Spring Batch - Reference Documentation

While open source software projects and associated communities have focused greater attention on web-based and microservices-based architecture frameworks, there has been a notable lack of focus on reusable architecture frameworks to accommodate Java-based batch processing needs, despite continued needs to handle such processing within enterprise IT environments. The lack of a standard, reusable batch architecture has resulted in the proliferation of many one-off, in-house solutions developed within client enterprise IT functions.

SpringSource (now Pivotal) and Accenture collaborated to change this. Accenture’s hands-on industry and technical experience in implementing batch architectures, SpringSource’s depth of technical experience, and Spring’s proven programming model together made a natural and powerful partnership to create high-quality, market-relevant software aimed at filling an important gap in enterprise Java. Both companies worked with a number of clients who were solving similar problems by developing Spring-based batch architecture solutions. This provided some useful additional detail and real-life constraints that helped to ensure the solution can be applied to the real-world problems posed by clients.

Accenture contributed previously proprietary batch processing architecture frameworks to the Spring Batch project, along with committer resources to drive support, enhancements, and the existing feature set. Accenture’s contribution was based upon decades of experience in building batch architectures with the last several generations of platforms: COBOL/Mainframe, C++/Unix, and now Java/anywhere.

The collaborative effort between Accenture and SpringSource aimed to promote the standardization of software processing approaches, frameworks, and tools that can be consistently leveraged by enterprise users when creating batch applications. Companies and government agencies desiring to deliver standard, proven solutions to their enterprise IT environments can benefit from Spring Batch.

A typical batch program generally:

Spring Batch automates this basic batch iteration, providing the capability to process similar transactions as a set, typically in an offline environment without any user interaction. Batch jobs are part of most IT projects, and Spring Batch is the only open source framework that provides a robust, enterprise-scale solution.

Business Scenarios

Technical Objectives

Spring Batch is designed with extensibility and a diverse group of end users in mind. The figure below shows the layered architecture that supports the extensibility and ease of use for end-user developers.

This layered architecture highlights three major high-level components: Application, Core, and Infrastructure. The application contains all batch jobs and custom code written by developers using Spring Batch. The Batch Core contains the core runtime classes necessary to launch and control a batch job. It includes implementations for JobLauncher, Job, and Step. Both Application and Core are built on top of a common infrastructure. This infrastructure contains common readers and writers and services (such as the RetryTemplate), which are used both by application developers(readers and writers, such as ItemReader and ItemWriter) and the core framework itself (retry, which is its own library).

The following key principles, guidelines, and general considerations should be considered when building a batch solution.

To help design and implement batch systems, basic batch application building blocks and patterns should be provided to the designers and programmers in the form of sample structure charts and code shells. When starting to design a batch job, the business logic should be decomposed into a series of steps that can be implemented using the following standard building blocks:

Additionally, a basic application shell should be provided for business logic that cannot be built using the previously mentioned building blocks.

In addition to the main building blocks, each application may use one or more of standard utility steps, such as:

Batch applications can additionally be categorized by their input source:

The foundation of any batch system is the processing strategy. Factors affecting the selection of the strategy include: estimated batch system volume, concurrency with on-line systems or with other batch systems, available batch windows. (Note that, with more enterprises wanting to be up and running 24x7, clear batch windows are disappearing).

Typical processing options for batch are (in increasing order of implementation complexity):

Some or all of these options may be supported by a commercial scheduler.

The following section discusses these processing options in more detail. It is important to notice that, as a rule of thumb, the commit and locking strategy adopted by batch processes depends on the type of processing performed and that the on-line locking strategy should also use the same principles. Therefore, the batch architecture cannot be simply an afterthought when designing an overall architecture.

The locking strategy can be to use only normal database locks or to implement an additional custom locking service in the architecture. The locking service would track database locking (for example, by storing the necessary information in a dedicated db-table) and give or deny permissions to the application programs requesting a db operation. Retry logic could also be implemented by this architecture to avoid aborting a batch job in case of a lock situation.

1. Normal processing in a batch window For simple batch processes running in a separate batch window where the data being updated is not required by on-line users or other batch processes, concurrency is not an issue and a single commit can be done at the end of the batch run.

In most cases, a more robust approach is more appropriate. Keep in mind that batch systems have a tendency to grow as time goes by, both in terms of complexity and the data volumes they handle. If no locking strategy is in place and the system still relies on a single commit point, modifying the batch programs can be painful. Therefore, even with the simplest batch systems, consider the need for commit logic for restart-recovery options as well as the information concerning the more complex cases described later in this section.

2. Concurrent batch or on-line processing Batch applications processing data that can be simultaneously updated by on-line users should not lock any data (either in the database or in files) which could be required by on-line users for more than a few seconds. Also, updates should be committed to the database at the end of every few transactions. This minimizes the portion of data that is unavailable to other processes and the elapsed time the data is unavailable.

Another option to minimize physical locking is to have logical row-level locking implemented with either an Optimistic Locking Pattern or a Pessimistic Locking Pattern.

These patterns are not necessarily suitable for batch processing, but they might be used for concurrent batch and on-line processing (such as in cases where the database does not support row-level locking). As a general rule, optimistic locking is more suitable for on-line applications, while pessimistic locking is more suitable for batch applications. Whenever logical locking is used, the same scheme must be used for all applications accessing data entities protected by logical locks.

Note that both of these solutions only address locking a single record. Often, we may need to lock a logically related group of records. With physical locks, you have to manage these very carefully in order to avoid potential deadlocks. With logical locks, it is usually best to build a logical lock manager that understands the logical record groups you want to protect and that can ensure that locks are coherent and non-deadlocking. This logical lock manager usually uses its own tables for lock management, contention reporting, time-out mechanism, and other concerns.

