Spring XD is a unified, distributed, and extensible service for data ingestion, real time analytics, batch processing, and data export. The Spring XD project is an open source Apache 2 License licenced project whose goal is to tackle big data complexity. Much of the complexity in building real-world big data applications is related to integrating many disparate systems into one cohesive solution across a range of use-cases. Common use-cases encountered in creating a comprehensive big data solution are

The Spring XD project aims to provide a one stop shop solution for these use-cases.

If you want to try out Spring XD, we’d recommend downloading a snapshot build, since things are changing quite fast. A snapshot distribution can be downloaded from the spring snapshots repository. You can also build the project from source if you wish. The wiki content should also be kept up to date with the current snapshot so if you are reading this on the github website, things may have changed since the last milestone.

Unzip the distribution which will unpack to a single installation directory. All the commands below are executed from this directory, so change into it before proceeding.

If you are sure you want the previous milestone release, you can also download the distribution spring-xd-1.0.0.M5-dist.zip and its accompanying documentation.

Set the environment variable XD_HOME to the installation directory <root-install-dir>\spring-xd\xd

If you are on a Mac and using homebrew, all you need to do to install Spring XD is:

Homebrew will install springxd to /usr/local/bin. Now you can jump straight into using Spring XD:

Spring XD can be run in two different modes. There’s a single-node runtime option for testing and development, and there’s a distributed runtime which supports distribution of processing tasks across multiple nodes. This document will get you up and running quickly with a single-node runtime. See Running Distributed Mode for details on setting up a distributed runtime.

The single node option is the easiest to get started with. It runs everything you need in a single process. To start it, you just need to cd to the xd directory and run the following command

In a separate terminal, cd into the shell directory and start the XD shell, which you can use to issue commands.

The shell is a more user-friendly front end to the REST API which Spring XD exposes to clients. The URL of the currently targeted Spring XD server is shown at startup.

You should now be able to start using Spring XD.

In Spring XD, a basic stream defines the ingestion of event driven data from a source to a sink that passes through any number of processors. You can create a new stream by issuing a stream create command from the XD shell. Stream defintions are built from a simple DSL. For example, execute:

This defines a stream named ticktock based off the DSL expression time | log. The DSL uses the "pipe" symbol |, to connect a source to a sink. The stream server finds the time and log definitions in the modules directory and uses them to setup the stream. In this simple example, the time source simply sends the current time as a message each second, and the log sink outputs it using the logging framework at the WARN logging level. In the shell where you started the server, you will see log output similar to that listed below

To stop the stream, and remove the definition completely, you can use the stream destroy command:

It is also possibly to stop and restart the stream instead, using the undeploy and deploy commands. The shell supports command completion so you can hit the tab key to see which commands and options are available.

Learn about the modules available in Spring XD in the Sources, Processors, and Sinks sections of the documentation.

Don’t see what you’re looking for? Create a custom module: source, processor or sink (and then consider contributing it back to Spring XD).

Want to add some analytics to your stream? Check out the Taps and Analytics sections.

The Spring XD distributed runtime (DIRT) supports distribution of processing tasks across multiple nodes. See Getting Started for information on running Spring XD as a single node.

Spring XD can use several middlewares when running in distributed mode. At the time of writing, Redis and RabbitMQ are available options.

Let’s see how to install those first, before diving into the specifics of running Spring XD. Again, those are alternatives when it comes to transport middleware used, so you need only one (although practically, Redis may be required for other purposes, for example storage of definitions or Analytics).

Redis is actually the default when it comes to running in distributed mode, so let’s start with that.

Spring XD consists of two servers

You can start the xd-container and xd-admin servers individually as follows:

Spring XD supports the following Hadoop distributions:

To specify the distribution to use for Hadoop client connections,

Pass in the --help option to see other configuration properties.

If you wish to use a XD-Shell that is on a different machine than where you deployed your admin server.

1) Open your shell

2) From the xd shell use the "admin config server" command i.e.

To change the configuration of the server components you can use environment variables or make changes to the $XD_HOME/config/xd-config.yml YAML file. Alternatively, you can set the environment variable XD_CONFIG to point to a file (use a file:// URL). and that file will then be used instead. The values in this configuration file will overwrite the values of the default application.yml file that is embedded in the XD jar file.

The use of YAML is an alternative to using property files. The support for processing YAML files comes from the underlying use of Spring Boot as the basis for the XD server implementation.

To change the configuration of properties used in modules, look into the module definition file and locate the property file that is being referenced. The file will often reside in the directory $XD_HOME/config.

In this section we will walk through configuring some of the more common properties of Spring XD servers.

Spring XD is a unified, distributed, and extensible service for data ingestion, real time analytics, batch processing, and data export. The foundations of XD’s architecture are based on the over 100+ man years of work that have gone into the Spring Batch, Integration and Data projects. Building upon these projects, Spring XD provides servers and a configuration DSL that you can immediately use to start processing data.  You do not need to build an application yourself from a collection of jars to start using Spring XD.

Spring XD has two modes of operation - single and multi-node. The first is a single process that is responsible for all processing and administration. This mode helps you get started easily and simplifies the development and testing of your application. The second is a distributed mode, where processing tasks can be spread across a cluster of machines and an administrative server sends commands to control processing tasks executing on the cluster.

The creation and execution of Batch jobs builds upon the functionality available in the Spring Batch and Spring for Apache Hadoop projects. See the Batch Jobs section for more information.

Taps provide a non-invasive way to consume the data that is being processed by either a Stream or a Job, much like a real time telephone wire tap lets you eavesdrop on telephone conversations. Taps are recommended as way to collect metrics and perform analytics on a Stream of data. See the section Taps for more information.

In Spring XD, a basic stream defines the ingestion of event driven data from a source to a sink that passes through any number of processors. Stream processing is performed inside the XD Containers and the deployment of stream definitions to containers is done via the XD Admin Server. The Getting Started section shows you how to start these servers and how to start and use the Spring XD shell

Sources, sinks and processors are predefined configurations of a module. Module definitions are found in the xd/modules directory. [1]. Modules definitions are standard Spring configuration files that use existing Spring classes, such as Input/Output adapters and Transformers from Spring Integration that support general Enterprise Integration Patterns.

A high level DSL is used to create stream definitions. The DSL to define a stream that has an http source and a file sink (with no processors) is shown below

The DSL mimics a UNIX pipes and filters syntax. Default values for ports and filenames are used in this example but can be overriden using -- options, such as

To create these stream definitions you make an HTTP POST request to the XD Admin Server. More details can be found in the sections below.

