Entity Mapping by Region Type

In addition to the @Region annotation, Spring Data GemFire also recognizes the Region type-specific mapping annotations: @ClientRegion, @LocalRegion, @PartitionRegion and @ReplicateRegion.

Functionally, these annotations are treated exactly the same as the generic @Region annotation in the SDG mapping infrastructure. However, these additional mapping annotations are useful in Spring Data GemFire’s` Annotation configuration model. When combined with the @EnableEntityDefinedRegions configuration annotation on _Spring @Configuration annotated class, it is possible to generate Regions in the local cache, whether the application is a client or peer.

These annotations allow you, the developer, to be more specific about what type of Region that your application entity class should be mapped to, and also has an impact on the data management policies of the Region (e.g. partition (a.k.a. sharding) vs. just replicating data).

Using these Region type-specific mapping annotations with the SDG Annotation config model saves you from having to explicitly define these Regions in config.

The details of the new Annotation configuration model will be discussed in more detail in a subsequent releaase.

9.1.2. Repository Mapping

As an alternative to specifying the Region in which the entity will be stored using the @Region annotation on the entity class, you can also specify the @Region annotation on the entity’s Repository. See Spring Data GemFire Repositories for more details.

However, let’s say you want to store a Person in multiple GemFire Regions (e.g. People and Customers), then you can define your corresponding Repository interface extensions like so:

public interface PersonRepository extends GemfireRepository<Person, String> {

public interface CustomerRepository extends GemfireRepository<Person, String> {

Then, using each Repository individually, you can store the entity in multiple GemFire Regions.

class CustomerService {

  CustomerRepository customerRepo;

  PersonRepository personRepo;

  Customer update(Customer customer) {
    return customer;

It is not difficult to imagine wrapping the update service method in a Spring managed transaction, either as a local cache transaction or a global transaction.

9.2. Mapping PDX Serializer

Spring Data GemFire provides a custom PdxSerializer implementation that uses the mapping information to customize entity serialization. Beyond that, it allows customizing the entity instantiation by using the Spring Data EntityInstantiator abstraction. By default the serializer uses a ReflectionEntityInstantiator that will use the persistence constructor of the mapped entity (either the default constructor, a singly declared constructor or an explicitly annotated constructor annotated with the @PersistenceConstructor annotation).

To provide values for constructor parameters it will read fields with name of the constructor parameters from the supplied PdxReader.

Example 2. Using @Value on entity constructor parameters
public class Person {

  public Person(@Value("#root.foo") String firstname, @Value("bean") String lastname) {
    // …

An entity class annotated in this way will have the field foo read from the PdxReader and passed to the constructor parameter value for firstname. The value for lastname will be the Spring bean with the name bean.

10. Spring Data GemFire Repositories

10.1. Introduction

Spring Data GemFire provides support to use the Spring Data Repository abstraction to easily persist entities into GemFire along with execute queries. A general introduction to the Repository programming model is provided here.

10.2. Spring XML Configuration

To bootstrap Spring Data Repositories, you use the <repositories/> element from the Spring Data GemFire Data namespace:

Example 3. Bootstrap Spring Data GemFire Repositories in XML
<beans xmlns="http://www.springframework.org/schema/beans"
         http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
         http://www.springframework.org/schema/data/gemfire http://www.springframework.org/schema/data/gemfire/spring-data-gemfire.xsd>

  <gfe-data:repositories base-package="com.example.acme.repository"/>


This configuration snippet looks for interfaces below the configured base package and creates Repository instances for those interfaces backed by a SimpleGemFireRepository.

You must have your application domain classes correctly mapped to configured Regions or the bootstrap process will fail otherwise.

10.3. Spring Java-based Configuration

Alternatively, many users prefer to use Spring’s Java-based container configuration.

Using this approach, it is a simple matter to bootstrap Spring Data Repositories using the SDG @EnableGemfireRepositories annotation:

Example 4. Bootstrap Spring Data Geode Repositories with @EnableGemfireRepositories
@EnableGemfireRepositories(basePackages = "com.example.acme.repository")
class SpringDataApplication {

Rather than use the basePackages attribute, you may prefer to use the type-safe basePackageClasses attribute instead. The basePackageClasses allows you to specify the package containing all your application Repository classes by specifying just one of your application Repository interface types. Consider creating a special no-op marker class or interface in each package that serves no other purpose than to identify the location of application Repositories referenced by this attribute.

In addition to the basePackage[sClasses] attributes, like Spring’s @ComponentScan annotation, the @EnableGemfireRepositories annotation provides include and exclude filters, based on Spring’s ComponentScan.Filter type. You can use the filterType attribute to filter by different aspects, such as whether an application Repository type is annotated with a particular Annotation or extends a particular class type, and so on. See the FilterType Javadoc for more details.

The @EnableGemfireRepositories annotation also provides the ability to specify the location of named OQL queries, which reside in a Java Properties file, using the namedQueriesLocation attribute. The property name must match the name of a Repository query method and the property value is the OQL query you want executed when the Repository query method is called.

The repositoryImplementationPostfix attribute can be set to an alternate value (defaults to "Impl") if your application requires 1 or more custom Repository implementations. This feature is commonly used to extend the Spring Data Repository infrastructure in order to implement a feature not provided out-of-the-box (OOTB) by the data store (e.g. SDG).

One example of where custom Repository implementations are needed with Pivotal GemFire is when performing Joins. Joins are not supported by SDG Repositories OOTB. With a Pivotal GemFire PARTITION Region, the Join must be performed on collocated PARTITION Regions even, since Pivotal GemFire does not support "distributed" Joins. In addition, the Equi-Join OQL Query must be performed inside a GemFire Function. See here for more details on Pivotal GemFire Equi-Join Queries.

Many other aspects of the SDG’s Repository infrastructure extension maybe customized as well. See the @EnableGemfireRepositories Javadoc for more details on all configuration settings.

10.4. Executing OQL Queries

Spring Data GemFire Repositories enable the definition of query methods to easily execute GemFire OQL Queries against the Region the managed entity is mapped to.