3. Parallel Processing Parallel processing allows multiple batch runs or jobs to run in parallel to minimize the total elapsed batch processing time. This is not a problem as long as the jobs are not sharing the same files, db-tables, or index spaces. If they do, this service should be implemented using partitioned data. Another option is to build an architecture module for maintaining interdependencies by using a control table. A control table should contain a row for each shared resource and whether it is in use by an application or not. The batch architecture or the application in a parallel job would then retrieve information from that table to determine if it can get access to the resource it needs or not.

If the data access is not a problem, parallel processing can be implemented through the use of additional threads to process in parallel. In the mainframe environment, parallel job classes have traditionally been used, in order to ensure adequate CPU time for all the processes. Regardless, the solution has to be robust enough to ensure time slices for all the running processes.

Other key issues in parallel processing include load balancing and the availability of general system resources such as files, database buffer pools, and so on. Also note that the control table itself can easily become a critical resource.

4. Partitioning Using partitioning allows multiple versions of large batch applications to run concurrently. The purpose of this is to reduce the elapsed time required to process long batch jobs. Processes that can be successfully partitioned are those where the input file can be split and/or the main database tables partitioned to allow the application to run against different sets of data.

In addition, processes which are partitioned must be designed to only process their assigned data set. A partitioning architecture has to be closely tied to the database design and the database partitioning strategy. Note that database partitioning does not necessarily mean physical partitioning of the database, although in most cases this is advisable. The following picture illustrates the partitioning approach:

The architecture should be flexible enough to allow dynamic configuration of the number of partitions. Both automatic and user controlled configuration should be considered. Automatic configuration may be based on parameters such as the input file size and the number of input records.

4.1 Partitioning Approaches Selecting a partitioning approach has to be done on a case-by-case basis. The following list describes some of the possible partitioning approaches:

1. Fixed and Even Break-Up of Record Set

This involves breaking the input record set into an even number of portions (for example, 10, where each portion has exactly 1/10th of the entire record set). Each portion is then processed by one instance of the batch/extract application.

In order to use this approach, preprocessing is required to split the record set up. The result of this split will be a lower and upper bound placement number which can be used as input to the batch/extract application in order to restrict its processing to only its portion.

Preprocessing could be a large overhead, as it has to calculate and determine the bounds of each portion of the record set.

2. Break up by a Key Column

This involves breaking up the input record set by a key column, such as a location code, and assigning data from each key to a batch instance. In order to achieve this, column values can be either:

Under option 1, adding new values means a manual reconfiguration of the batch/extract to ensure that the new value is added to a particular instance.

Under option 2, this ensures that all values are covered via an instance of the batch job. However, the number of values processed by one instance is dependent on the distribution of column values (there may be a large number of locations in the 0000-0999 range, and few in the 1000-1999 range). Under this option, the data range should be designed with partitioning in mind.

Under both options, the optimal even distribution of records to batch instances cannot be realized. There is no dynamic configuration of the number of batch instances used.

3. Breakup by Views

This approach is basically breakup by a key column but on the database level. It involves breaking up the record set into views. These views are used by each instance of the batch application during its processing. The breakup is done by grouping the data.

With this option, each instance of a batch application has to be configured to hit a particular view (instead of the main table). Also, with the addition of new data values, this new group of data has to be included into a view. There is no dynamic configuration capability, as a change in the number of instances results in a change to the views.

4. Addition of a Processing Indicator

This involves the addition of a new column to the input table, which acts as an indicator. As a preprocessing step, all indicators are marked as being non-processed. During the record fetch stage of the batch application, records are read on the condition that that record is marked as being non-processed, and once they are read (with lock), they are marked as being in processing. When that record is completed, the indicator is updated to either complete or error. Many instances of a batch application can be started without a change, as the additional column ensures that a record is only processed once.

With this option, I/O on the table increases dynamically. In the case of an updating batch application, this impact is reduced, as a write must occur anyway.

5. Extract Table to a Flat File

This involves the extraction of the table into a file. This file can then be split into multiple segments and used as input to the batch instances.

With this option, the additional overhead of extracting the table into a file and splitting it may cancel out the effect of multi-partitioning. Dynamic configuration can be achieved by changing the file splitting script.

6. Use of a Hashing Column

This scheme involves the addition of a hash column (key/index) to the database tables used to retrieve the driver record. This hash column has an indicator to determine which instance of the batch application processes this particular row. For example, if there are three batch instances to be started, then an indicator of 'A' marks a row for processing by instance 1, an indicator of 'B' marks a row for processing by instance 2, and an indicator of 'C' marks a row for processing by instance 3.

The procedure used to retrieve the records would then have an additional WHERE clause to select all rows marked by a particular indicator. The inserts in this table would involve the addition of the marker field, which would be defaulted to one of the instances (such as 'A').

A simple batch application would be used to update the indicators, such as to redistribute the load between the different instances. When a sufficiently large number of new rows have been added, this batch can be run (anytime, except in the batch window) to redistribute the new rows to other instances.

Additional instances of the batch application only require the running of the batch application as described in the preceding paragraphs to redistribute the indicators to work with a new number of instances.

4.2 Database and Application Design Principles

An architecture that supports multi-partitioned applications which run against partitioned database tables using the key column approach should include a central partition repository for storing partition parameters. This provides flexibility and ensures maintainability. The repository generally consists of a single table, known as the partition table.