The XD Admin server [2] exposes a full RESTful API for managing the lifecycle of stream definitions, but the easiest way to use the XD shell. Start the shell as described in the Getting Started section

New streams are created by posting stream definitions. The definitions are built from a simple DSL. For example, let’s walk through what happens if we execute the following shell command

This defines a stream named ticktock based off the DSL expression time | log. The DSL uses the "pipe" symbol |, to connect a source to a sink. The stream server finds the time and log definitions in the modules directory and uses them to setup the stream. In this simple example, the time source simply sends the current time as a message each second, and the log sink outputs it using the logging framework.

You can delete a stream by issuing the stream destroy command from the shell:

Often you will want to stop a stream, but retain the name and definition for future use. In that case you can undeploy the stream by name and issue the deploy command at a later time to restart it.

Let’s try something a bit more complicated and swap out the time source for something else. Another supported source type is http, which accepts data for ingestion over HTTP POSTs. Note that the http source accepts data on a different port (default 9000) from the Admin Server (default 8080).

To create a stream using an http source, but still using the same log sink, we would change the original command above to

which will produce the following output from the server

Note that we don’t see any other output this time until we actually post some data (using shell command)

and the stream will then funnel the data from the http source to the output log implemented by the log sink

Of course, we could also change the sink implementation. You could pipe the output to a file (file), to hadoop (hdfs) or to any of the other sink modules which are provided. You can also define your own modules.

As an example of a simple processing step, we can transform the payload of the HTTP posted data to upper case using the stream definitions

To create this stream enter the following command in the shell

Posting some data (using shell command)

Will result in an uppercased hello in the log

See the Processors section for more information.

In the examples above, we connected a source to a sink using the pipe symbol |. You can also pass parameters to the source and sink configurations. The parameter names will depend on the individual module implementations, but as an example, the http source module exposes a port setting which allows you to change the data ingestion port from the default value. To create the stream using port 8000, we would use

If you know a bit about Spring configuration files, you can inspect the module definition to see which properties it exposes. Alternatively, you can read more in the source and sink documentation.

In the examples above, simple module definitions are used to construct each stream. However, modules may be grouped together in order to avoid duplication and/or reduce the amount of chattiness over the messaging middleware. To learn more about that feature, refer to the Composing Modules section.

If directed graphs are needed instead of the simple linear streams described above, two features are relevant. First, named channels may be used as a way to combine multiple flows upstream and/or downstream from the channel. The behavior of that channel may either be queue-based or topic-based depending on what prefix is used ("queue:myqueue" or "topic:mytopic", respectively). To learn more, refer to the Named Channels section. Second, you may need to determine the output channel of a stream based on some information that is only known at runtime. To learn about such content-based routing, refer to the Dynamic Router section.

The XD runtime environment supports data ingestion by allowing users to define streams. Streams are composed of modules which encapsulate a unit of work into a reusable component.

Modules are categorized by type, typically representing the role or function of the module. Current XD module types include source, sink, and processor which indicate how they modules may be composed in a stream. Specifically, a source polls an external resource, or is triggered by an event and only provides an output. The first module in a stream is always a source. A processor performs some type of transformation or business logic and provides an input and one or more outputs. A sink provides only an input and outputs data to an external resource to terminate the stream.

XD comes with a number of modules used for assembling streams which perform common input and/or output operations with files, HDFS, http, twitter, syslog, GemFire, and more. Users can easily assemble these into streams to build complex big data applications without having to know the underlying Spring products on which XD is built.

However, if you are interested in extending XD with your own modules, some knowledge of Spring, Spring Integration, and Spring Batch is essential. The remainder of this document assumes the reader has some familiarity with these topics.

This section provides details on how to write and register custom modules. For a quick start, dive into the examples of creating source, processor, and sink modules.

A Module has the following required attributes:

XD provides a strategy interface ModuleRegistry which it uses to find a module of a given name and type. Currently XD provides RedisModuleRegistry and FileModuleRegistry, The ModuleRegistry is a required component for the XD Server. By default the XD Server is configured with the FileModuleRegistry which looks for modules in ${xd.home:..}/modules. Where xd.home is a Java System Property or may be passed as a command line argument to the container launcher. So out of the box, the modules are contained in the XD modules directory. The modules directory organizes module types in sub-directories. So you will see something like:

Using the default server configuration, you simply drop your module file into the modules directory and deploy a stream to the server.

As described above, a stream is defined as a sequence of modules, minimally a source module followed by a sink module. One or more processor modules may be added in between the source and sink, but they are not mandatory. Sometimes streams are similar for a subset of their modules. For example, consider the following two streams:

Other than the source module, the definitions of those two streams are the same. It would be better to avoid this degree of duplication. This is the first problem that composed modules address.

Each module within a stream represents a unit of deployment. Therefore, in each of the streams defined above, there would be 3 such units (the source, the processor, and the sink). In a singlenode runtime, it doesn’t make much of a difference since the communication between each module would be a bridge between in-memory channels. When deploying a stream to a distributed runtime environment, however, the communication between each module occurs over messaging middleware. That decoupling between modules is useful in that it promotes loose-coupling and thus enables load-balancing and buffering of messages when the consuming module(s) are temporarily busy or down. Nevertheless, at times the individual module boundaries are more fine-grained than necessary for these middleware "hops". Overhead may be avoided by reducing the overall number of deployment units and therefore the number of hops. In such cases, it’s convenient to be able to wrap multiple modules together so that they act as a single "black box" unit for deployment. In other words, if "foo | bar" are composed together as a new module named "baz", the input and/or output to "baz" would still occur as a hop over the middleware, but the communication from foo to bar would occur directly, in-process. This is the second problem that composed modules address.

Now let’s look at an example. Returning to the two similar streams above, the filter processor and file sink could be combined into a single module. In the shell, the following command would take care of that:

Then, to verify the new module composition was successful, check if it exists:

Notice that the composed module shows up in the list of sink modules. That is because logically, it has the structure of a sink: it provides an input channel (which is bridged to the filter processor’s input channel), but it provides no output channel (since the file sink has no output).

If a module were composed of two processors, it would be classified as a processor itself:

If a module were composed of a source and a processor, it would be classified as a source itself:

Based on the logical type of the composed module, it may be used in a stream as if it were a simple module instance. For example, to redefine the two streams from the first problem case above, now that the "foo" sink module has been composed, you would issue the following shell commands:

To test the "httpfoo" stream, try the following:

The first message should have been ignored due to the filter, but the second one should exist in the file:

To test the "filefoo" stream, echo "foo" to a file in the /tmp/xd/input/filefoo directory, then verify:

When you no longer need a composed module, you may delete it with the "module delete" command in the shell. However, if that composed module is currently being used by one or more streams, the deletion will fail as shown below:

As you can see, the failure message shows which stream(s) depend upon the composed module you are trying to delete.