Example 5. Sample Repository
public class Person { … }
public interface PersonRepository extends CrudRepository<Person, Long> {

  Person findByEmailAddress(String emailAddress);

  Collection<Person> findByFirstname(String firstname);

  @Query("SELECT * FROM /People p WHERE p.firstname = $1")
  Collection<Person> findByFirstnameAnnotated(String firstname);

  @Query("SELECT * FROM /People p WHERE p.firstname IN SET $1")
  Collection<Person> findByFirstnamesAnnotated(Collection<String> firstnames);

The first query method listed here will cause the following OQL query to be derived: SELECT x FROM /People x WHERE x.emailAddress = $1. The second query method works the same way except it’s returning all entities found whereas the first query method expects a single result to be found.

In case the supported keywords are not sufficient to expresss and declare your OQL query, or the method name becomes too verbose, you can annotate the query methods with @Query as seen for methods 3 and 4.

Table 4. Supported keywords for query methods
Keyword Sample Logical result


findByAgeGreaterThan(int age)

x.age > $1


findByAgeGreaterThanEqual(int age)

x.age >= $1


findByAgeLessThan(int age)

x.age < $1


findByAgeLessThanEqual(int age)

x.age ⇐ $1

IsNotNull, NotNull


x.firstname =! NULL

IsNull, Null


x.firstname = NULL


findByFirstnameIn(Collection<String> x)

x.firstname IN SET $1


findByFirstnameNotIn(Collection<String> x)

x.firstname NOT IN SET $1


findByFirstnameIgnoreCase(String firstName)


(No keyword)

findByFirstname(String name)

x.firstname = $1


findByFirstnameLike(String name)

x.firstname LIKE $1


findByFirstnameNot(String name)

x.firstname != $1

IsTrue, True


x.active = true

IsFalse, False


x.active = false

10.5. OQL Query Extensions using Annotations

Many query languages, such as Pivotal GemFire’s OQL (Object Query Language), have extensions that are not directly supported by Spring Data Commons' Repository infrastructure.

One of Spring Data Commons' Repository infrastructure goals is to function as the lowest common denominator in order to maintain support for and portability across the widest array of data stores available and in use for application development today. Technically, this means developers can access multiple different data stores supported by Spring Data Commons within their applications by reusing their existing application-specific Repository interfaces, a very convenient and powerful abstraction.

To support GemFire’s OQL Query language extensions and preserve portability across different data stores, Spring Data GemFire adds support for OQL Query extensions using Java Annotations. These Annotations will be ignored by other Spring Data Repository implementations (e.g. Spring Data JPA or Spring Data Redis) that do not have similar query language extensions.

For instance, many data stores will most likely not implement GemFire’s OQL IMPORT keyword. By implementing IMPORT as an Annotation (i.e. @Import) rather than as part of the query method signature (specifically, the method 'name'), then this will not interfere with the parsing infrastructure when evaluating the query method name to construct another data store language appropriate query.

Currently, the set of GemFire OQL Query language extensions that are supported by Spring Data GemFire include:

Table 5. Supported GemFire OQL extensions for Repository query methods
Keyword Annotation Description Arguments



OQL Query Index Hints

String[] (e.g. @Hint({ "IdIdx", "TxDateIdx" }))



Qualify application-specific types.

String (e.g. @Import("org.example.app.domain.Type"))



Limit the returned query result set.

Integer (e.g. @Limit(10); default is Integer.MAX_VALUE)



Enable OQL Query specific debugging.


As an example, suppose you have a Customers application domain class and corresponding GemFire Region along with a CustomerRepository and a query method to lookup Customers by last name, like so…​

Example 6. Sample Customers Repository
package ...;

import org.springframework.data.annotation.Id;
import org.springframework.data.gemfire.mapping.annotation.Region;

public class Customer ... {

  private Long id;

package ...;

import org.springframework.data.gemfire.repository.GemfireRepository;

public interface CustomerRepository extends GemfireRepository<Customer, Long> {

  List<Customer> findByLastName(String lastName);


This will result in the following OQL Query:

<TRACE> <HINT 'LastNameIdx'> IMPORT org.example.app.domain.Customer; SELECT * FROM /Customers x WHERE x.lastName = $1 LIMIT 10

Spring Data GemFire’s Repository extension and support is careful not to create conflicting declarations when the OQL Annotation extensions are used in combination with the @Query annotation.

As another example, suppose you have a raw @Query annotated query method defined in your CustomerRepository like so…​

Example 7. CustomerRepository
public interface CustomerRepository extends GemfireRepository<Customer, Long> {

  @Query("<TRACE> <HINT 'ReputationIdx'> SELECT DISTINCT * FROM /Customers c WHERE c.reputation > $1 ORDER BY c.reputation DESC LIMIT 5")
  List<Customer> findDistinctCustomersByReputationGreaterThanOrderByReputationDesc(Integer reputation);

This query method results in the following OQL Query:

IMPORT org.example.app.domain.Customer; <TRACE> <HINT 'ReputationIdx'> SELECT DISTINCT * FROM /Customers x WHERE x.reputation > $1 ORDER BY c.reputation DESC LIMIT 5

As you can see, the @Limit(10) annotation will not override the LIMIT defined explicitly in the raw query. As well, @Hint("CustomerIdx") annotation does not override the HINT explicitly defined in the raw query. Finally, the @Trace annotation is redundant and has no additional effect.

The "ReputationIdx" Index is probably not the most sensible index given the number of Customers who will possibly have the same value for their reputation, which will effectively reduce the effectiveness of the index. Please choose indexes and other optimizations wisely as an improper or poorly choosen index can have the opposite effect on your performance given the overhead in maintaining the index. The "ReputationIdx" was only used to serve the purpose of the example.

11. Annotation Support for Function Execution

11.1. Introduction

Spring Data GemFire includes annotation support to simplify working with GemFire Function Execution. Under-the-hood, the Pivotal GemFire API provides classes to implement and register GemFire Functions that are deployed on GemFire servers, which may then be invoked by other peer member applications or remotely from cache clients.