Information stored in the partition table is static and, in general, should be maintained by the DBA. The table should consist of one row of information for each partition of a multi-partitioned application. The table should have columns for Program ID Code, Partition Number (logical ID of the partition), Low Value of the db key column for this partition, and High Value of the db key column for this partition.

On program start-up, the program id and partition number should be passed to the application from the architecture (specifically, from the Control Processing Tasklet). If a key column approach is used, these variables are used to read the partition table in order to determine what range of data the application is to process. In addition the partition number must be used throughout the processing to:

4.3 Minimizing Deadlocks

When applications run in parallel or are partitioned, contention in database resources and deadlocks may occur. It is critical that the database design team eliminates potential contention situations as much as possible as part of the database design.

Also, the developers must ensure that the database index tables are designed with deadlock prevention and performance in mind.

Deadlocks or hot spots often occur in administration or architecture tables, such as log tables, control tables, and lock tables. The implications of these should be taken into account as well. A realistic stress test is crucial for identifying the possible bottlenecks in the architecture.

To minimize the impact of conflicts on data, the architecture should provide services such as wait-and-retry intervals when attaching to a database or when encountering a deadlock. This means a built-in mechanism to react to certain database return codes and, instead of issuing an immediate error, waiting a predetermined amount of time and retrying the database operation.

4.4 Parameter Passing and Validation

The partition architecture should be relatively transparent to application developers. The architecture should perform all tasks associated with running the application in a partitioned mode, including:

The validation should include checks to ensure that:

If the database is partitioned, some additional validation may be necessary to ensure that a single partition does not span database partitions.

Also, the architecture should take into consideration the consolidation of partitions. Key questions include:

Spring Batch follows Spring Framework’s baselines for both Java version and third party dependencies. With Spring Batch 5, the Spring Framework version is being upgraded to Spring Framework 6 which requires Java 17. As a result, the Java version requirement for Spring Batch is also increasing to Java 17.

In order to continue the integration with supported versions of the third party libraries that Spring Batch uses, Spring Batch 5 is updating the dependencies across the board to the following versions:

This release also marks the migration to Jakarta EE 9 APIs.

This section describes stereotypes relating to the concept of a batch job. A Job is an entity that encapsulates an entire batch process. As is common with other Spring projects, a Job is wired together with either an XML configuration file or Java-based configuration. This configuration may be referred to as the "job configuration". However, Job is just the top of an overall hierarchy, as shown in the following diagram:

In Spring Batch, a Job is simply a container for Step instances. It combines multiple steps that belong logically together in a flow and allows for configuration of properties global to all steps, such as restartability. The job configuration contains:

A Step is a domain object that encapsulates an independent, sequential phase of a batch job. Therefore, every Job is composed entirely of one or more steps. A Step contains all of the information necessary to define and control the actual batch processing. This is a necessarily vague description because the contents of any given Step are at the discretion of the developer writing a Job. A Step can be as simple or complex as the developer desires. A simple Step might load data from a file into the database, requiring little or no code (depending upon the implementations used). A more complex Step may have complicated business rules that are applied as part of the processing. As with a Job, a Step has an individual StepExecution that correlates with a unique JobExecution, as shown in the following image:

An ExecutionContext represents a collection of key/value pairs that are persisted and controlled by the framework in order to allow developers a place to store persistent state that is scoped to a StepExecution object or a JobExecution object. For those familiar with Quartz, it is very similar to JobDataMap. The best usage example is to facilitate restart. Using flat file input as an example, while processing individual lines, the framework periodically persists the ExecutionContext at commit points. Doing so allows the ItemReader to store its state in case a fatal error occurs during the run or even if the power goes out. All that is needed is to put the current number of lines read into the context, as shown in the following example, and the framework will do the rest:

Using the EndOfDay example from the Job Stereotypes section as an example, assume there is one step, 'loadData', that loads a file into the database. After the first failed run, the metadata tables would look like the following example:

In the preceding case, the Step ran for 30 minutes and processed 40,321 'pieces', which would represent lines in a file in this scenario. This value is updated just before each commit by the framework and can contain multiple rows corresponding to entries within the ExecutionContext. Being notified before a commit requires one of the various StepListener implementations (or an ItemStream), which are discussed in more detail later in this guide. As with the previous example, it is assumed that the Job is restarted the next day. When it is restarted, the values from the ExecutionContext of the last run are reconstituted from the database. When the ItemReader is opened, it can check to see if it has any stored state in the context and initialize itself from there, as shown in the following example:

In this case, after the above code runs, the current line is 40,322, allowing the Step to start again from where it left off. The ExecutionContext can also be used for statistics that need to be persisted about the run itself. For example, if a flat file contains orders for processing that exist across multiple lines, it may be necessary to store how many orders have been processed (which is much different from the number of lines read), so that an email can be sent at the end of the Step with the total number of orders processed in the body. The framework handles storing this for the developer, in order to correctly scope it with an individual JobInstance. It can be very difficult to know whether an existing ExecutionContext should be used or not. For example, using the 'EndOfDay' example from above, when the 01-01 run starts again for the second time, the framework recognizes that it is the same JobInstance and on an individual Step basis, pulls the ExecutionContext out of the database, and hands it (as part of the StepExecution) to the Step itself. Conversely, for the 01-02 run, the framework recognizes that it is a different instance, so an empty context must be handed to the Step. There are many of these types of determinations that the framework makes for the developer, to ensure the state is given to them at the correct time. It is also important to note that exactly one ExecutionContext exists per StepExecution at any given time. Clients of the ExecutionContext should be careful, because this creates a shared keyspace. As a result, care should be taken when putting values in to ensure no data is overwritten. However, the Step stores absolutely no data in the context, so there is no way to adversely affect the framework.