If you destroy both of those streams and try again, it will work:

When creating a module, if you duplicate the name of an existing module for the same type, you will receive an error. In the example below the user tried to compose a tcp module, however one already exists:

However, you can create a module for a given type even though a module of that name exists but as a different type. For example: I can create a sink module named filter, even though a filter module exists already as a processor.

Finally, it’s worth mentioning that in some cases duplication may be avoided by reusing an actual stream rather than a composed module. That is possible when named channels are used in the source and/or sink position of a stream definition. For example, the same overall functionality as provided by the two streams above could also be achieved as follows:

This approach is more appropriate for use-cases where individual streams on either side of the named channel may need to be deployed or undeployed independently. Whereas the queue typed channel will load-balance across multiple downstream consumers, the "topic:" prefix may be used if broadcast behavior is needed instead. For more information about named channels, refer to the Named Channels section.

To view the available modules use the the module list command. To filter the module result set you can use the --type option. For example:

To get information about a particular module (such as what options it accepts), use the module info --<module type>:<module name> command. For example:

To display the actual definition file of a module use the module display --name <module type>:<module name> command. For example:

In this section we will show some variations on input sources. As a prerequisite start the XD Container as instructed in the Getting Started page.

The Sources covered are

Future releases will provide support for other currently available Spring Integration Adapters. For information on how to adapt an existing Spring Integration Adapter for use in Spring XD see the section Creating a Source Module.

The following sections show a mix of Spring XD shell and plain Unix shell commands, so if you are trying them out, you should open two separate terminal prompts, one running the XD shell and one to enter the standard commands for sending HTTP data, creating directories, reading files and so on.

To create a stream definition in the server using the XD shell

Post some data to the http server on the default port of 9000

See if the data ended up in the file

Make sure the default input directory exists

Create an empty file to tail (this is not needed on some platforms such as Linux)

To create a stream definition using the XD shell

Send some text into the file being monitored

See if the data ended up in the file

The file source provides the contents of a File as a byte array by default but may be configured to provide the file reference itself.

To log the contents of a file create a stream definition using the XD shell

The file source by default will look into a directory named after the stream, in this case /tmp/xd/input/filetest

Note the above will log the raw bytes. For text files, it is normally desirable to output the contents as plain text. To do this, set the outputType parameter:

For more details on the use of the outputType parameter see Type Conversion

Copy a file into the directory /tmp/xd/input/filetest and observe its contents being logged in the XD Container.

Spring XD provides two modules for receiving emails. Both have very similar options so they’ll be described together here. The first one is named imap and only supports the imap protocol, using the IDLE command. As such, it does not use polling. Instead messages are pushed as soon as they arrive. The other module is named mail and supports all protocols (pop & imap), but it uses polling.

Let’s see an example:

Then send an email to yourself and you should see it appear inside a file at /tmp/xd/output/mailstream

The full list of options for the mail and imap sources is below (most of them can be configured once and for all in the mail.properties file):

The twittersearch source has four parameters

To get a consumerKey and consumerSecret you need to register a twitter application. If you don’t already have one set up, you can create an app at the Twitter Developers site to get these credentials.

To create a stream definition in the server using the XD shell

Make sure the default output directory for the file sink exists

Let the twittersearch run for a little while and then check to see if some data ended up in the file

This source ingests data from Twitter’s streaming API. It uses the sample and filter stream endpoints rather than the full "firehose" which needs special access. The endpoint used will depend on the parameters you supply in the stream definition (some are specific to the filter endpoint).

You need to supply all keys and secrets (both consumer and accessToken) to authenticate for this source, so it is easiest if you just add these to the XD_HOME/config/twitter.properties file. Stream creation is then straightforward:

The parameters available are pretty much the same as those listed in the API docs and unless otherwise stated, the accepted formats are the same.

This source configures a cache and replicated region in the XD container process along with a Spring Integration GemFire inbound channel adapter, backed by a CacheListener that outputs messages triggered by an external entry event on the region. By default the payload contains the updated entry value, but may be controlled by passing in a SpEL expression that uses the EntryEvent as the evaluation context.

Continuous query allows client applications to create a GemFire query using Object Query Language(OQL) and register a CQ listener which subscribes to the query and is notified every time the query 's result set changes. The gemfire_cq source registers a CQ which will post CQEvent messages to the stream.

Three syslog sources are provided: syslog-udp, syslog-tcp and reactor-syslog. The reactor-syslog adapter uses tcp and builds upon the functionality available in Reactor project and provides improved throughput over the syslog-tcp adapter. They all support the following option:

To create a stream definition (using shell command)

or

Send a test message to the syslog

See if the data ended up in the file

Refer to your syslog documentation to configure the syslog daemon to forward syslog messages to the stream; some examples are:

UDP - Mac OSX (syslog.conf) and Ubuntu (rsyslog.conf)

TCP - Ubuntu (rsyslog.conf)

Restart the syslog daemon after reconfiguring.

The tcp source acts as a server and allows a remote party to connect to XD and submit data over a raw tcp socket.

To create a stream definition in the server, use the following XD shell command

This will create the default TCP source and send data read from it to the tcptest file.

TCP is a streaming protocol and some mechanism is needed to frame messages on the wire. A number of decoders are available, the default being CRLF which is compatible with Telnet.

See if the data ended up in the file

The tcp-client source module uses raw tcp sockets, as does the tcp module but contrary to the tcp module, acts as a client. Whereas the tcp module will open a listening socket and wait for connections from a remote party, the tcp-client will initiate the connection to a remote server and emit as messages what that remote server sends over the wire. As an optional feature, the tcp-client can itself emit messages to the remote server, so that a simple conversation can take place.

The "rabbit" source enables receiving messages from RabbitMQ.

The following example shows the default settings.

Configure a stream:

This receives messages from a queue named rabbittest and writes them to the default file sink (/tmp/xd/output/rabbittest.out). It uses the default RabbitMQ broker running on localhost, port 5672.

The queue(s) must exist before the stream is deployed. We do not create the queue(s) automatically. However, you can easily create a Queue using the RabbitMQ web UI. Then, using that same UI, you can navigate to the "rabbittest" Queue and publish test messages to it.

Notice that the file sink has --binary=true; this is because, by default, the data emitted by the source will be bytes. This can be modified by setting the content_type property on messages to text/plain. In that case, the source will convert the message to a String; you can then omit the --binary=true and the file sink will then append a newline after each message.

To destroy the stream, enter the following at the shell prompt:

The "jms" source enables receiving messages from JMS.

The following example shows the default settings.

Configure a stream:

This receives messages from a queue named jmstest and writes them to the default file sink (/tmp/xd/output/jmstest). It uses the default ActiveMQ broker running on localhost, port 61616.

To destroy the stream, enter the following at the shell prompt:

To test the above stream, you can use something like the following…

and tail -f /tmp/xd/output/jmstest

Run this as a Java application; each time you hit <enter> in the console, it will send a message to queue jmstest.