Functions can execute in parallel, distributed among multiple GemFire servers in the cluster, aggregating results with the map-reduce pattern that are sent back to the caller. Functions can also be targeted to run on a single server or Region. The Pivotal GemFire API supports remote execution of Functions targeted using various predefined scopes: on Region, on members [in groups], on servers, etc. The implementation and execution of remote Functions, as with any RPC protocol, requires some boilerplate code.

Spring Data GemFire, true to Spring’s core value proposition, aims to hide the mechanics of remote Function execution and allow developers to focus on core POJO programming and business logic. To this end, Spring Data GemFire introduces annotations to declaratively register public methods of a POJO class as GemFire Functions along with the ability to invoke registered Functions [remotely] via annotated interfaces.

11.2. Implementation vs Execution

There are two separate concerns to address implementation and execution.

First is Function implementation (server-side), which must interact with the FunctionContext to access the invocation arguments, ResultsSender as well as other execution context information. The Function implementation typically accesses the Cache and/or Regions and is registered with the FunctionService under a unique Id.

A cache client application invoking a Function does not depend on the implementation. To invoke a Function, the application instantiates an Execution providing the Function ID, invocation arguments and the Function target, which defines its scope: Region, server, servers, member or members. If the Function produces a result, the invoker uses a ResultCollector to aggregate and acquire the execution results. In certain cases, a custom ResultCollector implementation is required and may be registered with the Execution.

'Client' and 'Server' are used here in the context of Function execution, which may have a different meaning than client and server in GemFire’s client-server topology. While it is common for an application using a ClientCache to invoke a Function on one or more GemFire servers in a cluster, it is also possible to execute Functions in a peer-to-peer (P2P) configuration, where the application is a member of the cluster hosting a peer Cache. Keep in mind that a peer member cache application is subject to all the same constraints of being a peer member of the cluster.

11.3. Implementing a Function

Using GemFire APIs, the FunctionContext provides a runtime invocation context that includes the client’s calling arguments and a ResultSender implementation to send results back to the client. Additionally, if the Function is executed on a Region, the FunctionContext is actually an instance of RegionFunctionContext, which provides additional information such as the target Region on which the Function was invoked and any Filter (set of specific keys) associated with the Execution, etc. If the Region is a PARTITION Region, the Function should use the PartitionRegionHelper to extract only the local data.

Using Spring, a developer can write a simple POJO and use the Spring container to bind one or more of it’s public methods to a Function. The signature for a POJO method intended to be used as a Function must generally conform to the client’s execution arguments. However, in the case of a Region execution, the Region data may also be provided (presumably the data held in the local partition if the Region is a PARTITION Region). Additionally, the Function may require the Filter that was applied, if any. This suggests that the client and server share a contract for the calling arguments but that the method signature may include additional parameters to pass values provided by the FunctionContext. One possibility is for the client and server to share a common interface, but this is not strictly required. The only constraint is that the method signature includes the same sequence of calling arguments with which the Function was invoked after the additional parameters are resolved.

For example, suppose the client provides a String and int as the calling arguments. These are provided in the FunctionContext as an array:

Object[] args = new Object[] { "test", 123 };

Then, the Spring container should be able to bind to any method signature similar to the following. Let’s ignore the return type for the moment:

public Object method1(String s1, int i2) {...}
public Object method2(Map<?, ?> data, String s1, int i2) {...}
public Object method3(String s1, Map<?, ?> data, int i2) {...}
public Object method4(String s1, Map<?, ?> data, Set<?> filter, int i2) {...}
public void method4(String s1, Set<?> filter, int i2, Region<?,?> data) {...}
public void method5(String s1, ResultSender rs, int i2);
public void method6(FunctionContest context);

The general rule is that once any additional arguments, i.e. Region data and Filter, are resolved, the remaining arguments must correspond exactly, in order and type, to the expected Function method parameters. The method’s return type must be void or a type that may be serialized (either as a java.io.Serializable, DataSerializable or PdxSerializable). The latter is also a requirement for the calling arguments. The Region data should normally be defined as a Map, to facilitate unit testing, but may also be of type Region if necessary. As shown in the example above, it is also valid to pass the FunctionContext itself, or the ResultSender, if you need to control how the results are returned to the client.

11.3.1. Annotations for Function Implementation

The following example illustrates how SDG’s Function annotations are used to expose POJO methods as GemFire Functions:

public class ApplicationFunctions {

   public String function1(String value, @RegionData Map<?, ?> data, int i2) { ... }

   @GemfireFunction("myFunction", batchSize=100, HA=true, optimizedForWrite=true)
   public List<String> function2(String value, @RegionData Map<?, ?> data, int i2, @Filter Set<?> keys) { ... }

   public void functionWithContext(FunctionContext functionContext) { ... }


Note, the class itself must be registered as a Spring bean and each GemFire Function is annotated with @GemfireFunction. In this example, Spring’s @Component annotation was used, but you may register the bean by any method supported by Spring (e.g. XML configuration or with a Java configuration class using Spring Boot). This allows the Spring container to create an instance of this class and wrap it in a PojoFunctionWrapper. Spring creates a wrapper instance for each method annotated with @GemfireFunction. Each wrapper instance shares the same target object instance to invoke the corresponding method.

The fact that the POJO Function class is a Spring bean may offer other benefits since it shares the ApplicationContext with GemFire components such as the Cache and Regions. These may be injected into the class if necessary.

Spring creates the wrapper class and registers the Function(s) with GemFire’s Function Service. The Function id used to register the Functions must be unique. Using convention it defaults to the simple (unqualified) method name. The name can be explicitly defined using the id attribute of the @GemfireFunction annotation. The @GemfireFunction annotation also provides other configuration attributes, HA and optimizedForWrite, which correspond to properties defined by GemFire’s Function interface. If the method’s return type is void, then the hasResult property is automatically set to false; otherwise, if the method returns a value the hasResult attributes is set to true.

Even for void return types, the annotation’s hasResult attribute can be set to true to override this convention, as shown in the functionWithContext method above. Presumably, the intention is to use the ResultSender directly to send results to the caller.

The PojoFunctionWrapper implements GemFire’s Function interface, binds method parameters and invokes the target method in its execute() method. It also sends the method’s return value using the ResultSender.