It is also important to note that there is at least one ExecutionContext per JobExecution and one for every StepExecution. For example, consider the following code snippet:

As noted in the comment, ecStep does not equal ecJob. They are two different ExecutionContexts. The one scoped to the Step is saved at every commit point in the Step, whereas the one scoped to the Job is saved in between every Step execution.

JobRepository is the persistence mechanism for all of the Stereotypes mentioned above. It provides CRUD operations for JobLauncher, Job, and Step implementations. When a Job is first launched, a JobExecution is obtained from the repository, and, during the course of execution, StepExecution and JobExecution implementations are persisted by passing them to the repository.

JobLauncher represents a simple interface for launching a Job with a given set of JobParameters, as shown in the following example:

It is expected that implementations obtain a valid JobExecution from the JobRepository and execute the Job.

ItemReader is an abstraction that represents the retrieval of input for a Step, one item at a time. When the ItemReader has exhausted the items it can provide, it indicates this by returning null. More details about the ItemReader interface and its various implementations can be found in Readers And Writers.

ItemWriter is an abstraction that represents the output of a Step, one batch or chunk of items at a time. Generally, an ItemWriter has no knowledge of the input it should receive next and knows only the item that was passed in its current invocation. More details about the ItemWriter interface and its various implementations can be found in Readers And Writers.

ItemProcessor is an abstraction that represents the business processing of an item. While the ItemReader reads one item, and the ItemWriter writes them, the ItemProcessor provides an access point to transform or apply other business processing. If, while processing the item, it is determined that the item is not valid, returning null indicates that the item should not be written out. More details about the ItemProcessor interface can be found in Readers And Writers.

Spring 3 brought the ability to configure applications with java instead of XML. As of Spring Batch 2.2.0, you can configure batch jobs by using the same Java configuration. There are three components for the Java-based configuration: the @EnableBatchProcessing annotation and two builders.

The @EnableBatchProcessing annotation works similarly to the other @Enable* annotations in the Spring family. In this case, @EnableBatchProcessing provides a base configuration for building batch jobs. Within this base configuration, an instance of StepScope is created in addition to a number of beans being made available to be autowired:

The core interface for this configuration is the BatchConfigurer. The default implementation provides the beans mentioned in the preceding list and requires a DataSource to be provided as a bean within the context. This data source is used by the JobRepository instance. You can customize any of these beans by creating a custom implementation of the BatchConfigurer interface. Typically, extending the DefaultBatchConfigurer (which is provided if a BatchConfigurer is not found) and overriding the required getter is sufficient. However, you may need to implement your own from scratch. The following example shows how to provide a custom transaction manager:

With the base configuration in place, you can use the provided builder factories to configure a job. The following example shows a two-step job configured with the JobBuilderFactory and the StepBuilderFactory:

As described earlier, the JobRepository is used for basic CRUD operations of the various persisted domain objects within Spring Batch, such as JobExecution and StepExecution. It is required by many of the major framework features, such as the JobLauncher, Job, and Step.

The most basic implementation of the JobLauncher interface is the SimpleJobLauncher. Its only required dependency is a JobRepository (needed to obtain an execution).

Once a JobExecution is obtained, it is passed to the execute method of Job, ultimately returning the JobExecution to the caller, as shown in the following image:

The sequence is straightforward and works well when launched from a scheduler. However, issues arise when trying to launch from an HTTP request. In this scenario, the launching needs to be done asynchronously so that the SimpleJobLauncher returns immediately to its caller. This is because it is not good practice to keep an HTTP request open for the amount of time needed by long running processes (such as batch jobs). The following image shows an example sequence:

You can configure the SimpleJobLauncher to allow for this scenario by configuring a TaskExecutor.

Any implementation of the spring TaskExecutor interface can be used to control how jobs are asynchronously executed.

At a minimum, launching a batch job requires two things: the Job to be launched and a JobLauncher. Both can be contained within the same context or different contexts. For example, if you launch jobs from the command line, a new JVM is instantiated for each Job. Thus, every job has its own JobLauncher. However, if you run from within a web container within the scope of an HttpRequest, there is usually one JobLauncher (configured for asynchronous job launching) that multiple requests invoke to launch their jobs.

So far, both the JobLauncher and JobRepository interfaces have been discussed. Together, they represent the simple launching of a job and basic CRUD operations of batch domain objects:

A JobLauncher uses the JobRepository to create new JobExecution objects and run them. Job and Step implementations later use the same JobRepository for basic updates of the same executions during the running of a Job. The basic operations suffice for simple scenarios. However, in a large batch environment with hundreds of batch jobs and complex scheduling requirements, more advanced access to the metadata is required:

The JobExplorer and JobOperator interfaces, which are discussed in the coming sections, add additional functionality for querying and controlling the metadata.

Spring Batch uses a 'Chunk-oriented' processing style within its most common implementation. Chunk oriented processing refers to reading the data one at a time and creating 'chunks' that are written out within a transaction boundary. Once the number of items read equals the commit interval, the entire chunk is written out by the ItemWriter, and then the transaction is committed. The following image shows the process:

The following pseudo code shows the same concepts in a simplified form:

A chunk-oriented step can also be configured with an optional ItemProcessor to process items before passing them to the ItemWriter. The following image shows the process when an ItemProcessor is registered in the step:

The following pseudo code shows how this is implemented in a simplified form:

For more details about item processors and their use cases, please refer to the Item processing section.