The time source will simply emit a String with the current time every so often. It supports the following options:

The mqtt source connects to an mqtt server and receives telemetry messages.

Configure a stream:

If you wish to use the MQTT Source defaults you can execute the command as follows:

This section will cover the processors available out-of-the-box with Spring XD. As a prerequisite, start the XD Container as instructed in the Getting Started page.

The Processors covered are

See the section Creating a Processor Module for information on how to create custom processor modules.

Use the filter module in a stream to determine whether a Message should be passed to the output channel.

Use this filter to only pass messages to the output channel if they contain a specific JSON field matching a specific value.

This filter will only pass Messages to the log sink if the JSON payload contains the firstName "John". Try sending this payload to the HTTP endpoint and you should see it in the XD log:

Alternatively, if you send a different firstName, you shouldn’t see the log entry.

Use the transform module in a stream to convert a Message’s content or structure.

This processor converts a JSON message payload to the value of a specific JSON field.

Try sending this payload to the HTTP endpoint and you should see just the value "John" in the XD log:

Note: JSON fields should be separated by a comma without any spaces.

The script processor contains a Service Activator that invokes a specified Groovy script. This is a slightly more generic way to accomplish processing logic, as the provided script may simply terminate the stream as well as transform or filter Messages.

To use the module, pass the location of a Groovy script using the location attribute. If you want to pass variable values to your script, you can optionally pass the path to a properties file using the properties-location attribute. All properties in the file will be made available to the script as variables.

By default, Spring XD will search the classpath for custom-processor.groovy and custom-processor.properties. You can place the script in ${xd.home}/modules/processor/scripts and the properties file in ${xd.home}/config to make them available on the classpath. Alternatively, you can prefix the location and properties-location values with file: to load from the file system.

The splitter module builds upon the concept of the same name in Spring Integration and allows the splitting of a single message into several distinct messages.

The splitter module accepts the following options:

As part of the SpEL expression you can make use of the pre-registered JSON Path function. The syntax is #jsonPath(payload,<json path expression>)2

The aggregator module does the opposite of the splitter, and builds upon the concept of the same name found in Spring Integration. By default, it will consider all incoming messages from a stream to belong to the same group:

This uses a SpEL expression that will basically concatenate all payloads together, inserting a space character in between. As such,

would emit a single message whose contents is "Hello World !". This is because we set the aggregator release strategy to accumulate 3 messages.

The aggregator modules comes with many more options, as shown below:

Additionally, the message store used to retain messages can be configured using the store option. Valid options are memory (the default), redis and jdbc.

In this section we will show some variations on output sinks. As a prerequisite start the XD Container as instructed in the Getting Started page.

The Sinks covered are

See the section Creating a Sink Module for information on how to create sink modules using other Spring Integration Adapters.

Probably the simplest option for a sink is just to log the data. The log sink uses the application logger to output the data for inspection. The log level is set to WARN and the logger name is created from the stream name. To create a stream using a log sink you would use a command like

You can then try adding some data. We’ve used the http source on port 8000 here, so run the following command to send a message

and you should see the following output in the XD container console.

The logger name is the sink name prefixed with the string "logger.". The sink name is the same as the stream name by default, but you can set it by passing the --name parameter

Another simple option is to stream data to a file on the host OS. This can be done using the file sink module to create a stream.

We’ve used the http source again, so run the following command to send a message

The file sink uses the stream name as the default name for the file it creates, and places the file in the /tmp/xd/output/ directory.

You can cutomize the behavior and specify the name and dir properties of the output file. For example

If you do not have Hadoop installed, you can install Hadoop 1.2.1 as described in our separate guide. Spring XD supports 4 Hadoop distributions, see using Hadoop for more information on how to start Spring XD to target a specific distribution.

Once Hadoop is up and running, you can then use the hdfs sink when creating a stream

In the above example, we’ve scheduled time source to automatically send ticks to hdfs once in every second. If you wait a little while for data to accumuluate you can then list can then list the files in the hadoop filesystem using the shell’s built in hadoop fs commands. Before making any access to HDFS in the shell you first need to configure the shell to point to your name node. This is done using the hadoop config command.

In this example the hdfs protocol is used but you may also use the webhdfs protocol. Listing the contents in the output directory (named by default after the stream name) is done by issuing the following command.

While the file is being written to it will have the tmp suffix. When the data written exceeds the rollover size (default 1GB) it will be renamed to remove the tmp suffix. There are several options to control the in use file file naming options. These are --inUsePrefix and --inUseSuffix set the file name prefix and suffix respectfully.

When you destroy a stream

and list the stream directory again, in use file suffix doesn’t exist anymore.

To list the list the contents of a file directly from a shell execute the hadoop cat command.

In the above examples we didn’t yet go through why the file was written in a specific directory and why it was named in this specific way. Default location of a file is defined as /xd/<stream name>/<stream name>-<rolling part>.txt. These can be changed using options --directory and --fileName respectively. Example is shown below.

It is also possible to control the size of a files written into HDFS. The --rollover option can be used to control when file currently being written is rolled over and a new file opened by providing the rollover size in bytes, kilobytes, megatypes, gigabytes, and terabytes.

Shortcuts to specify sizes other than bytes are written as --rollover=64M, --rollover=512G or --rollover=1T.

The stream can also be compressed during the write operation. Example of this is shown below.

From a native os shell we can use hadoop’s fs commands and pipe data into gunzip.

Often a stream of data may not have a high enough rate to roll over files frequently, leaving the file in an opened state. This prevents users from reading a consistent set of data when running mapreduce jobs. While one can alleviate this problem by using a small rollover value, a better way is to use the idleTimeout option that will automatically close the file if there was no writes during the specified period of time. This feature is also useful in cases where burst of data is written into a stream and you’d like that data to become visible in HDFS.

In the above example we changed a source to http order to control what we write into a hdfs sink. We defined a small rollover size and a timeout of 10 seconds. Now we can simply post data into this stream via source end point using a below command.

If we repeat the command very quickly and then wait for the timeout we should be able to see that some files are closed before rollover size was met and some were simply rolled because of a rollover size.

The Hadoop Dataset Avro sink is used to store Java classes that are sent as the payload on the stream. It uses the Kite SDK Data Module's Dataset implementation to store the payload data serialized in Avro format. The Avro schema is generated from the Java class that is persisted.

The Hadoop Dataset Avro sink requires that you have a Hadoop installation that is based on Hadoop v2 (Hadoop 2.2.0, Pivotal HD 1.0, Cloudera CDH4 or Hortonworks HDP 2.0), see using Hadoop for more information on how to start Spring XD to target a specific distribution.