11.3.2. Batching Results

If the return type is an array or Collection, then some consideration must be given to how the results are returned. By default, the PojoFunctionWrapper returns the entire array or Collection at once. If the number of elements in the array or Collection quite is large, it may incur a performance penalty. To divide the payload into smaller, more maneable chunks, you can set the batchSize attribute, as illustrated in function2, above.

If you need more control of the ResultSender, especially if the method itself would use too much memory to create the Collection, you can pass the ResultSender, or access it via the FunctionContext and use it directly within the method to sends results back to the caller.

11.3.3. Enabling Annotation Processing

In accordance with Spring standards, you must explicitly activate annotation processing for @GemfireFunction annotations.

Using XML:


Or by annotating a Java configuration class:

class ApplicationConfiguration { .. }

11.4. Executing a Function

A process invoking a remote Function needs to provide the Function’s ID, calling arguments, the execution target (onRegion, onServers, onServer, onMember, onMembers) and optionally, a Filter set. Using Spring Data GemFire, all a developer need do is define an interface supported by annotations. Spring will create a dynamic proxy for the interface, which will use the FunctionService to create an Execution, invoke the Execution and coerce the results to the defined return type, if necessary. This technique is very similar to the way Spring Data GemFire’s Repository extension works, thus some of the configuration and concepts should be familiar. Generally, a single interface definition maps to multiple Function executions, one corresponding to each method defined in the interface.

11.4.1. Annotations for Function Execution

To support client-side Function execution, the following SDG Function annotations are provided: @OnRegion, @OnServer, @OnServers, @OnMember, @OnMembers. These annotations correspond to the Execution implementations prodided by GemFire’s FunctionService. Each annotation exposes the appropriate attributes. These annotations also provide an optional resultCollector attribute whose value is the name of a Spring bean implementing the ResultCollector to use for the execution.

The proxy interface binds all declared methods to the same execution configuration. Although, it is expected that single method interfaces will be common, all methods in the interface are backed by the same proxy instance and therefore all share the same configuration.

Here are a few examples:

@OnRegion(region="SomeRegion", resultCollector="myCollector")
public interface FunctionExecution {

    String doIt(String s1, int i2);

    String getString(Object arg1, @Filter Set<Object> keys);


By default, the Function ID is the simple (unqualified) method name. The @FunctionId annotation can be used to bind this invocation to a different Function ID.

11.4.2. Enabling Annotation Processing

The client-side uses Spring’s classpath component scanning capability to discover annotated interfaces. To enable Function execution annotation processing in XML:

<gfe-data:function-executions base-package="org.example.myapp.gemfire.functions"/>

The function-executions element is provided in the gfe-data namespace. The base-package attribute is required to avoid scanning the entire classpath. Additional filters are provided as described in the Spring reference documentation.

Optionally, a developer can annotate her Java configuration class:

@EnableGemfireFunctionExecutions(basePackages = "org.example.myapp.gemfire.functions")

11.5. Programmatic Function Execution

Using the Function execution annotated interface defined in the previous section, simply auto-wire your interface into an application bean that will invoke the Function:

public class MyApplication {

    FunctionExecution functionExecution;

    public void doSomething() {
         functionExecution.doIt("hello", 123);

Alternately, you can use a Function execution template directly. For example, GemfireOnRegionFunctionTemplate creates an onRegion Function Execution.

Example 8. Using the GemfireOnRegionFunctionTemplate
Set<?, ?> myFilter = getFilter();
Region<?, ?> myRegion = getRegion();
GemfireOnRegionOperations template = new GemfireOnRegionFunctionTemplate(myRegion);
String result = template.executeAndExtract("someFunction", myFilter, "hello", "world", 1234);

Internally, Function Executions always return a List. executeAndExtract assumes a singleton List containing the result and will attempt to coerce that value into the requested type. There is also an execute method that returns the List as is. The first parameter is the Function ID. The Filter argument is optional. The following arguments are a variable argument List.

11.6. Function Execution with PDX

When using Spring Data GemFire’s Function annotation support combined with Pivotal GemFire’s PDX Serialization, there are a few logistical things to keep in mind.

As explained above, and by way of example, typically developers will define GemFire Functions using POJO classes annotated with Spring Data GemFire Function annotations like so…​

public class OrderFunctions {

  Order process(@RegionData data, Order order, OrderSource orderSourceEnum, Integer count) { ... }

The Integer type, count parameter is arbitrary as is the separation of the Order class and OrderSource Enum, which might be logical to combine. However, the arguments were setup this way to demonstrate the problem with Function executions in the context of PDX.

Your Order and OrderSource enum might be as follows…​

public class Order ... {

  private Long orderNumber;
  private Calendar orderDateTime;
  private Customer customer;
  private List<Item> items


public enum OrderSource {

Of course, a developer may define a Function Execution interface to call the 'process' GemFire Server Function…​

public interface OrderProcessingFunctions {
  Order process(Order order, OrderSource orderSourceEnum, Integer count);

Clearly, this process(..) Order Function is being called from a client-side with a ClientCache (i.e. <gfe:client-cache/>) based application. This implies that the Function arguments must also be serializable. The same is true when invoking peer-to-peer member Functions (e.g. @OnMember(s)) between peers in the cluster. Any form of `distribution requires the data transmitted between client and server, or peers, to be serialized.

Now, if the developer has configured GemFire to use PDX for serialization (instead of Java serialization, for instance) it is common for developers to also set the pdx-read-serialized attribute to true in their configuration of the GemFire server(s)…​

<gfe:cache ... pdx-read-serialized="true"/>

Or from a GemFire cache client application…​

<gfe:client-cache ... pdx-read-serialized="true"/>

This causes all values read from the cache (i.e. Regions) as well as information passed between client and servers, or peers, to remain in serialized form, including, but not limited to, Function arguments.

GemFire will only serialize application domain object types that you have specifically configured (registered), with either GemFire’s ReflectionBasedAutoSerializer, or specifically (and recommended) using a "custom" GemFire PdxSerializer. If you are using Spring Data GemFire’s Repository extension to Spring Data Common’s Repository abstraction and infrastructure, you might even want to consider using Spring Data GemFire’s MappingPdxSerializer, which uses a entity’s mapping meta-data to determine data from the application domain object that will be serialized to the PDX instance.

What is less than apparent, though, is that GemFire automatically handles Java Enum types regardless of whether they are explicitly configured or not (i.e. registered with a ReflectionBasedAutoSerializer using a regex pattern and the classes parameter, or are handled by a "custom" GemFire PdxSerializer), despite the fact that Java Enums implement java.io.Serializable.

So, when a developer sets pdx-read-serialized to true on GemFire Servers where the GemFire Functions (including Spring Data GemFire Function annotated POJO classes) are registered, then the developer may encounter surprising behavior when invoking the Function Execution.

What the developer may pass as arguments when invoking the Function is…​

orderProcessingFunctions.process(new Order(123, customer, Calendar.getInstance(), items), OrderSource.ONLINE, 400);

But, what the GemFire Function on the Server gets is…​

process(regionData, order:PdxInstance, :PdxInstanceEnum, 400);

The Order and OrderSource have been passed to the Function as PDX instances. Again, this is all because pdx-read-serialized is set to true, which may be necessary in cases where the GemFire Servers are interacting with multiple different clients (e.g. Java, native clients, such as C++/C#, etc).

This flies in the face of Spring Data GemFire’s "strongly-typed", Function annotated POJO class method signatures, as the developer is expecting application domain object types, not PDX serialized instances.

So, Spring Data GemFire includes enhanced Function support to automatically convert method arguments passed to the Function that are of type PDX to the desired application domain object types defined by the Function method’s parameter types.

However, this also requires the developer to explicitly register a GemFire PdxSerializer on the GemFire Servers where Spring Data GemFire Function annotated POJOs are registered and used, e.g. …​

<bean id="customPdxSerializer" class="x.y.z.gemfire.serialization.pdx.MyCustomPdxSerializer"/>

<gfe:cache ... pdx-serializer-ref="customPdxSerializeer" pdx-read-serialized="true"/>

Alternatively, a developer my use GemFire’s ReflectionBasedAutoSerializer for convenience. Of course, it is recommended that you use a "custom" PdxSerializer where possible to maintain finer grained control over your serialization strategy.

Finally, Spring Data GemFire is careful not to convert your Function arguments if you treat your Function arguments generically, or as one of GemFire’s PDX types…​

public Object genericFunction(String value, Object domainObject, PdxInstanceEnum enum) {

Spring Data GemFire only converts PDX type data to the corresponding application domain types if and only if the corresponding application domain types are on the classpath the the Function annotated POJO method expects it.

For a good example of "custom", "composed" application-specific GemFire PdxSerializers as well as appropriate POJO Function parameter type handling based on the method signatures, see Spring Data GemFire’s ClientCacheFunctionExecutionWithPdxIntegrationTest class.

12. Apache Lucene Integration

Pivotal GemFire integrates with Apache Lucene to allow developers to index and search on data stored in Pivotal GemFire using Lucene queries. Search-based queries also includes the capability to page through query results.

Additionally, Spring Data GemFire adds support for query projections based on Spring Data Commons Projection infrastructure. This feature enables the query results to be projected into first-class, application domain types as needed or required by the application use case.

However, a Lucene Index must be created first before any Lucene search-based query can be ran. A LuceneIndex can be created in Spring (Data GemFire) XML config like so…​

<gfe:lucene-index id="IndexOne" fields="fieldOne, fieldTwo" region-path="/Example"/>

Additionally, Apache Lucene allows the specification of Analyzers per field and can be configured using…​

<gfe:lucene-index id="IndexTwo" lucene-service-ref="luceneService" region-path="/AnotherExample">
            <entry key="fieldOne">
                <bean class="example.AnalyzerOne"/>
            <entry key="fieldTwo">
                <bean class="example.AnalyzerTwo"/>

Of course, the Map can be specified as a top-level bean definition and referenced using the ref attribute on the nested <gfe:field-analyzers> element like this, <gfe-field-analyzers ref="refToTopLevelMapBeanDefinition"/>.

Alternatively, a LuceneIndex can be declared in Spring Java config, inside a @Configuration class with…​

@Bean(name = "People")
PartitionedRegionFactoryBean<Long, Person> peopleRegion(GemFireCache gemfireCache) {
    PartitionedRegionFactoryBean<Long, Person> peopleRegion = new PartitionedRegionFactoryBean<>();


    return peopleRegion;

LuceneIndexFactoryBean personTitleIndex(GemFireCache gemFireCache) {
    LuceneIndexFactoryBean luceneIndex = new LuceneIndexFactoryBean();


    return luceneIndex;

There are a few limitations of Pivotal GemFire’s, Apache Lucene integration support. First, a LuceneIndex can only be created on a GemFire PARTITION Region. Second, all LuceneIndexes must be created before the the Region on which the LuceneIndex is applied.

It is possible that these Pivotal GemFire restrictions will not apply in a future release which is why the SDG LuceneIndexFactoryBean API takes a reference to the Region directly as well, rather than just the Region path.

This is more ideal if think about the case in which users may want to define a LuceneIndex on an existing Region with data at a later point during the application’s lifecycle and as requirements demand. Where possible, SDG strives to stick to strongly-typed objects.

Now that we have a LuceneIndex we can perform Lucene based data access operations, such as queries.

12.1. Lucene Template Data Accessors

Spring Data GemFire provides 2 primary templates for Lucene data access operations, depending on how low a level your application is prepared to deal with.

The LuceneOperations interface defines query operations using Pivotal GemFire Lucene types.

public interface LuceneOperations {

    <K, V> List<LuceneResultStruct<K, V>> query(String query, String defaultField [, int resultLimit]
        , String... projectionFields);

    <K, V> PageableLuceneQueryResults<K, V> query(String query, String defaultField,
        int resultLimit, int pageSize, String... projectionFields);

    <K, V> List<LuceneResultStruct<K, V>> query(LuceneQueryProvider queryProvider [, int resultLimit]
        , String... projectionFields);

    <K, V> PageableLuceneQueryResults<K, V> query(LuceneQueryProvider queryProvider,
        int resultLimit, int pageSize, String... projectionFields);

    <K> Collection<K> queryForKeys(String query, String defaultField [, int resultLimit]);

    <K> Collection<K> queryForKeys(LuceneQueryProvider queryProvider [, int resultLimit]);

    <V> Collection<V> queryForValues(String query, String defaultField [, int resultLimit]);

    <V> Collection<V> queryForValues(LuceneQueryProvider queryProvider [, int resultLimit]);
The [, int resultLimit] indicates that the resultLimit parameter is optional.

The operations in the LuceneOperations interface match the operations provided by the Pivotal GemFire’s LuceneQuery interface. However, SDG has the added value of translating proprietary GemFire or Lucene Exceptions into Spring’s highly consistent and expressive DAO Exception Hierarchy, particularly as many modern data access operations involve more than single store or repository.

Additionally, SDG’s LuceneOperations interface can shield your application from interface breaking changes introduced by the underlying Pivotal GemFire or Apache Lucene APIs when they do and will occur.

However, it would be remorse to only offer a Lucene Data Access Object that only uses Pivotal GemFire and Apache Lucene data types (e.g. GemFire’s LuceneResultStruct), therefore SDG gives you the ProjectingLuceneOperations interface to remedy these important application concerns.

public interface ProjectingLuceneOperations {

    <T> List<T> query(String query, String defaultField [, int resultLimit], Class<T> projectionType);

    <T> Page<T> query(String query, String defaultField, int resultLimit, int pageSize, Class<T> projectionType);

    <T> List<T> query(LuceneQueryProvider queryProvider [, int resultLimit], Class<T> projectionType);

    <T> Page<T> query(LuceneQueryProvider queryProvider, int resultLimit, int pageSize, Class<T> projectionType);

The ProjectingLuceneOperations interface primarily uses application domain object types to work with your application data. The query method variants accept a projection type and the template applies the query results to instances of the given projection type using the Spring Data Commons Projection infrastructure.

Additionally, the template wraps the paged Lucene query results in an instance of the Spring Data Commons abstraction representing a Page. The same projection logic can still be applied to the results in the page and are lazily projected as each page in the collection is accessed.

By way of example, suppose I have a class representing a Person like so…​

class Person {

    Gender gender;

    LocalDate birthDate;

    String firstName;
    String lastName;


    String getName() {
        return String.format("%1$s %2$s", getFirstName(), getLastName());

Additionally, I might have a single interface to represent people as Customers depending on my application view…​

interface Customer {

    String getName()

If I define the following LuceneIndex…​

LuceneIndexFactoryBean personLastNameIndex(GemFireCache gemfireCache) {
    LuceneIndexFactoryBean personLastNameIndex = new LuceneIndexFactoryBean();


    return personLastNameIndex;

Then it is a simple matter to query for people as either Person objects…​

List<Person> people = luceneTemplate.query("lastName: D*", "lastName", Person.class);

Or as a Page of type Customer…​

Page<Customer> customers = luceneTemplate.query("lastName: D*", "lastName", 100, 20, Customer.class);

The Page can then be used to fetch individual pages of results…​

List<Customer> firstPage = customers.getContent();

Conveniently, the Spring Data Commons Page interface implements java.lang.Iterable<T> too making it very easy to iterate over the content as well.

The only restriction to the Spring Data Commons Projection infrastructure is that the projection type must be an interface. However, it is possible to extend the provided, out-of-the-box (OOTB) SDC Projection infrastructure and provide a custom ProjectionFactory that uses CGLIB to generate proxy classes as the projected entity.

A custom ProjectionFactory can be set on a Lucene template using setProjectionFactory(:ProjectionFactory).

12.2. Annotation configuration support

Finally, Spring Data GemFire provides Annotation configuration support for LuceneIndexes. Eventually, the SDG Lucene support will find its way into the Repository infrastructure extension for Pivotal GemFire so that Lucene queries can be expressed as methods on an application Repository interface, much like the OQL support today.

However, in the meantime, if you want to conveniently express LuceneIndexes, you can do so directly on your application domain objects like so…​

class Person {

    Gender gender;

    LocalDate birthDate;

    String firstName;

    String lastName;


You must be using the SDG Annotation configuration support along with the @EnableEntityDefineRegions and @EnableIndexing Annotations to enable this feature…​

class ApplicationConfiguration {


Given our definition of the Person class above, the SDG Annotation configuration support will find the Person entity class definition, determine that people will be stored in a PARTITION Region called "People" and that the Person will have an OQL Index on birthDate along with a LuceneIndex on lastName.

More will be described with this feature in subsequent releases.

13. Bootstrapping a Spring ApplicationContext in Pivotal GemFire

13.1. Introduction

Normally, a Spring-based application will bootstrap Pivotal GemFire using Spring Data GemFire’s. Just by specifying a <gfe:cache/> element using the _Spring Data GemFire XML namespace, a single, embedded GemFire peer Cache instance is created and initialized with default settings in the same JVM process as your application.

However, it is sometimes necessary, perhaps a requirement imposed by your IT organization, that GemFire be fully managed and operated using the provided Pivotal GemFire tool suite, such as with Gfsh. By using Gfsh, GemFire will bootstrap your Spring application context rather than the other way around. Instead of an application server, or a Java main class using Spring Boot, whatever, GemFire does the bootstrapping and will host your application.

Keep in mind, however, that GemFire is not an application server. In addition, there are limitations to using this approach where GemFire cache configuration is concerned.

13.2. Using Pivotal GemFire to Bootstrap a Spring Context Started with Gfsh

In order to bootstrap a Spring application context in GemFire when starting a GemFire Server process using Gfsh, a user must make use of GemFire’s Initalizer functionality. An Initializer block can declare a callback application that is launched after the cache is initialized by GemFire.

An Initializer is declared within an initializer element using a minimal snippet of GemFire’s native cache.xml. The cache.xml file is required in order to bootstrap the Spring application context, much like a minimal snippet of Spring XML config is needed to bootstrap a Spring application context configured with component scanning (e.g. <context:component-scan base-packages="…​"/>)

Fortunately, such an Initializer is already conveniently provided by the framework, the SpringContextBootstrappingInitializer. A typical, yet very minimal configuration for this class inside GemFires’s cache.xml file will look like this:

<?xml version="1.0" encoding="UTF-8"?>
<cache xmlns="http://geode.apache.org/schema/cache"
       xsi:schemaLocation="http://geode.apache.org/schema/cache http://geode.apache.org/schema/cache/cache-1.0.xsd"

    <parameter name="contextConfigLocations">


The SpringContextBootstrappingInitializer class follows similar conventions as Spring’s ContextLoaderListener class used to bootstrap a Spring application context inside a Web Application, where application context configuration files are specified with the contextConfigLocations Servlet Context Parameter.

In addition, the SpringContextBootstrappingInitializer class can also be used with a basePackages parameter to specify a comma-separated list of base packages containing appropriately annotated application components that the Spring container will search in order to find and create Spring beans and other application components on the classpath:

<?xml version="1.0" encoding="UTF-8"?>
<cache xmlns="http://geode.apache.org/schema/cache"
       xsi:schemaLocation="http://geode.apache.org/schema/cache http://geode.apache.org/schema/cache/cache-1.0.xsd"

    <parameter name="basePackages">


Then, with a properly configured and constructed CLASSPATH along with cache.xml file shown above, specified as a command-line option when starting a GemFire Server in Gfsh, the command-line would be:

gfsh>start server --name=Server1 --log-level=config ...

The application-context.xml can be any valid Spring context configuration meta-data including all the SDG namespace elements. The only limitation with this approach is that a GemFire cache cannot be configured using the Spring Data GemFire namespace. In other words, none of the <gfe:cache/> element attributes, such as cache-xml-location, properties-ref, critical-heap-percentage, pdx-serializer-ref, lock-lease, etc, can be specified. If used, these attributes will be ignored.

The reason for this is that GemFire itself has already created an initialized the cache before the Initializer gets invoked. As such, the cache will already exist and since it is a "Singleton", it cannot be re-initialized or have any of it’s configuration augmented.

13.3. Lazy-Wiring GemFire Components

Spring Data GemFire already provides existing support for wiring GemFire components, such as CacheListeners, CacheLoaders, CacheWriters and so on, that are declared and created by GemFire in cache.xml using SDG’s WiringDeclarableSupport class as described in Configuration using auto-wiring and annotations. However, this only works when Spring is the one doing the bootstrapping (i.e. bootstrapping GemFire).

When your Spring application context is bootstrapped by GemFire, then these GemFire application components go unnoticed since the Spring application context does not even exist yet! The Spring application context will not get created until GemFire calls the Initializer block, which only occurs after all the other GemFire components and configuration have already been created and initialized.

So, in order to solve this problem, a new LazyWiringDeclarableSupport class was introduced that is, in a sense, Spring application context aware. The intention of this abstract base class is that any implementing class will register itself to be configured by the Spring container that will eventually be created by GemFire once the Initializer is called. In essence, this give your GemFire defined application components a chance to be configured and auto-wired with Spring beans defined in the Spring application context.

In order for your GemFire application components to be auto-wired by the Spring container, create an application class that extends the LazyWiringDeclarableSupport and annotate any class member that needs to be provided as a Spring bean dependency, similar to:

public class UserDataSourceCacheLoader extends LazyWiringDeclarableSupport
    implements CacheLoader<String, User> {

  private DataSource userDataSource;


As implied in the CacheLoader example above, you might necessarily (although, rarely) have defined both a Region and CacheListener component in GemFire cache.xml. The CacheLoader may need access to an application DAO, or perhaps a Spring application context defined JDBC DataSource for loading Users into a GemFire REPLICATE Region on start.

Be careful when mixing the different life-cycles of Pivotal GemFire and the Spring Container together in this manner as not all use cases and scenarios are supported. The GemFire cache.xml configuration would be similar to the following (which comes from SDG’s test suite):
<?xml version="1.0" encoding="UTF-8"?>
<cache xmlns="http://geode.apache.org/schema/cache"
       xsi:schemaLocation="http://geode.apache.org/schema/cache http://geode.apache.org/schema/cache/cache-1.0.xsd"

  <region name="Users" refid="REPLICATE">
    <region-attributes initial-capacity="101" load-factor="0.85">

    <parameter name="basePackages">


14. Sample Applications

Sample applications are now maintained in the Spring GemFire Examples repository.

The Spring Data GemFire project also includes one sample application. Named "Hello World", the sample application demonstrates how to configure and use Pivotal GemFire inside a Spring application. At runtime, the sample offers a shell to the user allowing her to run various commands against the data grid. It provides an excellent starting point for users unfamiliar with the essential components or with Spring and GemFire concepts.

The sample is bundled with the distribution and is Maven-based. A developer can easily import them into any Maven-aware IDE (such as Spring Tool Suite) or run them from the command-line.

14.1. Hello World

The Hello World sample application demonstrates the core functionality of the Spring Data GemFire project. It bootstraps GemFire, configures it, executes arbitrary commands against the cache and shuts it down when the application exits. Multiple instances of the application can be started at the same time and they will work together, sharing data without any user intervention.

Running under Linux
If you experience networking problems when starting GemFire or the samples, try adding the following system property java.net.preferIPv4Stack=true to the command line (e.g. -Djava.net.preferIPv4Stack=true). For an alternative (global) fix especially on Ubuntu see SGF-28.

14.1.1. Starting and stopping the sample

Hello World is designed as a stand-alone Java application. It features a main class which can be started either from your IDE of choice (in Eclipse/STS through Run As/Java Application) or from the command-line through Maven using mvn exec:java. A developer can also use java directly on the resulting artifact if the classpath is properly set.

To stop the sample, simply type exit at the command-line or press Ctrl+C to stop the JVM and shutdown the Spring container.

14.1.2. Using the sample

Once started, the sample will create a shared data grid and allow the user to issue commands against it. The output will likely look as follows:

INFO: Created GemFire Cache [Spring GemFire World] v. X.Y.Z
INFO: Created new cache region [myWorld]
INFO: Member xxxxxx:50694/51611 connecting to region [myWorld]
Hello World!
Want to interact with the world ? ...
Supported commands are:

get <key> - retrieves an entry (by key) from the grid
put <key> <value> - puts a new entry into the grid
remove <key> - removes an entry (by key) from the grid

For example to add new items to the grid one can use:

-> Bold Section qName:emphasis level:5, chunks:[put 1 unu] attrs:[role:bold]
INFO: Added [1=unu] to the cache
-> Bold Section qName:emphasis level:5, chunks:[put 1 one] attrs:[role:bold]
INFO: Updated [1] from [unu] to [one]
-> Bold Section qName:emphasis level:5, chunks:[size] attrs:[role:bold]
-> Bold Section qName:emphasis level:5, chunks:[put 2 two] attrs:[role:bold]
INFO: Added [2=two] to the cache
-> Bold Section qName:emphasis level:5, chunks:[size] attrs:[role:bold]

Multiple instances can be ran at the same time. Once started, the new VMs automatically see the existing Region and its information:

INFO: Connected to Distributed System ['Spring GemFire World'=xxxx:56218/[email protected]]
Hello World!

-> Bold Section qName:emphasis level:5, chunks:[size] attrs:[role:bold]
-> Bold Section qName:emphasis level:5, chunks:[map] attrs:[role:bold]
[2=two] [1=one]
-> Bold Section qName:emphasis level:5, chunks:[query length = 3] attrs:[role:bold]
[one, two]

Experiment with the example, start (and stop) as many instances as you want, run various commands in one instance and see how the others react. To preserve data, at least one instance needs to be alive all times. If all instances are shutdown, the grid data is completely destroyed.

14.1.3. Hello World Sample Explained

Hello World uses both Spring XML and annotations for its configuration. The initial bootstrapping configuration is app-context.xml, which includes the cache configuration defined in the cache-context.xml file and performs classpath component scanning for Spring components.

The cache configuration defines the GemFire cache, Region and for illustrative purposes, a simple CacheListener that acts as a logger.

The main beans are HelloWorld and CommandProcessor which rely on the GemfireTemplate to interact with the distributed fabric. Both classes use annotations to define their dependency and life-cycle callbacks.


In addition to this reference documentation, there are a number of other resources that may help you learn how to use Pivotal GemFire with the Spring Framework. These additional, third-party resources are enumerated in this section.


Appendix A: Namespace reference

The <repositories /> element

The <repositories /> element triggers the setup of the Spring Data repository infrastructure. The most important attribute is base-package which defines the package to scan for Spring Data repository interfaces.[1]

Table 6. Attributes
Name Description


Defines the package to be used to be scanned for repository interfaces extending *Repository (actual interface is determined by specific Spring Data module) in auto detection mode. All packages below the configured package will be scanned, too. Wildcards are allowed.


Defines the postfix to autodetect custom repository implementations. Classes whose names end with the configured postfix will be considered as candidates. Defaults to Impl.


Determines the strategy to be used to create finder queries. See [repositories.query-methods.query-lookup-strategies] for details. Defaults to create-if-not-found.


Defines the location to look for a Properties file containing externally defined queries.


Controls whether nested repository interface definitions should be considered. Defaults to false.

Appendix B: Populators namespace reference

The <populator /> element

The <populator /> element allows to populate the a data store via the Spring Data repository infrastructure.[2]

Table 7. Attributes
Name Description


Where to find the files to read the objects from the repository shall be populated with.

Appendix C: Repository query keywords

Supported query keywords

The following table lists the keywords generally supported by the Spring Data repository query derivation mechanism. However, consult the store-specific documentation for the exact list of supported keywords, because some listed here might not be supported in a particular store.

Table 8. Query keywords
Logical keyword Keyword expressions






After, IsAfter


Before, IsBefore


Containing, IsContaining, Contains


Between, IsBetween


EndingWith, IsEndingWith, EndsWith




False, IsFalse


GreaterThan, IsGreaterThan


GreaterThanEqual, IsGreaterThanEqual


In, IsIn


Is, Equals, (or no keyword)


IsEmpty, Empty


IsNotEmpty, NotEmpty


NotNull, IsNotNull


Null, IsNull


LessThan, IsLessThan


LessThanEqual, IsLessThanEqual


Like, IsLike


Near, IsNear


Not, IsNot


NotIn, IsNotIn


NotLike, IsNotLike


Regex, MatchesRegex, Matches


StartingWith, IsStartingWith, StartsWith


True, IsTrue


Within, IsWithin

Appendix D: Repository query return types

Supported query return types

The following table lists the return types generally supported by Spring Data repositories. However, consult the store-specific documentation for the exact list of supported return types, because some listed here might not be supported in a particular store.

Geospatial types like (GeoResult, GeoResults, GeoPage) are only available for data stores that support geospatial queries.
Table 9. Query return types
Return type Description


Denotes no return value.


Java primitives.

Wrapper types

Java wrapper types.


An unique entity. Expects the query method to return one result at most. In case no result is found null is returned. More than one result will trigger an IncorrectResultSizeDataAccessException.


An Iterator.


A Collection.


A List.


A Java 8 or Guava Optional. Expects the query method to return one result at most. In case no result is found Optional.empty()/Optional.absent() is returned. More than one result will trigger an IncorrectResultSizeDataAccessException.


An either Scala or JavaSlang Option type. Semantically same behavior as Java 8’s Optional described above.


A Java 8 Stream.


A Future. Expects method to be annotated with @Async and requires Spring’s asynchronous method execution capability enabled.


A Java 8 CompletableFuture. Expects method to be annotated with @Async and requires Spring’s asynchronous method execution capability enabled.


A org.springframework.util.concurrent.ListenableFuture. Expects method to be annotated with @Async and requires Spring’s asynchronous method execution capability enabled.


A sized chunk of data with information whether there is more data available. Requires a Pageable method parameter.


A Slice with additional information, e.g. the total number of results. Requires a Pageable method parameter.


A result entry with additional information, e.g. distance to a reference location.


A list of GeoResult<T> with additional information, e.g. average distance to a reference location.


A Page with GeoResult<T>, e.g. average distance to a reference location.

Appendix E: Spring Data GemFire Schema

1. see [repositories.create-instances.spring]
2. see [repositories.create-instances.spring]