Chunk-oriented processing is not the only way to process in a Step. What if a Step must consist of a simple stored procedure call? You could implement the call as an ItemReader and return null after the procedure finishes. However, doing so is a bit unnatural, since there would need to be a no-op ItemWriter. Spring Batch provides the TaskletStep for this scenario.

Tasklet is a simple interface that has one method, execute, which is called repeatedly by the TaskletStep until it either returns RepeatStatus.FINISHED or throws an exception to signal a failure. Each call to a Tasklet is wrapped in a transaction. Tasklet implementors might call a stored procedure, a script, or a simple SQL update statement.

With the ability to group steps together within an owning job comes the need to be able to control how the job "flows" from one step to another. The failure of a Step does not necessarily mean that the Job should fail. Furthermore, there may be more than one type of 'success' that determines which Step should be executed next. Depending upon how a group of Steps is configured, certain steps may not even be processed at all.

Both the XML and flat file examples shown earlier use the Spring Resource abstraction to obtain a file. This works because Resource has a getFile method, which returns a java.io.File. Both XML and flat file resources can be configured using standard Spring constructs:

The preceding Resource loads the file from the specified file system location. Note that absolute locations have to start with a double slash (//). In most Spring applications, this solution is good enough, because the names of these resources are known at compile time. However, in batch scenarios, the file name may need to be determined at runtime as a parameter to the job. This can be solved using '-D' parameters to read a system property.

All that would be required for this solution to work would be a system argument (such as -Dinput.file.name="file://outputs/file.txt").

Often, in a batch setting, it is preferable to parametrize the file name in the JobParameters of the job, instead of through system properties, and access them that way. To accomplish this, Spring Batch allows for the late binding of various Job and Step attributes.

Both the JobExecution and StepExecution level ExecutionContext can be accessed in the same way.

Although a simple concept, an ItemReader is the means for providing data from many different types of input. The most general examples include:

There are many more possibilities, but we focus on the basic ones for this chapter. A complete list of all available ItemReader implementations can be found in Appendix A.

ItemReader is a basic interface for generic input operations, as shown in the following interface definition:

The read method defines the most essential contract of the ItemReader. Calling it returns one item or null if no more items are left. An item might represent a line in a file, a row in a database, or an element in an XML file. It is generally expected that these are mapped to a usable domain object (such as Trade, Foo, or others), but there is no requirement in the contract to do so.

It is expected that implementations of the ItemReader interface are forward only. However, if the underlying resource is transactional (such as a JMS queue) then calling read may return the same logical item on subsequent calls in a rollback scenario. It is also worth noting that a lack of items to process by an ItemReader does not cause an exception to be thrown. For example, a database ItemReader that is configured with a query that returns 0 results returns null on the first invocation of read.

ItemWriter is similar in functionality to an ItemReader but with inverse operations. Resources still need to be located, opened, and closed but they differ in that an ItemWriter writes out, rather than reading in. In the case of databases or queues, these operations may be inserts, updates, or sends. The format of the serialization of the output is specific to each batch job.

As with ItemReader, ItemWriter is a fairly generic interface, as shown in the following interface definition:

As with read on ItemReader, write provides the basic contract of ItemWriter. It attempts to write out the list of items passed in as long as it is open. Because it is generally expected that items are 'batched' together into a chunk and then output, the interface accepts a list of items, rather than an item by itself. After writing out the list, any flushing that may be necessary can be performed before returning from the write method. For example, if writing to a Hibernate DAO, multiple calls to write can be made, one for each item. The writer can then call flush on the hibernate session before returning.

Both ItemReaders and ItemWriters serve their individual purposes well, but there is a common concern among both of them that necessitates another interface. In general, as part of the scope of a batch job, readers and writers need to be opened, closed, and require a mechanism for persisting state. The ItemStream interface serves that purpose, as shown in the following example:

Before describing each method, we should mention the ExecutionContext. Clients of an ItemReader that also implement ItemStream should call open before any calls to read, in order to open any resources such as files or to obtain connections. A similar restriction applies to an ItemWriter that implements ItemStream. As mentioned in Chapter 2, if expected data is found in the ExecutionContext, it may be used to start the ItemReader or ItemWriter at a location other than its initial state. Conversely, close is called to ensure that any resources allocated during open are released safely. update is called primarily to ensure that any state currently being held is loaded into the provided ExecutionContext. This method is called before committing, to ensure that the current state is persisted in the database before commit.

In the special case where the client of an ItemStream is a Step (from the Spring Batch Core), an ExecutionContext is created for each StepExecution to allow users to store the state of a particular execution, with the expectation that it is returned if the same JobInstance is started again. For those familiar with Quartz, the semantics are very similar to a Quartz JobDataMap.

Note that the CompositeItemWriter is an example of the delegation pattern, which is common in Spring Batch. The delegates themselves might implement callback interfaces, such as StepListener. If they do and if they are being used in conjunction with Spring Batch Core as part of a Step in a Job, then they almost certainly need to be registered manually with the Step. A reader, writer, or processor that is directly wired into the Step gets registered automatically if it implements ItemStream or a StepListener interface. However, because the delegates are not known to the Step, they need to be injected as listeners or streams (or both if appropriate).

One of the most common mechanisms for interchanging bulk data has always been the flat file. Unlike XML, which has an agreed upon standard for defining how it is structured (XSD), anyone reading a flat file must understand ahead of time exactly how the file is structured. In general, all flat files fall into two types: delimited and fixed length. Delimited files are those in which fields are separated by a delimiter, such as a comma. Fixed Length files have fields that are a set length.

Spring Batch provides transactional infrastructure for both reading XML records and mapping them to Java objects as well as writing Java objects as XML records.

We need to consider how XML input and output works in Spring Batch. First, there are a few concepts that vary from file reading and writing but are common across Spring Batch XML processing. With XML processing, instead of lines of records (FieldSet instances) that need to be tokenized, it is assumed an XML resource is a collection of 'fragments' corresponding to individual records, as shown in the following image:

The 'trade' tag is defined as the 'root element' in the scenario above. Everything between '<trade>' and '</trade>' is considered one 'fragment'. Spring Batch uses Object/XML Mapping (OXM) to bind fragments to objects. However, Spring Batch is not tied to any particular XML binding technology. Typical use is to delegate to Spring OXM, which provides uniform abstraction for the most popular OXM technologies. The dependency on Spring OXM is optional and you can choose to implement Spring Batch specific interfaces if desired. The relationship to the technologies that OXM supports is shown in the following image:

With an introduction to OXM and how one can use XML fragments to represent records, we can now more closely examine readers and writers.

Spring Batch provides support for reading and Writing JSON resources in the following format:

It is assumed that the JSON resource is an array of JSON objects corresponding to individual items. Spring Batch is not tied to any particular JSON library.

It is a common requirement to process multiple files within a single Step. Assuming the files all have the same formatting, the MultiResourceItemReader supports this type of input for both XML and flat file processing. Consider the following files in a directory:

file-1.txt and file-2.txt are formatted the same and, for business reasons, should be processed together. The MultiResourceItemReader can be used to read in both files by using wildcards.

The referenced delegate is a simple FlatFileItemReader. The above configuration reads input from both files, handling rollback and restart scenarios. It should be noted that, as with any ItemReader, adding extra input (in this case a file) could cause potential issues when restarting. It is recommended that batch jobs work with their own individual directories until completed successfully.

Like most enterprise application styles, a database is the central storage mechanism for batch. However, batch differs from other application styles due to the sheer size of the datasets with which the system must work. If a SQL statement returns 1 million rows, the result set probably holds all returned results in memory until all rows have been read. Spring Batch provides two types of solutions for this problem:

Batch systems are often used in conjunction with other application styles. The most common is an online system, but it may also support integration or even a thick client application by moving necessary bulk data that each application style uses. For this reason, it is common that many users want to reuse existing DAOs or other services within their batch jobs. The Spring container itself makes this fairly easy by allowing any necessary class to be injected. However, there may be cases where the existing service needs to act as an ItemReader or ItemWriter, either to satisfy the dependency of another Spring Batch class or because it truly is the main ItemReader for a step. It is fairly trivial to write an adapter class for each service that needs wrapping, but because it is such a common concern, Spring Batch provides implementations: ItemReaderAdapter and ItemWriterAdapter. Both classes implement the standard Spring method by invoking the delegate pattern and are fairly simple to set up.

One important point to note is that the contract of the targetMethod must be the same as the contract for read: When exhausted, it returns null. Otherwise, it returns an Object. Anything else prevents the framework from knowing when processing should end, either causing an infinite loop or incorrect failure, depending upon the implementation of the ItemWriter.

By default, all of the ItemReader and ItemWriter implementations store their current state in the ExecutionContext before it is committed. However, this may not always be the desired behavior. For example, many developers choose to make their database readers 'rerunnable' by using a process indicator. An extra column is added to the input data to indicate whether or not it has been processed. When a particular record is being read (or written) the processed flag is flipped from false to true. The SQL statement can then contain an extra statement in the where clause, such as where PROCESSED_IND = false, thereby ensuring that only unprocessed records are returned in the case of a restart. In this scenario, it is preferable to not store any state, such as the current row number, since it is irrelevant upon restart. For this reason, all readers and writers include the 'saveState' property.

The ItemReader configured above does not make any entries in the ExecutionContext for any executions in which it participates.

So far, this chapter has discussed the basic contracts of reading and writing in Spring Batch and some common implementations for doing so. However, these are all fairly generic, and there are many potential scenarios that may not be covered by out-of-the-box implementations. This section shows, by using a simple example, how to create a custom ItemReader and ItemWriter implementation and implement their contracts correctly. The ItemReader also implements ItemStream, in order to illustrate how to make a reader or writer restartable.

In this section, we will introduce you to readers and writers that have not already been discussed in the previous sections.

Performing a single transformation is useful in many scenarios, but what if you want to 'chain' together multiple ItemProcessor implementations? This can be accomplished using the composite pattern mentioned previously. To update the previous, single transformation, example, Foo is transformed to Bar, which is transformed to Foobar and written out, as shown in the following example:

A FooProcessor and a BarProcessor can be 'chained' together to give the resultant Foobar, as shown in the following example:

Just as with the previous example, the composite processor can be configured into the Step:

One typical use for an item processor is to filter out records before they are passed to the ItemWriter. Filtering is an action distinct from skipping. Skipping indicates that a record is invalid, while filtering simply indicates that a record should not be written.

For example, consider a batch job that reads a file containing three different types of records: records to insert, records to update, and records to delete. If record deletion is not supported by the system, then we would not want to send any "delete" records to the ItemWriter. But, since these records are not actually bad records, we would want to filter them out rather than skip them. As a result, the ItemWriter would receive only "insert" and "update" records.

To filter a record, you can return null from the ItemProcessor. The framework detects that the result is null and avoids adding that item to the list of records delivered to the ItemWriter. As usual, an exception thrown from the ItemProcessor results in a skip.

In the ItemReaders and ItemWriters chapter, multiple approaches to parsing input have been discussed. Each major implementation throws an exception if it is not 'well-formed'. The FixedLengthTokenizer throws an exception if a range of data is missing. Similarly, attempting to access an index in a RowMapper or FieldSetMapper that does not exist or is in a different format than the one expected causes an exception to be thrown. All of these types of exceptions are thrown before read returns. However, they do not address the issue of whether or not the returned item is valid. For example, if one of the fields is an age, it obviously cannot be negative. It may parse correctly, because it exists and is a number, but it does not cause an exception. Since there are already a plethora of validation frameworks, Spring Batch does not attempt to provide yet another. Rather, it provides a simple interface, called Validator, that can be implemented by any number of frameworks, as shown in the following interface definition:

The contract is that the validate method throws an exception if the object is invalid and returns normally if it is valid. Spring Batch provides an out of the box ValidatingItemProcessor, as shown in the following bean definition:

You can also use the BeanValidatingItemProcessor to validate items annotated with the Bean Validation API (JSR-303) annotations. For example, given the following type Person:

you can validate items by declaring a BeanValidatingItemProcessor bean in your application context and register it as a processor in your chunk-oriented step:

When a chunk is rolled back, items that have been cached during reading may be reprocessed. If a step is configured to be fault tolerant (typically by using skip or retry processing), any ItemProcessor used should be implemented in a way that is idempotent. Typically that would consist of performing no changes on the input item for the ItemProcessor and only updating the instance that is the result.

The simplest way to start parallel processing is to add a TaskExecutor to your Step configuration.

In this example, the taskExecutor is a reference to another bean definition that implements the TaskExecutor interface. TaskExecutor is a standard Spring interface, so consult the Spring User Guide for details of available implementations. The simplest multi-threaded TaskExecutor is a SimpleAsyncTaskExecutor.

The result of the above configuration is that the Step executes by reading, processing, and writing each chunk of items (each commit interval) in a separate thread of execution. Note that this means there is no fixed order for the items to be processed, and a chunk might contain items that are non-consecutive compared to the single-threaded case. In addition to any limits placed by the task executor (such as whether it is backed by a thread pool), there is a throttle limit in the tasklet configuration which defaults to 4. You may need to increase this to ensure that a thread pool is fully utilized.

Note also that there may be limits placed on concurrency by any pooled resources used in your step, such as a DataSource. Be sure to make the pool in those resources at least as large as the desired number of concurrent threads in the step.

There are some practical limitations of using multi-threaded Step implementations for some common batch use cases. Many participants in a Step (such as readers and writers) are stateful. If the state is not segregated by thread, then those components are not usable in a multi-threaded Step. In particular, most of the off-the-shelf readers and writers from Spring Batch are not designed for multi-threaded use. It is, however, possible to work with stateless or thread safe readers and writers, and there is a sample (called parallelJob) in the Spring Batch Samples that shows the use of a process indicator (see Preventing State Persistence) to keep track of items that have been processed in a database input table.

Spring Batch provides some implementations of ItemWriter and ItemReader. Usually, they say in the Javadoc if they are thread safe or not or what you have to do to avoid problems in a concurrent environment. If there is no information in the Javadoc, you can check the implementation to see if there is any state. If a reader is not thread safe, you can decorate it with the provided SynchronizedItemStreamReader or use it in your own synchronizing delegator. You can synchronize the call to read() and as long as the processing and writing is the most expensive part of the chunk, your step may still complete much faster than it would in a single threaded configuration.

As long as the application logic that needs to be parallelized can be split into distinct responsibilities and assigned to individual steps, then it can be parallelized in a single process. Parallel Step execution is easy to configure and use.

The configurable task executor is used to specify which TaskExecutor implementation should be used to execute the individual flows. The default is SyncTaskExecutor, but an asynchronous TaskExecutor is required to run the steps in parallel. Note that the job ensures that every flow in the split completes before aggregating the exit statuses and transitioning.

See the section on Split Flows for more detail.

In remote chunking, the Step processing is split across multiple processes, communicating with each other through some middleware. The following image shows the pattern:

The manager component is a single process, and the workers are multiple remote processes. This pattern works best if the manager is not a bottleneck, so the processing must be more expensive than the reading of items (as is often the case in practice).

The manager is an implementation of a Spring Batch Step with the ItemWriter replaced by a generic version that knows how to send chunks of items to the middleware as messages. The workers are standard listeners for whatever middleware is being used (for example, with JMS, they would be MessageListener implementations), and their role is to process the chunks of items using a standard ItemWriter or ItemProcessor plus ItemWriter, through the ChunkProcessor interface. One of the advantages of using this pattern is that the reader, processor, and writer components are off-the-shelf (the same as would be used for a local execution of the step). The items are divided up dynamically and work is shared through the middleware, so that, if the listeners are all eager consumers, then load balancing is automatic.

The middleware has to be durable, with guaranteed delivery and a single consumer for each message. JMS is the obvious candidate, but other options (such as JavaSpaces) exist in the grid computing and shared memory product space.

See the section on Spring Batch Integration - Remote Chunking for more detail.

Spring Batch also provides an SPI for partitioning a Step execution and executing it remotely. In this case, the remote participants are Step instances that could just as easily have been configured and used for local processing. The following image shows the pattern:

The Job runs on the left-hand side as a sequence of Step instances, and one of the Step instances is labeled as a manager. The workers in this picture are all identical instances of a Step, which could in fact take the place of the manager, resulting in the same outcome for the Job. The workers are typically going to be remote services but could also be local threads of execution. The messages sent by the manager to the workers in this pattern do not need to be durable or have guaranteed delivery. Spring Batch metadata in the JobRepository ensures that each worker is executed once and only once for each Job execution.

The SPI in Spring Batch consists of a special implementation of Step (called the PartitionStep) and two strategy interfaces that need to be implemented for the specific environment. The strategy interfaces are PartitionHandler and StepExecutionSplitter, and their role is shown in the following sequence diagram:

The Step on the right in this case is the “remote” worker, so, potentially, there are many objects and or processes playing this role, and the PartitionStep is shown driving the execution.

Similar to the multi-threaded step’s throttle-limit attribute, the grid-size attribute prevents the task executor from being saturated with requests from a single step.

There is a simple example that can be copied and extended in the unit test suite for Spring Batch Samples (see partition*Job.xml configuration).

Spring Batch creates step executions for the partitions called "step1:partition0", and so on. Many people prefer to call the manager step "step1:manager" for consistency. You can use an alias for the step (by specifying the name attribute instead of the id attribute).

Batch processing is about repetitive actions, either as a simple optimization or as part of a job. To strategize and generalize the repetition and to provide what amounts to an iterator framework, Spring Batch has the RepeatOperations interface. The RepeatOperations interface has the following definition:

The callback is an interface, shown in the following definition, that lets you insert some business logic to be repeated:

The callback is executed repeatedly until the implementation determines that the iteration should end. The return value in these interfaces is an enumeration that can either be RepeatStatus.CONTINUABLE or RepeatStatus.FINISHED. A RepeatStatus enumeration conveys information to the caller of the repeat operations about whether there is any more work to do. Generally speaking, implementations of RepeatOperations should inspect the RepeatStatus and use it as part of the decision to end the iteration. Any callback that wishes to signal to the caller that there is no more work to do can return RepeatStatus.FINISHED.

The simplest general purpose implementation of RepeatOperations is RepeatTemplate, as shown in the following example:

In the preceding example, we return RepeatStatus.CONTINUABLE, to show that there is more work to do. The callback can also return RepeatStatus.FINISHED, to signal to the caller that there is no more work to do. Some iterations can be terminated by considerations intrinsic to the work being done in the callback. Others are effectively infinite loops as far as the callback is concerned and the completion decision is delegated to an external policy, as in the case shown in the preceding example.

Inside a RepeatTemplate, the termination of the loop in the iterate method is determined by a CompletionPolicy, which is also a factory for the RepeatContext. The RepeatTemplate has the responsibility to use the current policy to create a RepeatContext and pass that in to the RepeatCallback at every stage in the iteration. After a callback completes its doInIteration, the RepeatTemplate has to make a call to the CompletionPolicy to ask it to update its state (which will be stored in the RepeatContext). Then it asks the policy if the iteration is complete.

Spring Batch provides some simple general purpose implementations of CompletionPolicy. SimpleCompletionPolicy allows execution up to a fixed number of times (with RepeatStatus.FINISHED forcing early completion at any time).

Users might need to implement their own completion policies for more complicated decisions. For example, a batch processing window that prevents batch jobs from executing once the online systems are in use would require a custom policy.

If there is an exception thrown inside a RepeatCallback, the RepeatTemplate consults an ExceptionHandler, which can decide whether or not to re-throw the exception.

The following listing shows the ExceptionHandler interface definition:

A common use case is to count the number of exceptions of a given type and fail when a limit is reached. For this purpose, Spring Batch provides the SimpleLimitExceptionHandler and a slightly more flexible RethrowOnThresholdExceptionHandler. The SimpleLimitExceptionHandler has a limit property and an exception type that should be compared with the current exception. All subclasses of the provided type are also counted. Exceptions of the given type are ignored until the limit is reached, and then they are rethrown. Exceptions of other types are always rethrown.

An important optional property of the SimpleLimitExceptionHandler is the boolean flag called useParent. It is false by default, so the limit is only accounted for in the current RepeatContext. When set to true, the limit is kept across sibling contexts in a nested iteration (such as a set of chunks inside a step).

Often, it is useful to be able to receive additional callbacks for cross-cutting concerns across a number of different iterations. For this purpose, Spring Batch provides the RepeatListener interface. The RepeatTemplate lets users register RepeatListener implementations, and they are given callbacks with the RepeatContext and RepeatStatus where available during the iteration.

The RepeatListener interface has the following definition:

The open and close callbacks come before and after the entire iteration. before, after, and onError apply to the individual RepeatCallback calls.

Note that, when there is more than one listener, they are in a list, so there is an order. In this case, open and before are called in the same order while after, onError, and close are called in reverse order.

Implementations of RepeatOperations are not restricted to executing the callback sequentially. It is quite important that some implementations are able to execute their callbacks in parallel. To this end, Spring Batch provides the TaskExecutorRepeatTemplate, which uses the Spring TaskExecutor strategy to run the RepeatCallback. The default is to use a SynchronousTaskExecutor, which has the effect of executing the whole iteration in the same thread (the same as a normal RepeatTemplate).

Sometimes there is some business processing that you know you want to repeat every time it happens. The classic example of this is the optimization of a message pipeline. It is more efficient to process a batch of messages, if they are arriving frequently, than to bear the cost of a separate transaction for every message. Spring Batch provides an AOP interceptor that wraps a method call in a RepeatOperations object for just this purpose. The RepeatOperationsInterceptor executes the intercepted method and repeats according to the CompletionPolicy in the provided RepeatTemplate.