Once Hadoop is up and running, you can then use the avro sink when creating a stream

In the above example, we’ve scheduled time source to automatically send ticks to the avro sink once every second. The data will be stored in a directory named /xd/<streamname> by default, so in this example it will be /xd/mydataset. You can change this by supplying a ‑‑directory parameter. The Avro data files are stored in a sub-directory named after the payload Java class, in this example the stream payload is a String so the name of the data sub-directory is string. If you have multiple Java classes as payloads, each class will get its own sub-directory.

Let the stream run for a minute or so. You can then list the contents of the hadoop filesystem using the shell’s built in hadoop fs commands. You will first need to configure the shell to point to your name node using the hadoop config command. We use the hdfs protocol is to access the hadoop name node.

Then list the contents of the stream’s data directory.

You can see that the sink has created two files containing the first two batches of 20 stream payloads each. There is also a .metadata directory created that contains the metadata that the Kite SDK Dataset implementation uses as well as the generated Avro schema for the persisted type.

Now destroy the stream.

The JDBC sink can be used to insert message payload data into a relational database table. By default it inserts the entire payload into a table named after the stream name in the HSQLDB database that XD uses to store metadata for batch jobs. To alter this behavior you should modify the config/jdbc.properties file with the connection parameters you want to use. There is also a config/init_db.sql file that contains the SQL statements used to initialize the database table. You can modify this file if you’d like to create a table with your specific layout when the sink starts. You should also change the initializeDatabase property to true to have this script execute when the sink starts up.

The payload data will be inserted as-is if the names option is set to payload. This is the default behavior. If you specify any other column names the payload data will be assumed to be a JSON document that will be converted to a hash map. This hash map will be used to populate the data values for the SQL insert statement. A matching of column names with underscores like user_name will match onto camel case style keys like userName in the hash map. There will be one insert statement executed for each message.

To create a stream using a jdbc sink relying on all defaults you would use a command like

This will insert the time messages into a payload column in a table named mydata. Since the default is using the XD batch metadata HSQLDB database we can connect to this database instance from an external tool. After we let the stream run for a little while, we can connect to the database and look at the data stored in the database.

You can query the database with your favorite SQL tool using the following database URL: jdbc:hsqldb:hsql://localhost:9101/xdjob with sa as the user name and a blank password. You can also use the HSQL provided SQL Tool (download from HSQLDB) to run a quick query from the command line:

This should result in something similar to the following output:

Now we can destroy the stream using:

The TCP Sink provides for outbound messaging over TCP.

The following examples use netcat (linux) to receive the data; the equivalent on Mac OSX is nc.

First, start a netcat to receive the data, and background it

Now, configure a stream

This sends the time, every 3 seconds to the default tcp Sink, which connects to port 1234 on localhost.

TCP is a streaming protocol and some mechanism is needed to frame messages on the wire. A number of encoders are available, the default being CRLF.

Destroy the stream; netcat will terminate when the TCP Sink disconnects.

The "mail" sink allows sending of messages as emails, leveraging Spring Integration mail-sending channel adapter. Please refer to Spring Integration documentation for the details, but in a nutshell, the sink is able to handle String, byte[] and MimeMessage messages out of the box.

Here is a simple example of how the mail module is used:

Then,

You would then receive an email whose body contains "Hello" and whose subject is "Hellow world". Of special attention here is the way you need to escape strings for most of the parameters, because they’re actually SpEL expressions (so here for example, we used a String literal for the to parameter).

The full list of options available to the mail module is below (note that most of these options can be set once and for all in the mail.properties file):

The "rabbit" sink enables outbound messaging over RabbitMQ.

The following example shows the default settings.

Configure a stream:

This sends the time, every 3 seconds to the default (no-name) Exchange for a RabbitMQ broker running on localhost, port 5672.

The routing key will be the name of the stream by default; in this case: "rabbittest". Since the default Exchange is a direct-exchange to which all Queues are bound with the Queue name as the binding key, all messages sent via this sink will be passed to a Queue named "rabbittest", if one exists. We do not create that Queue automatically. However, you can easily create a Queue using the RabbitMQ web UI. Then, using that same UI, you can navigate to the "rabbittest" Queue and click the "Get Message(s)" button to pop messages off of that Queue (you can choose whether to requeue those messages).

To destroy the stream, enter the following at the shell prompt:

Currently XD supports GemFire’s client-server topology. A sink that writes data to a GemFire cache requires at least one cache server to be running in a separate process and may also be configured to use a Locator. While Gemfire configuration is outside of the scope of this document, details are covered in the GemFire Product documentation. The XD distribution includes a standalone GemFire server executable suitable for development and test purposes and bootstrapped using a Spring configuration file provided as a command line argument. The GemFire jar is distributed freely under GemFire’s development license and is subject to the license’s terms and conditions. Sink modules provided with the XD distrubution that write data to GemFire create a client cache and client region. No data is cached on the client.

A Splunk sink that writes data to a TCP Data Input type for Splunk.

The mqtt sink connects to an mqtt server and publishes telemetry messages.

The Dynamic Router support allows for routing Spring XD messages to named channels based on the evaluation of SpEL expressions or Groovy Scripts.

A Tap allows you to "listen in" to data from another stream and process the data separately from the original stream definition. The original stream is unaffected by the tap and isn’t aware of its presence, similar to a phone wiretap (WireTaps are part of the standard catalog of EAI patterns and are part of the Spring Integration EAI framework used by Spring XD).

A tap acts like a source in that it occurs as the first module within a stream and can pipe its output to a sink (and/or one or more processors added to a chain before the ultimate sink), but for a tap the messages are actually those being processed by some other stream.

Taps are specified as named channels in a stream definition, where the channel name always begins with tap:.

To create a tap using the shell, use the following command (assuming you want to tap into the "foo1" stream, which we’ll create first):

A tap can consume data from any point along the target stream’s processing pipeline. For example, if you have a stream called mystream, defined as

Then creating a tap using

would tap into the stream’s data after the filter has been applied but before the transformer. So the untransformed data would be sent to sink2.

A primary use case is to perform realtime analytics at the same time as data is being ingested via its primary stream. For example, consider a Stream of data that is consuming Twitter search results and writing them to HDFS. A tap can be created before the data is written to HDFS, and the data piped from the tap to a counter that correspond to the number of times specific hashtags were mentioned in the tweets.

Creating a tap on a named channel, a stream whose source is a named channel, or a label is not yet supported. This is planned for a future release.

You’ll find specific examples of creating taps on existing streams in the Analytics section.

A side effect of a stream being unaware of any taps on its pipeline is that deleting the stream will not automatically delete the taps. The taps have to be deleted separately. However if the tapped stream is re-created, the existing tap will continue to function.

XD allows you to declaratively specify type conversion within processing streams using inputType and outputType parameters on module definitions. Currently, XD supports the following type conversions commonly used in streams:

Where JSON represents JSON content in the form of a String. Registration of custom type converters will likely be supported in a future release.

inputType and outputType values are parsed as media types, e.g., application/json or text/plain;charset=UTF-8. Media types are especially useful for indicating how to interpret String or byte[] content. XD also uses standard media type format to represent Java types, using the general type application/x-java-object with a type parameter. For example, application/x-java-object;type=java.util.Map or application/x-java-object;type=com.bar.Foo . For convenience, you can specify the class name by itself and XD will map it to the corresponding media type. In addition, XD provides a namespace for internal types, notably, application/x-xd-tuple to specify a Tuple.

Internally, XD implements type conversion using Spring Integration’s [datatype channels](http://docs.spring.io/spring-integration/docs/latest-ga/reference/htmlsingle/#channel-configuration). The data type channel converts payloads to the configured datatype using Spring’s [conversion service](http://docs.spring.io/spring/docs/current/spring-framework-reference/htmlsingle/#core-convert). When XD processes a module with a declared type conversion, it modifies the module’s input and/or output channel definition to set the required Java type and register Converters targeting the corresponding Java type to the channels conversion service. This requires that the Java type be determined from the specified media type, as shown below.

One of the features that XD offers is the ability to launch and monitor batch jobs based on Spring Batch. The Spring Batch project was started in 2007 as a collaboration between SpringSource and Accenture to provide a comprehensive framework to support the development of robust batch applications. Batch jobs have their own set of best practices and domain concepts which have been incorporated into Spring Batch building upon Accenture’s consulting business. Since then Spring Batch has been used in thousands of enterprise applications and is the basis for the recent JSR standardization of batch processing, JSR-352.

Spring XD builds upon Spring Batch to simplify creating batch workflow solutions that span traditional use-cases such as moving data between flat files and relational databases as well as Hadoop use-cases where analysis logic is broken up into several steps that run on a Hadoop cluster. Steps specific to Hadoop in a workflow can be MapReduce jobs, executing Hive/Pig scripts or HDFS operations.

The concept of a workflow translates to a Job, not to be confused with a MapReduce job. A Job is a directed graph, each node of the graph is a processing Step. Steps can be executed sequentially or in parallel, depending on the configuration. Jobs can be started, stopped, and restarted. Restarting jobs is possible since the progress of executed steps in a Job is persisted in a database via a JobRepository. The following figures shows the basic components of a workflow.

A Job that has steps specific to Hadoop is shown below.

A JobLauncher is responsible for starting a job and is often triggered via a scheduler. Other options to launch a job are through Spring XD’s RESTful administration API, the XD web application, or in response to an external event from and XD stream defintion, e.g file polling using the file source.

Spring XD allows you to create and launch jobs. The launching of a job can be trigger using a cron expression or in reaction to data on a stream. When jobs are executing, they are also a souce of event data that can be subscribed to by a stream. There are several type of events sent during a job’s execution, the most common being the status of the job and the steps taken within the job. This bi-direction communication between stream processing and batch processing allows for more complex chains of processing to be developed.

As a starting point, jobs for the following cases are provided to use out of the box

These are described in the section below.

This purpose of this section is to show you how to create, schedule and monitor a job.

The Jobs definitions provided as part of the Spring XD distribution as well as those included in the Spring XD Samples repository can be used a basis for building your own custom Jobs. The development of a Job largely follows the development of a Spring Batch job, for which there are several references.

For help developing Job steps specific to Hadoop, e.g. HDFS, Pig, Hive, the Spring XD Samples is useful as wella s the following resources

Once your Jobs have been developed and unit tested, they are integrated into Spring XD by copying the resulting .jar file and Job XML definition to $XD_HOME/lib and $XD_HOME/modules/jobs.

To describe the creation of a job we will use the job definition that is part of the batch-simple example.

To create a job in the XD shell, execute the job create command composed of:

So using our example above where we have a myjob.xml job definiton file in the $XD_HOME/modules/jobs directory, this will look like:

In the logging output of the XDContainer you should see the following:

XD uses triggers as well as regular event flow to launch the batch jobs. So in this section we will cover how to:

Spring XD offers the facilities to capture the notifications that are sent from the job as it is executing.

Notifications include:

In the following example, we setup a Batch Job called myHttpJob. Afterwards we create a stream that will tap into the notifications channel that was implicitly generated when the myHttpJob job was created. The stream receives the notifications and sends those notifications to the log.

In the logging output of the container you should see something like the following when the job completes:

Spring XD provides a series of pre-defined Batch listeners in order to send job notifications:

You can directly reference those Spring beans from within your Batch jobs. If you need to further customize the provided behavior you can of course create your own Batch listeners and directly inject a reference to the notifications message channel.

For a complete example, please see the Batch Notifications Sample which is part of the Spring XD Samples repository.

Batch Jobs can be deleted by executing:

Alternatively, one can just undeploy the job, keeping its definition for a future redeployment:

Spring XD comes with several batch import and export modules. You can run them out of the box or use them as a basis for building your own custom modules.

Spring XD Analytics provides support for real-time analysis of data using metrics such as counters and gauges. Spring XD intends to support a wide range of these metrics and analytical data structures as a general purpose class library that works with several backend storage technologies.

We’ll look at the following metrics

An in memory implementation and a Redis implementation are provided in Spring XD 1.0.0.M3. Other metrics that will be provided in a future release are Rate Counters and Histograms.

Metrics can be used directly in place of a sink just as if you were creating any other stream, but you can also analyze data from an existing stream using a tap. We’ll look at some examples of using metrics with taps in the following sections. As a prerequisite start the XD Container as instructed in the Getting Started page.

A counter is a Metric that associates a unique name with a long value. It is primarily used for counting events triggered by incoming messages on a target stream. You create a counter with a unique name and optionally an initial value then set its value in response to incoming messages. The most straightforward use for counter is simply to count messages coming into the target stream. That is, its value is incremented on every message. This is exactly what the counter module provided by Spring XD does.

Here’s an example:

Start by creating a data ingestion stream. Something like:

Next, create a tap on the springtweets stream that sets a message counter named tweetcount

The results are written to redis under the key counter.${name}. To retrieve the count:

A field value counter is a Metric used for counting occurrences of unique values for a named field in a message payload. XD Supports the following payload types out of the box:

For example suppose a message source produces a payload with a field named user :

If the stream source produces messages with the following objects:

The field value counter on the field user will contain:

Multi-value fields are also supported. For example, if a field contains a list, each value will be counted once:

The field value counter on the field users will contain:

field_value_counter has the following options:

To try this out, create a stream to ingest twitter feeds containing the word spring and output to a file:

Now create a tap for a field value counter:

The twittersearch source produces JSON strings which contain the user id of the tweeter in the fromUser field. The field_value_counter sink parses the tweet and updates a field value counter named fromUser in Redis. To view the counts:

The aggregate counter differs from a simple counter in that it not only keeps a total value for the count, but also retains the total count values for each minute, hour day and month of the period for which it is run. The data can then be queried by supplying a start and end date and the resolution at which the data should be returned.

Creating an aggregate counter is very similar to a simple counter. For example, to obtain an aggregate count for our spring tweets stream:

you’d simply create a tap which pipes the input to aggregatecounter:

The Redis back-end stores the aggregate counts in buckets prefixed with aggregatecounters.${name}. The rest of the string contains the date information. So for our tweetcount counter you might see something like the following keys appearing in Redis:

The general format is

A gauge is a Metric, similar to a counter in that it holds a single long value associated with a unique name. In this case the value can represent any numeric value defined by the application.

The gauge sink provided with XD stores expects a numeric value as a payload, typically this would be a decimal formatted string, and stores its values in Redis. The gauge includes the following attributes:

Here is an example of creating a tap for a gauge:

A rich gauge is a Metric that holds a double value associated with a unique name. In addition to the value, the rich gauge keeps a running average, along with the minimum and maximum values and the sample count.

The richgauge sink provided with XD expects a numeric value as a payload, typically this would be a decimal formatted string, and keeps its value in a store. The richgauge includes the following attributes:

When stored in Redis, the values are kept as a space delimited string, formatted as value alpha mean max min count

Here are some examples of creating a tap for a rich gauge:

Spring XD has a discoverable RESTful API based on the Spring HATEAOS library. You can discover the resources available by making a GET request on the root resource of the Admin server. Here is an example where navigate down to find the data for a counter named httptap that was created by these commands

The root resource returns

Following the resource location for the counter

And then the data for the counter itself

Spring XD provides a DSL for defining a stream. Over time the DSL is likely to evolve significantly as it gains the ability to define more and more sophisticated streams as well as the steps of a batch job.

A simple linear stream consists of a sequence of modules. Typically an Input Source, (optional) Processing Steps, and an Output Sink. As a simple example consider the collection of data from an HTTP Source writing to a File Sink. Using the DSL the stream description is:

A stream that involves some processing:

The modules in a stream definition are connected together using the pipe symbol |.

Each module may take parameters. The parameters supported by a module are defined by the module implementation. As an example the http source module exposes port setting which allows the data ingestion port to be changed from the default value.

It is only necessary to quote parameter values if they contain spaces or the | character. Here the transform processor module is being passed a SpEL expression that will be applied to any data it encounters:

If the parameter value needs to embed a single quote, use two single quotes:

Instead of a source or sink it is possible to use a named channel. Normally the modules in a stream are connected by anonymous internal channels (represented by the pipes), but by using explicitly named channels it becomes possible to construct more sophisticated flows. In keeping with the unix theme, sourcing/sinking data from/to a particular channel uses the > character. A named channel is specified by using a channel type, followed by a : followed by a name. The channel types available are:

Here is an example that shows how you can use a named channel to share a data pipeline driven by different input sources.

Now if you post data to the http source, you will see that data intermingled with the time value in the file.

The opposite case, the fanout of a message to multiple streams, is planned for a future release. However, taps are a specialization of named channels that do allow publishing data to multiple sinks. For example:

Once data is received on mystream, it will be written to both file and log.

Support for routing messages to different streams based on message content is also planned for a future release.

Labels provide a means to alias or group modules. Labels are simply a name followed by a : When used as an alias a label can provide a more descriptive name for a particular configuration of a module and possibly something easier to refer to in other streams.

A module may have multiple labels:

When used for grouping a series of modules might share the same label:

Referring to the label group1 then effectively refers to all the labeled modules. This is not yet exploited in XD but in future may be used for something like configuring deployment options:

The Tuple class is a central data structure in Spring XD. It is an ordered list of values that can be retrieved by name or by index. Tuples are created by a TupleBuilder and are immutable. The values that are stored can be of any type and null values are allowed.

The underlying Message class that moves data from one processing step to the next can have an arbitrary data type as its payload. Instead of creating a custom Java class that encapsulates the properties of what is read or set in each processing step, the Tuple class can be used instead. Processing steps can be developed that read data from specific named values and write data to specific named values.

There are accessor methods that perform type conversion to the basic primitive types as well as BigDecimal and Date. This avoids you from having to cast the values to specific types. Insteam you can rely on the Tuple’s type conversion infastructure to perform the conversion.

The Tuple’s types conversion is performed by Spring’s Type Conversion Infrastructure which supports commonly encountered type conversions and is extensible.

There are several overloads for getters that let you provide default values for primitive types should the field you are looking for not be found. Date format patterns and Locale aware NumberFormat conversion are also supported. A best effort has been made to preserve the functionality available in Spring Batch’s FieldSet class that has been extensively used for parsing String based data in files.

In this section we will show a simple example on how to setup syslog ingestion from multiple hosts into HDFS.

Create the streams with syslog as source and HDFS as sink (Please refer to source and sink)

Please note for hdfs sink, set rollover parameter to a smaller value to avoid buffering and to see the data has made to HDFS (incase of smaller volume of log).

Configure the external hosts’ syslog daemons forward their messages to the xd-container host’s UDP/TCP port (where the syslog-udp/syslog-tcp source module is deployed).

Spring XD provides a browser-based GUI which currently allows you to perform Batch Job related tasks. Upon starting Spring XD, the Admin UI is available at:

http://localhost:9393/admin-ui/index.html

By selecting the provided tabs you can

Once a Job has been deployed, they can be launched through the Admin UI as well. Navigate to the Deployments tab. Select the job you want to launch and press Launch. The following modal dialog should appear:

Using this screen, you can define one or more job parameters. Job parameters can be typed and the following data types are available:

If you don’t have a local Hadoop cluster available already, you can do a local single node installation (v1.2.1) and use that to try out Hadoop with Spring XD.

You should now finally be ready to run Hadoop. Run the start-all.sh script

You should see five Hadoop Java processes running:

Try a few commands with hadoop dfs to make sure the basic system works

Lastly, you can also browse the web interface for NameNode and JobTracker at:

At this point you should be good to create a Spring XD stream using a Hadoop sink.

As outlined in the modules document, XD currently supports 3 types of modules: source, sink, and processor. This document walks through creation of a custom source module.

The first module in a stream is always a source. Source modules are built with Spring Integration and are typically very fine-grained. A module of type source is responsible for placing a message on a channel named output. This message can then be consumed by the other processor and sink modules in the stream. A source module is typically fed data by an inbound channel adapter, configured with a poller.

Spring Integration provides a number of adapters out of the box to support various transports, such as JMS, File, HTTP, Web Services, Mail, and more. You can typically create a source module that uses these inbound channel adapters by writing just a single Spring application context file.

These steps will demonstrate how to create and deploy a source module using the Spring Integration Feed Inbound Channel Adapter.

Create the Inbound Channel Adapter in a file called feed.xml:

The adapter is configured to poll the BBC News Feed every 5 seconds. Once an item is found, it will create a message with a SyndEntryImpl domain object payload and write it to a message channel called output. The name output should be used by convention so that your source module can easily be combined with any processor and sink module in a stream.

This section covers setup of a local project containing some code for testing outside of an XD container. This step can be skipped if you prefer to test the module by deploying to Spring XD.

Spring XD looks for modules in the ${xd.home}/modules directory. The modules directory organizes module types in sub-directories. So you will see something like:

Simply drop feed.xml into the modules/source directory and add the dependencies to the lib directory. For now, all module dependencies need to be added to ${xd.home}/lib. Future versions of Spring XD will provide a more elegant module packaging approach. Copy the following jars from your gradle cache to ${xd.home}/lib:

Now fire up the server. See Getting Started to learn how to start the Spring XD server.

Once the XD server is running, create a stream to test it out. This stream will write SyndEntry objects to the XD log:

You should start seeing messages like the following in the container console window:

As you can see, the SyndEntryImpl toString is fairly verbose. To make the output more concise, create a processor module to further transform the SyndEntry or consider converting the entry to JSON and using the JSON Field Extractor to send a single attribute value to the output channel.

As outlined in the modules document, XD currently supports 3 types of modules: source, sink, and processor. This document walks through creation of a custom processor module.

One or more processors can be included in a stream definition to modifythe data as it passes between the inital source and the destination sink. The architecture section covers the basics of processors modules provided out of the box are covered in the processors section.

Here we’ll look at how to create and deploy a custom processor module to transform the input from an incoming twittersearch. The steps are essentially the same for any source though. Rather than using built-in functionality, we’ll write a custom processor implementation class and wire it up using Spring Integration.

The tweet messages from twittersearch contain quite a lot of data (id, author, time and so on). The transformer we’ll write will discard everything but the text content and output this as a string. The output messages from the twittersearch source are also strings, containing the tweet data as JSON. We first parse this into a map using Jackson library code, then extract the "text" field from the map.

Create the following file as tweettransformer.xml:

To deploy the module, you need to copy the tweettransformer.xml file to the ${xd.home}/modules/processors directory. We also need to make the custom module code available. Currently Spring XD looks for code in the jars it finds in the ${xd.home}/lib directory. So create a jar with the TweetTransformer class in it (and the correct package structure) and drop it into lib.

Start the XD server and try creating a stream to test your processor:

If you haven’t already used twittersearch, read the sources section for more details. This command should stream tweets to the file /tmp/xd/output/javatweets but, unlike the normal twittersearch output, you should just see the plain tweet text there, rather than the full JSON data.

As outlined in the modules document, XD currently supports 3 types of modules: source, sink, and processor. This document walks through creation of a custom sink module.

The last module in a stream is always a sink. Sink modules are built with Spring Integration and are typically very fine-grained. A module of type sink listens on a channel named input and is responsible for outputting received messages to an external resource to terminate the stream.

Spring Integration provides a number of adapters out of the box to support various transports, such as JMS, File, HTTP, Web Services, Mail, and more. You can typically create a sink module that uses these outbound channel adapters by writing just a single Spring application context file.

These steps will demonstrate how to create and deploy a sink module using the Spring Integration RedisStore Outbound Channel Adapter.

Create the Outbound Channel Adapter in a file called redis-store.xml:

The adapter is configured to listen on a channel named input. The name input should be used by convention so that your sink module will receive all messages sent in the stream. Once a message is received, it will write the payload to a Redis list with key myCollection. By default, the RedisStore Outbound Channel Adapter uses a bean named redisConnectionFactory to connect to the Redis server.

Users may want to specify a different Redis server or key to use for storing data. Spring XD will automatically make a PropertyPlaceholderConfigurer available to your application context. You can simply reference property names and users can then pass in values when creating a stream using the DSL

Now users can optionally pass key, hostname, and port property values on stream creation. If not present, the specified defaults will be used.

This section covers setup of a local project containing some code for testing outside of an XD container. This step can be skipped if you prefer to test the module by deploying to Spring XD.

Spring XD looks for modules in the ${xd.home}/modules directory. The modules directory organizes module types in sub-directories. So you will see something like:

Simply drop redis-store.xml into the modules/sink directory and fire up the server. See Getting Started to learn how to start the Spring XD server.

Once the XD server is running, create a stream to test it out. This stream will write tweets containing the word "java" to Redis as a JSON string:

Note that you need to have a consumer key and secret to use the twittersearch module. See the description in the streams section for more information.

Fire up the redis-cli and verify that tweets are being stored:

Each available module can expose metadata about the options it accepts. This is useful to enhance the user experience, and is the foundation to advanced features like contextual help and code completion.

For example, provided that the file source module has been enriched with options metadata (and it has), one can use the module info command in the shell to get information about the module:

For this to be available, module authors have to provide a little bit of extra information, known as "Module Options Metadata". That metadata can take two forms, depending on the needs of the module: one can either use the "simple" approach, or the "POJO" approach. If one does not need advanced features like profile activation, validation or options encapsulation, then the "simple" approach is sufficient.

To use the simple approach, simply create a file named <module>.properties right next to the <module>.xml file for your module.

To use advanced features such as profile activation driven by the values provided by the end user, one would need to leverage the "POJO" approach.

Instead of writing a properties file, you will need to write a custom java class that will hold the values at runtime. That class is also introspected to derive metadata about your module.

To provide a uniform user experience, it is better if your options metadata information adheres to the following style:

Here are some useful steps to build and run Spring XD.

To build all sub-projects and run tests for Spring XD:

To build and bundle the distribution of Spring XD

The above gradle task creates spring-xd-<version>.zip binary distribution archive and spring-xd-<version>-docs.zip documentation archive files under build/distributions. This will also create a build/dist/spring-xd directory which is the expanded version of the binary distribution archive.

To just create the Spring XD expanded binary distribution directory

The above gradle task creates the distribution directory under build/dist/spring-xd.

Once the binary distribution directory is created, please refer to Getting Started on how to run Spring XD.

If you would like to work with the Spring XD code in your IDE, please use the following project generation depending on the IDE you use:

For Eclipse/Spring Tool Suite

For IntelliJ IDEA

Then just import the project as an existing project.

In order to run the UI tests, phantomjs must be installed on your system. On a Mac using brew execute:

Alternatively, you can also install phantomjs using the Node Package Manager:

Afterwards, execute:

As a result you should see console output similar to the following: