To simplify configuration, SHDP provides a dedicated namespace for most of its components. However, one can opt to configure the beans directly through the usual <bean> definition. For more information about XML Schema-based configuration in Spring, see this appendix in the Spring Framework reference documentation.

To use the SHDP namespace, one just needs to import it inside the configuration:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
   xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns:hdp="http://www.springframework.org/schema/hadoop"
   xsi:schemaLocation="
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd">

   <bean id ... >

   <hdp:configuration ...>
</beans>

Once imported, the namespace elements can be declared simply by using the aforementioned prefix. Note that is possible to change the default namespace, for example from <beans> to <hdp>. This is useful for configuration composed mainly of Hadoop components as it avoids declaring the prefix. To achieve this, simply swap the namespace prefix declarations above:

<?xml version="1.0" encoding="UTF-8"?>
<beans:beans xmlns="http://www.springframework.org/schema/hadoop"
   xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
   xmlns:beans="http://www.springframework.org/schema/beans"
   xsi:schemaLocation="
    http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd">
	    
    <beans:bean id ... >
	
    <configuration ...>
	
</beans:beans>

For the remainder of this doc, to improve readability, the XML examples may simply refer to the <hdp> namespace without the namespace declaration, where possible.

In order to use Hadoop, one needs to first configure it namely by creating a Configuration object. The configuration holds information about the job tracker, the input, output format and the various other parameters of the map reduce job.

In its simplest form, the configuration definition is a one liner:

<hdp:configuration />

The declaration above defines a Configuration bean (to be precise a factory bean of type ConfigurationFactoryBean) named, by default, hadoopConfiguration. The default name is used, by conventions, by the other elements that require a configuration - this leads to simple and very concise configurations as the main components can automatically wire themselves up without requiring any specific configuration.

For scenarios where the defaults need to be tweaked, one can pass in additional configuration files:

<hdp:configuration resources="classpath:/custom-site.xml, classpath:/hq-site.xml">

In this example, two additional Hadoop configuration resources are added to the configuration.

	Note
	Note that the configuration makes use of Spring's Resource abstraction to locate the file. This allows various search patterns to be used, depending on the running environment or the prefix specified (if any) by the value - in this example the classpath is used.

In addition to referencing configuration resources, one can tweak Hadoop settings directly through Java Properties. This can be quite handy when just a few options need to be changed:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:hdp="http://www.springframework.org/schema/hadoop"
	xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
        http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd">
        
     <hdp:configuration>
        fs.default.name=hdfs://localhost:9000
        hadoop.tmp.dir=/tmp/hadoop
        electric=sea
     </hdp:configuration>
</beans>

One can further customize the settings by avoiding the so called hard-coded values by externalizing them so they can be replaced at runtime, based on the existing environment without touching the configuration:

	Note
	Usual configuration parameters for fs.default.name, fs.defaultFS, mapred.job.tracker and yarn.resourcemanager.address can be configured using tag attributes file-system-uri, job-tracker-uri and rm-manager-uri respectively.

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:hdp="http://www.springframework.org/schema/hadoop"
    xmlns:context="http://www.springframework.org/schema/context"
	xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
        http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
        http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd">
        
     <hdp:configuration>
        fs.default.name=${hd.fs}
        hadoop.tmp.dir=file://${java.io.tmpdir}
        hangar=${number:18}
     </hdp:configuration>
     
     <context:property-placeholder location="classpath:hadoop.properties" />     
</beans>

Through Spring's property placeholder support, SpEL and the environment abstraction (available in Spring 3.1). one can externalize environment specific properties from the main code base easing the deployment across multiple machines. In the example above, the default file system is replaced based on the properties available in hadoop.properties while the temp dir is determined dynamically through SpEL. Both approaches offer a lot of flexbility in adapting to the running environment - in fact we use this approach extensivly in the Spring for Apache Hadoop test suite to cope with the differences between the different development boxes and the CI server.

Additionally, external Properties files can be loaded, Properties beans (typically declared through Spring's util namespace). Along with the nested properties declaration, this allows customized configurations to be easily declared:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
    xmlns:hdp="http://www.springframework.org/schema/hadoop"
    xmlns:context="http://www.springframework.org/schema/context"
    xmlns:util="http://www.springframework.org/schema/util"
    xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
        http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
        http://www.springframework.org/schema/util http://www.springframework.org/schema/util/spring-util.xsd
        http://www.springframework.org/schema/hadoop http://www.springframework.org/schema/hadoop/spring-hadoop.xsd">

   <!-- merge the local properties, the props bean and the two properties files -->        
   <hdp:configuration properties-ref="props" properties-location="cfg-1.properties, cfg-2.properties">
      star=chasing
      captain=eo
   </hdp:configuration>
     
   <util:properties id="props" location="props.properties"/>     
</beans>

When merging several properties, ones defined locally win. In the example above the configuration properties are the primary source, followed by the props bean followed by the external properties file based on their defined order. While it's not typical for a configuration to refer to so many properties, the example showcases the various options available.

	Note
	For more properties utilities, including using the System as a source or fallback, or control over the merging order, consider using Spring's PropertiesFactoryBean (which is what Spring for Apache Hadoop and util:properties use underneath).

It is possible to create configurations based on existing ones - this allows one to create dedicated configurations, slightly different from the main ones, usable for certain jobs (such as streaming - more on that below). Simply use the configuration-ref attribute to refer to the parent configuration - all its properties will be inherited and overridden as specified by the child:

<!-- default name is 'hadoopConfiguration' -->
<hdp:configuration>
    fs.default.name=${hd.fs}
    hadoop.tmp.dir=file://${java.io.tmpdir}
</hdp:configuration>
     
<hdp:configuration id="custom" configuration-ref="hadoopConfiguration">
    fs.default.name=${custom.hd.fs}
</hdp:configuration>     

...

Make sure though that you specify a different name since otherwise, because both definitions will have the same name, the Spring container will interpret this as being the same definition (and will usually consider the last one found).

Another option worth mentioning is register-url-handler which, as the name implies, automatically registers an URL handler in the running VM. This allows urls referrencing hdfs resource (by using the hdfs prefix) to be properly resolved - if the handler is not registered, such an URL will throw an exception since the VM does not know what hdfs means.

	Note
	Since only one URL handler can be registered per VM, at most once, this option is turned off by default. Due to the reasons mentioned before, once enabled if it fails, it will log the error but will not throw an exception. If your hdfs URLs stop working, make sure to investigate this aspect.

Last but not least a reminder that one can mix and match all these options to her preference. In general, consider externalizing Hadoop configuration since it allows easier updates without interfering with the application configuration. When dealing with multiple, similar configurations use configuration composition as it tends to keep the definitions concise, in sync and easy to update.

Once the Hadoop configuration is taken care of, one needs to actually submit some work to it. SHDP makes it easy to configure and run Hadoop jobs whether they are vanilla map-reduce type or streaming. Let us start with an example:

<hdp:job id="mr-job" 
  input-path="/input/" output-path="/ouput/"
  mapper="org.apache.hadoop.examples.WordCount.TokenizerMapper"
  reducer="org.apache.hadoop.examples.WordCount.IntSumReducer"/>

The declaration above creates a typical Hadoop Job: specifies its input and output, the mapper and the reducer classes. Notice that there is no reference to the Hadoop configuration above - that's because, if not specified, the default naming convention (hadoopConfiguration) will be used instead. Neither is there to the key or value types - these two are automatically determined through a best-effort attempt by analyzing the class information of the mapper and the reducer. Of course, these settings can be overridden: the former through the configuration-ref element, the latter through key and value attributes. There are plenty of options available not shown in the example (for simplicity) such as the jar (specified directly or by class), sort or group comparator, the combiner, the partitioner, the codecs to use or the input/output format just to name a few - they are supported, just take a look at the SHDP schema (Appendix C, Spring for Apache Hadoop Schema) or simply trigger auto-completion (usually CTRL+SPACE) in your IDE; if it supports XML namespaces and is properly configured it will display the available elements. Additionally one can extend the default Hadoop configuration object and add any special properties not available in the namespace or its backing bean (JobFactoryBean).

It is worth pointing out that per-job specific configurations are supported by specifying the custom properties directly or referring to them (more information on the pattern is available here):

<hdp:job id="mr-job" 
  input-path="/input/" output-path="/ouput/"
  mapper="mapper class" reducer="reducer class"
  jar-by-class="class used for jar detection"
  properties-location="classpath:special-job.properties">
    electric=sea
</hdp:job>

<hdp:job> provides additional properties, such as the generic options, however one that is worth mentioning is jar which allows a job (and its dependencies) to be loaded entirely from a specified jar. This is useful for isolating jobs and avoiding classpath and versioning collisions. Note that provisioning of the jar into the cluster still depends on the target environment - see the aforementioned section for more info (such as libs).

<hdp:streaming id="streaming" 
  input-path="/input/" output-path="/ouput/"
  mapper="${path.cat}" reducer="${path.wc}"/>

<hdp:streaming id="streaming-env" 
  input-path="/input/" output-path="/ouput/"
  mapper="${path.cat}" reducer="${path.wc}">
  <hdp:cmd-env>
     EXAMPLE_DIR=/home/example/dictionaries/
     ...
  </hdp:cmd-env>
</hdp:streaming>

The jobs, after being created and configured, need to be submitted for execution to a Hadoop cluster. For non-trivial cases, a coordinating, workflow solution such as Spring Batch is recommended . However for basic job submission SHDP provides the job-runner element (backed by JobRunner class) which submits several jobs sequentially (and waits by default for their completion):

<hdp:job-runner id="myjob-runner" pre-action="cleanup-script" post-action="export-results" job-ref="myjob" run-at-startup="true"/>

<hdp:job id="myjob"  input-path="/input/" output-path="/output/"
	mapper="org.apache.hadoop.examples.WordCount.TokenizerMapper"
	reducer="org.apache.hadoop.examples.WordCount.IntSumReducer" />

Multiple jobs can be specified and even nested if they are not used outside the runner:

<hdp:job-runner id="myjobs-runner" pre-action="cleanup-script" job-ref="myjob1, myjob2" run-at-startup="true"/>
 	
<hdp:job id="myjob1"  ... />
<hdp:streaming id="myjob2"  ... />

One or multiple Map-Reduce jobs can be specified through the job attribute in the order of the execution. The runner will trigger the execution during the application start-up (notice the run-at-startup flag which is by default false). Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. Additionally the runner (as in fact do all runners in SHDP) allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. For more information on runners, see the dedicated chapter.

Note

As the Hadoop job submission and execution (when wait-for-completion is true) is blocking, JobRunner uses a JDK Executor to start (or stop) a job. The default implementation, SyncTaskExecutor uses the calling thread to execute the job, mimicking the hadoop command line behaviour. However, as the hadoop jobs are time-consuming, in some cases this can lead to “application freeze”, preventing normal operations or even application shutdown from occuring properly. Before going into production, it is recommended to double-check whether this strategy is suitable or whether a throttled or pooled implementation is better. One can customize the behaviour through the executor-ref parameter.

The job runner also allows running jobs to be cancelled (or killed) at shutdown. This applies only to jobs that the runner waits for (wait-for-completion is true) using a different executor then the default - that is, using a different thread then the calling one (since otherwise the calling thread has to wait for the job to finish first before executing the next task). To customize this behaviour, one should set the kill-job-at-shutdown attribute to false and/or change the executor-ref implementation.

<hdp:job-tasklet id="hadoop-tasklet" job-ref="mr-job" wait-for-completion="true" />

It is common for Hadoop utilities and libraries to be started from the command-line (ex: hadoop jar some.jar). SHDP offers generic support for such cases provided that the packages in question are built on top of Hadoop standard infrastructure, namely Tool and ToolRunner classes. As opposed to the command-line usage, Tool instances benefit from Spring's IoC features; they can be parameterized, created and destroyed on demand and have their properties (such as the Hadoop configuration) injected.

Consider the typical jar example - invoking a class with some (two in this case) arguments (notice that the Hadoop configuration properties are passed as well):

bin/hadoop jar -conf hadoop-site.xml -jt darwin:50020 -Dproperty=value someJar.jar org.foo.SomeTool data/in.txt data/out.txt

Since SHDP has first-class support for configuring Hadoop, the so called generic options aren't needed any more, even more so since typically there is only one Hadoop configuration per application. Through tool-runner element (and its backing ToolRunner class) one typically just needs to specify the Tool implementation and its arguments:

<hdp:tool-runner id="someTool" tool-class="org.foo.SomeTool" run-at-startup="true">
   <hdp:arg value="data/in.txt"/>
   <hdp:arg value="data/out.txt"/>
   
   property=value
</hdp:tool-runner>

Additionally the runner (just like the job runner) allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. For more information on runners, see the dedicated chapter.

The previous example assumes the Tool dependencies (such as its class) are available in the classpath. If that is not the case, tool-runner allows a jar to be specified:

<hdp:tool-runner ... jar="myTool.jar">
    ...
</hdp:tool-runner>

The jar is used to instantiate and start the tool - in fact all its dependencies are loaded from the jar meaning they no longer need to be part of the classpath. This mechanism provides proper isolation between tools as each of them might depend on certain libraries with different versions; rather then adding them all into the same app (which might be impossible due to versioning conflicts), one can simply point to the different jars and be on her way. Note that when using a jar, if the main class (as specified by the Main-Class entry) is the target Tool, one can skip specifying the tool as it will picked up automatically.

Like the rest of the SHDP elements, tool-runner allows the passed Hadoop configuration (by default hadoopConfiguration but specified in the example for clarity) to be customized accordingly; the snippet only highlights the property initialization for simplicity but more options are available. Since usually the Tool implementation has a default argument, one can use the tool-class attribute. However it is possible to refer to another Tool instance or declare a nested one:

<hdp:tool-runner id="someTool" run-at-startup="true">
   <hdp:tool>
      <bean class="org.foo.AnotherTool" p:input="data/in.txt" p:output="data/out.txt"/>
   </hdp:tool>
</hdp:tool-runner>

This is quite convenient if the Tool class provides setters or richer constructors. Note that by default the tool-runner does not execute the Tool until its definition is actually called - this behavior can be changed through the run-at-startup attribute above.

hadoop jar job1.jar -files fullpath:props.properties -Dconfig=config.properties ...
hadoop jar job2.jar arg1 arg2...
...
hadoop jar job10.jar ...

<hdp:tool-runner id="job1" tool-class="job1.Tool" jar="job1.jar" files="fullpath:props.properties" properties-location="config.properties"/>
<hdp:tool-runner id="job2" jar="job2.jar">
   <hdp:arg value="arg1"/>
   <hdp:arg value="arg2"/>
</hdp:tool-runner>
<hdp:tool-runner id="job3" jar="job3.jar"/>
...

<hdp:tool-tasklet id="tool-tasklet" tool-ref="some-tool" />

SHDP also provides support for executing vanilla Hadoop jars. Thus the famous WordCount example:

bin/hadoop jar hadoop-examples.jar wordcount /wordcount/input /wordcount/output

becomes

<hdp:jar-runner id="wordcount" jar="hadoop-examples.jar" run-at-startup="true">
    <hdp:arg value="wordcount"/>
    <hdp:arg value="/wordcount/input"/>
    <hdp:arg value="/wordcount/output"/>
</hdp:jar-runner>

	Note
	Just like the hadoop jar command, by default the jar support reads the jar's Main-Class if none is specified. This can be customized through the main-class attribute.

Additionally the runner (just like the job runner) allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. For more information on runners, see the dedicated chapter.

The jar support provides a nice and easy migration path from jar invocations from the command-line to SHDP (note that Hadoop generic options are also supported). Especially since SHDP enables Hadoop Configuration objects, created during the jar execution, to automatically inherit the context Hadoop configuration. In fact, just like other SHDP elements, the jar element allows configurations properties to be declared locally, just for the jar run. So for example, if one would use the following declaration:

<hdp:jar-runner id="wordcount" jar="hadoop-examples.jar" run-at-startup="true">
    <hdp:arg value="wordcount"/>
    ...
    speed=fast
</hdp:jar-runner>

inside the jar code, one could do the following:

assert "fast".equals(new Configuration().get("speed"));

This enabled basic Hadoop jars to use, without changes, the enclosing application Hadoop configuration.

And while we think it is a useful feature (that is why we added it in the first place), we strongly recommend using the tool support instead or migrate to it; there are several reasons for this mainly because there are no contracts to use, leading to very poor embeddability caused by:

SHDP tries to use sensible defaults to provide the best integration experience possible but at the end of the day, without any contract in place, there are no guarantees. Hence using the Tool interface is a much better alternative.

<hdp:jar-tasklet id="jar-tasklet" jar="some-jar.jar" />

DistributedCache is a Hadoop facility for distributing application-specific, large, read-only files (text, archives, jars and so on) efficiently. Applications specify the files to be cached via urls (hdfs://) using DistributedCache and the framework will copy the necessary files to the slave nodes before any tasks for the job are executed on that node. Its efficiency stems from the fact that the files are only copied once per job and the ability to cache archives which are un-archived on the slaves. Note that DistributedCache assumes that the files to be cached (and specified via hdfs:// urls) are already present on the Hadoop FileSystem.

SHDP provides first-class configuration for the distributed cache through its cache element (backed by DistributedCacheFactoryBean class), allowing files and archives to be easily distributed across nodes:

<hdp:cache create-symlink="true">
   <hdp:classpath value="/cp/some-library.jar#library.jar" />
   <hdp:cache value="/cache/some-archive.tgz#main-archive" />
   <hdp:cache value="/cache/some-resource.res" />
   <hdp:local value="some-file.txt" />
</hdp:cache>

The definition above registers several resources with the cache (adding them to the job cache or classpath) and creates symlinks for them. As described in the DistributedCache documentation, the declaration format is (absolute-path#link-name). The link name is determined by the URI fragment (the text following the # such as #library.jar or #main-archive above) - if no name is specified, the cache bean will infer one based on the resource file name. Note that one does not have to specify the hdfs://node:port prefix as these are automatically determined based on the configuration wired into the bean; this prevents environment settings from being hard-coded into the configuration which becomes portable. Additionally based on the resource extension, the definition differentiates between archives (.tgz, .tar.gz, .zip and .tar) which will be uncompressed, and regular files that are copied as-is. As with the rest of the namespace declarations, the definition above relies on defaults - since it requires a Hadoop Configuration and FileSystem objects and none are specified (through configuration-ref and file-system-ref) it falls back to the default naming and is wired with the bean named hadoopConfiguration, creating the FileSystem automatically.

Warning

Clients setting up a classpath in the DistributedCache, running on Windows platforms should set the System path.separator property to :. Otherwise the classpath will be set incorrectly and will be ignored; see HADOOP-9123 bug report for more information. There are multiple ways to change the path.separator System property - a quick one being a simple script in Javascript (that uses the Rhino package bundled with the JDK) that runs at start-up: <hdp:script language="javascript" run-at-startup="true"> // set System 'path.separator' to ':' - see HADOOP-9123 java.lang.System.setProperty("path.separator", ":") </hdp:script>

The job, streaming and tool all support a subset of generic options, specifically archives, files and libs. libs is probably the most useful as it enriches a job classpath (typically with some jars) - however the other two allow resources or archives to be copied throughout the cluster for the job to consume. Whenver faced with provisioning issues, revisit these options as they can help up significantly. Note that the fs, jt or conf options are not supported - these are designed for command-line usage, for bootstrapping the application. This is no longer needed, as the SHDP offers first-class support for defining and customizing Hadoop configurations.

The Hadoop file-system, HDFS, can be accessed in various ways - this section will cover the most popular protocols for interacting with HDFS and their pros and cons. SHDP does not enforce any specific protocol to be used - in fact, as described in this section any FileSystem implementation can be used, allowing even other implementations than HDFS to be used.

The table below describes the common HDFS APIs in use:

hdfs:// protocol should be familiar to most readers - most docs (and in fact the previous chapter as well) mention it. It works out of the box and it's fairly efficient. However because it is RPC based, it requires both the client and the Hadoop cluster to share the same version. Upgrading one without the other causes serialization errors meaning the client cannot interact with the cluster. As an alternative one can use hftp:// which is HTTP-based or its more secure brother hsftp:// (based on SSL) which gives you a version independent protocol meaning you can use it to interact with clusters with an unknown or different version than that of the client. hftp is read only (write operations will fail right away) and it is typically used with disctp for reading data. webhdfs:// is one of the additions in Hadoop 1.0 and is a mixture between hdfs and hftp protocol - it provides a version-independent, read-write, REST-based protocol which means that you can read and write to/from Hadoop clusters no matter their version. Furthermore, since webhdfs:// is backed by a REST API, clients in other languages can use it with minimal effort.

	Note
	Not all file systems work out of the box. For example WebHDFS needs to be enabled first in the cluster (through dfs.webhdfs.enabled property, see this document for more information) while the secure hftp, hsftp requires the SSL configuration (such as certificates) to be specified. More about this (and how to use hftp/hsftp for proxying) in this page.

Once the scheme has been decided upon, one can specify it through the standard Hadoop configuration, either through the Hadoop configuration files or its properties:

<hdp:configuration>
  fs.default.name=webhdfs://localhost
  ...
</hdp:configuration>

This instructs Hadoop (and automatically SHDP) what the default, implied file-system is. In SHDP, one can create additional file-systems (potentially to connect to other clusters) and specify a different scheme:

<!-- manually creates the default SHDP file-system named 'hadoopFs' -->
<hdp:file-system uri="webhdfs://localhost"/>
 
<!-- creates a different FileSystem instance --> 
<hdp:file-system id="old-cluster" uri="hftp://old-cluster/"/>

As with the rest of the components, the file systems can be injected where needed - such as file shell or inside scripts (see the next section).

In Spring the ResourceLoader interface is meant to be implemented by objects that can return (i.e. load) Resource instances.

public interface ResourceLoader {
  Resource getResource(String location);
}

All application contexts implement the ResourceLoader interface, and therefore all application contexts may be used to obtain Resource instances.

When you call getResource() on a specific application context, and the location path specified doesn't have a specific prefix, you will get back a Resource type that is appropriate to that particular application context. For example, assume the following snippet of code was executed against a ClassPathXmlApplicationContext instance:

Resource template = ctx.getResource("some/resource/path/myTemplate.txt");

What would be returned would be a ClassPathResource; if the same method was executed against a FileSystemXmlApplicationContext instance, you'd get back a FileSystemResource. For a WebApplicationContext, you'd get back a ServletContextResource, and so on.

As such, you can load resources in a fashion appropriate to the particular application context.

On the other hand, you may also force ClassPathResource to be used, regardless of the application context type, by specifying the special classpath: prefix:

Resource template = ctx.getResource("classpath:some/resource/path/myTemplate.txt");

	Note
	More information about the generic usage of resource loading, check the Spring Framework Documentation.

Spring Hadoop is adding its own functionality into generic concept of resource loading. Resource abstraction in Spring has always been a way to ease resource access in terms of not having a need to know where there resource is and how it's accessed. This abstraction also goes beyond a single resource by allowing to use patterns to access multiple resources.

Lets first see how HdfsResourceLoader is used manually.

<hdp:file-system />
<hdp:resource-loader id="loader" file-system-ref="hadoopFs" />
<hdp:resource-loader id="loaderWithUser" user="myuser" uri="hdfs://localhost:8020" />

In above configuration we created two beans, one with reference to existing Hadoop FileSystem bean and one with impersonated user.

// get path '/tmp/file.txt'
Resource resource = loader.getResource("/tmp/file.txt");
// get path '/tmp/file.txt' with user impersonation
Resource resource = loaderWithUser.getResource("/tmp/file.txt");

// get path '/user/<current user>/file.txt'
Resource resource = loader.getResource("file.txt");
// get path '/user/myuser/file.txt'
Resource resource = loaderWithUser.getResource("file.txt");

// get all paths under '/tmp/'
Resource[] resources = loader.getResources("/tmp/*");
// get all paths under '/tmp/' recursively
Resource[] resources = loader.getResources("/tmp/**/*");
// get all paths under '/tmp/' using more complex ant path matching
Resource[] resources = loader.getResources("/tmp/?ile?.txt");

What would be returned in above examples would be instances of HdfsResources.

If there is a need for Spring Application Context to be aware of HdfsResourceLoader it needs to be registered using hdp:resource-loader-registrar namespace tag.

<hdp:file-system />
<hdp:resource-loader file-system-ref="hadoopFs" handle-noprefix="false" />
<hdp:resource-loader-registrar />

	Note
	On default the HdfsResourceLoader will handle all resource paths without prefix. Attribute handle-noprefix can be used to control this behaviour. If this attribute is set to false, non-prefixed resource uris will be handled by Spring Application Context.

// get 'default.txt' from current user's home directory
Resource[] resources = context.getResources("hdfs:default.txt");
// get all files from hdfs root
Resource[] resources = context.getResources("hdfs:/*");
// let context handle classpath prefix
Resource[] resources = context.getResources("classpath:cfg*properties");

What would be returned in above examples would be instances of HdfsResources and ClassPathResource for the last one. If requesting resource paths without existing prefix, this example would fall back into Spring Application Context. It may be advisable to let HdfsResourceLoader to handle paths without prefix if your application doesn't rely on loading resources from underlying context without prefixes.

Since Hadoop is written in Java, accessing its APIs in a native way provides maximum control and flexibility over the interaction with Hadoop. This holds true for working with its file systems; in fact all the other tools that one might use are built upon these. The main entry point is the org.apache.hadoop.fs.FileSystem abstract class which provides the foundation of most (if not all) of the actual file system implementations out there. Whether one is using a local, remote or distributed store through the FileSystem API she can query and manipulate the available resources or create new ones. To do so however, one needs to write Java code, compile the classes and configure them which is somewhat cumbersome especially when performing simple, straightforward operations (like copy a file or delete a directory).

JVM scripting languages (such as Groovy, JRuby, Jython or Rhino to name just a few) provide a nice solution to the Java language; they run on the JVM, can interact with the Java code with no or few changes or restrictions and have a nicer, simpler, less ceremonial syntax; that is, there is no need to define a class or a method - simply write the code that you want to execute and you are done. SHDP combines the two, taking care of the configuration and the infrastructure so one can interact with the Hadoop environment from her language of choice.

Let us take a look at a JavaScript example using Rhino (which is part of JDK 6 or higher, meaning one does not need any extra libraries):

<beans xmlns="http://www.springframework.org/schema/beans" ...>		
  <hdp:configuration .../>
		
  <hdp:script id="inlined-js" language="javascript" run-at-startup="true">
    try {load("nashorn:mozilla_compat.js");} catch (e) {} // for Java 8
    importPackage(java.util);

    name = UUID.randomUUID().toString()
    scriptName = "src/test/resources/test.properties"
    // fs - FileSystem instance based on 'hadoopConfiguration' bean
    // call FileSystem#copyFromLocal(Path, Path)  
    fs.copyFromLocalFile(scriptName, name)
    // return the file length 
    fs.getLength(name)
  </hdp:script>
	 
</beans>

The script element, part of the SHDP namespace, builds on top of the scripting support in Spring permitting script declarations to be evaluated and declared as normal bean definitions. Furthermore it automatically exposes Hadoop-specific objects, based on the existing configuration, to the script such as the FileSystem (more on that in the next section). As one can see, the script is fairly obvious: it generates a random name (using the UUID class from java.util package) and then copies a local file into HDFS under the random name. The last line returns the length of the copied file which becomes the value of the declaring bean (in this case inlined-js) - note that this might vary based on the scripting engine used.

	Note
	The attentive reader might have noticed that the arguments passed to the FileSystem object are not of type Path but rather String. To avoid the creation of Path object, SHDP uses a wrapper class (SimplerFileSystem) which automatically does the conversion so you don't have to. For more information see the implicit variables section.

Note that for inlined scripts, one can use Spring's property placeholder configurer to automatically expand variables at runtime. Using one of the examples seen before:

<beans ... >
  <context:property-placeholder location="classpath:hadoop.properties" />
   
  <hdp:script language="javascript" run-at-startup="true">
    ...
    tracker=${hd.fs}
    ...
  </hdp:script>
</beans>

Notice how the script above relies on the property placeholder to expand ${hd.fs} with the values from hadoop.properties file available in the classpath.

As you might have noticed, the script element defines a runner for JVM scripts. And just like the rest of the SHDP runners, it allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. For more information on runners, see the dedicated chapter.

<hdp:script location="org/company/basic-script.py" run-at-startup="true"/>

from java.util import UUID
from org.apache.hadoop.fs import Path

print "Home dir is " + str(fs.homeDirectory)
print "Work dir is " + str(fs.workingDirectory)
print "/user exists " + str(fs.exists("/user"))

name = UUID.randomUUID().toString()
scriptName = "src/test/resources/test.properties"
fs.copyFromLocalFile(scriptName, name)
print Path(name).makeQualified(fs)

To ease the interaction of the script with its enclosing context, SHDP binds by default the so-called implicit variables. These are:

	Note
	If no Hadoop Configuration can be detected (either by name hadoopConfiguration or by type), several log warnings will be made and none of the Hadoop-based variables (namely cfg, distcp, fs, fsh, distcp or hdfsRL) will be bound.

As mentioned in the Description column, the variables are first looked (either by name or by type) in the application context and, in case they are missing, created on the spot based on the existing configuration. Note that it is possible to override or add new variables to the scripts through the property sub-element that can set values or references to other beans:

<hdp:script location="org/company/basic-script.js" run-at-startup="true">
   <hdp:property name="foo" value="bar"/>
   <hdp:property name="ref" ref="some-bean"/>
</hdp:script>

<script-tasklet id="script-tasklet">
  <script language="groovy">
    inputPath = "/user/gutenberg/input/word/"
    outputPath = "/user/gutenberg/output/word/"
    if (fsh.test(inputPath)) {
      fsh.rmr(inputPath)
    }
    if (fsh.test(outputPath)) {
      fsh.rmr(outputPath)
    }
    inputFile = "src/main/resources/data/nietzsche-chapter-1.txt"
    fsh.put(inputFile, inputPath)
  </script>
</script-tasklet>

<script-tasklet id="script-tasklet" script-ref="clean-up"/>
	<hdp:script id="clean-up" location="org/company/myapp/clean-up-wordcount.groovy"/>

A handy utility provided by the Hadoop distribution is the file system shell which allows UNIX-like commands to be executed against HDFS. One can check for the existence of files, delete, move, copy directories or files or set up permissions. However the utility is only available from the command-line which makes it hard to use from/inside a Java application. To address this problem, SHDP provides a lightweight, fully embeddable shell, called FsShell which mimics most of the commands available from the command line: rather than dealing with System.in or System.out, one deals with objects.

Let us take a look at using FsShell by building on the previous scripting examples:

<hdp:script location="org/company/basic-script.groovy" run-at-startup="true"/>

name = UUID.randomUUID().toString()
scriptName = "src/test/resources/test.properties"
fs.copyFromLocalFile(scriptName, name)

// use the shell made available under variable fsh
dir = "script-dir"
if (!fsh.test(dir)) {
   fsh.mkdir(dir); fsh.cp(name, dir); fsh.chmodr(700, dir)
   println "File content is " + fsh.cat(dir + name).toString()
}
println fsh.ls(dir).toString()
fsh.rmr(dir)

As mentioned in the previous section, a FsShell instance is automatically created and configured for scripts, under the name fsh. Notice how the entire block relies on the usual commands: test, mkdir, cp and so on. Their semantics are exactly the same as in the command-line version however one has access to a native Java API that returns actual objects (rather than Strings) making it easy to use them programmatically whether in Java or another language. Furthermore, the class offers enhanced methods (such as chmodr which stands for recursive chmod) and multiple overloaded methods taking advantage of varargs so that multiple parameters can be specified. Consult the API for more information.

To be as close as possible to the command-line shell, FsShell mimics even the messages being displayed. Take a look at line 9 which prints the result of fsh.cat(). The method returns a Collection of Hadoop Path objects (which one can use programatically). However when invoking toString on the collection, the same printout as from the command-line shell is being displayed:

File content is some text

The same goes for the rest of the methods, such as ls. The same script in JRuby would look something like this:

require 'java'
name = java.util.UUID.randomUUID().to_s
scriptName = "src/test/resources/test.properties"
$fs.copyFromLocalFile(scriptName, name)

# use the shell
dir = "script-dir/"
...
print $fsh.ls(dir).to_s

which prints out something like this:

drwx------   - user     supergroup          0 2012-01-26 14:08 /user/user/script-dir
-rw-r--r--   3 user     supergroup        344 2012-01-26 14:08 /user/user/script-dir/520cf2f6-a0b6-427e-a232-2d5426c2bc4e

As you can see, not only can you reuse the existing tools and commands with Hadoop inside SHDP, but you can also code against them in various scripting languages. And as you might have noticed, there is no special configuration required - this is automatically inferred from the enclosing application context.

Note

The careful reader might have noticed that besides the syntax, there are some minor differences in how the various languages interact with the java objects. For example the automatic toString call called in Java for doing automatic String conversion is not necessarily supported (hence the to_s in Ruby or str in Python). This is to be expected as each language has its own semantics - for the most part these are easy to pick up but do pay attention to details.

<hdp:script language="groovy">distcp.copy("${distcp.src}", "${distcp.dst}")</hdp:script>

One of the most popular and powerful feature in Spring Framework is the Data Access Object (or DAO) support. It makes dealing with data access technologies easy and consistent allowing easy switch or interconnection of the aforementioned persistent stores with minimal friction (no worrying about catching exceptions, writing boiler-plate code or handling resource acquisition and disposal). Rather than reiterating here the value proposal of the DAO support, we recommend the DAO section in the Spring Framework reference documentation

SHDP provides the same functionality for Apache HBase through its org.springframework.data.hadoop.hbase package: an HbaseTemplate along with several callbacks such as TableCallback, RowMapper and ResultsExtractor that remove the low-level, tedious details for finding the HBase table, run the query, prepare the scanner, analyze the results then clean everything up, letting the developer focus on her actual job (users familiar with Spring should find the class/method names quite familiar).

At the core of the DAO support lies HbaseTemplate - a high-level abstraction for interacting with HBase. The template requires an HBase configuration, once it's set, the template is thread-safe and can be reused across multiple instances at the same time:

// default HBase configuration
<hdp:hbase-configuration/>

// wire hbase configuration (using default name 'hbaseConfiguration') into the template 
<bean id="htemplate" class="org.springframework.data.hadoop.hbase.HbaseTemplate" p:configuration-ref="hbaseConfiguration"/>

The template provides generic callbacks, for executing logic against the tables or doing result or row extraction, but also utility methods (the so-called one-liners) for common operations. Below are some examples of how the template usage looks like:

// writing to 'MyTable'
template.execute("MyTable", new TableCallback<Object>() {
  @Override
  public Object doInTable(HTable table) throws Throwable {
    Put p = new Put(Bytes.toBytes("SomeRow"));
    p.add(Bytes.toBytes("SomeColumn"), Bytes.toBytes("SomeQualifier"), Bytes.toBytes("AValue"));
    table.put(p);
    return null;
  }
});

// read each row from 'MyTable'
List<String> rows = template.find("MyTable", "SomeColumn", new RowMapper<String>() {
  @Override
  public String mapRow(Result result, int rowNum) throws Exception {
    return result.toString();
  }
}));

The first snippet showcases the generic TableCallback - the most generic of the callbacks, it does the table lookup and resource cleanup so that the user code does not have to. Notice the callback signature - any exception thrown by the HBase API is automatically caught, converted to Spring's DAO exceptions and resource clean-up applied transparently. The second example, displays the dedicated lookup methods - in this case find which, as the name implies, finds all the rows matching the given criteria and allows user code to be executed against each of them (typically for doing some sort of type conversion or mapping). If the entire result is required, then one can use ResultsExtractor instead of RowMapper.

Besides the template, the package offers support for automatically binding HBase table to the current thread through HbaseInterceptor and HbaseSynchronizationManager. That is, each class that performs DAO operations on HBase can be wrapped by HbaseInterceptor so that each table in use, once found, is bound to the thread so any subsequent call to it avoids the lookup. Once the call ends, the table is automatically closed so there is no leakage between requests. Please refer to the Javadocs for more information.

SHDP provides a dedicated namespace element for starting a Hive server as a Thrift service (only when using Hive 0.8 or higher). Simply specify the host, the port (the defaults are localhost and 10000 respectively) and you're good to go:

<!-- by default, the definition name is 'hive-server' -->
<hdp:hive-server host="some-other-host" port="10001" />

If needed the Hadoop configuration can be passed in or additional properties specified. In fact hiver-server provides the same properties configuration knobs as hadoop configuration:

<hdp:hive-server host="some-other-host" port="10001" properties-location="classpath:hive-dev.properties" configuration-ref="hadoopConfiguration">
  someproperty=somevalue
  hive.exec.scratchdir=/tmp/mydir
</hdp:hive-server>

The Hive server is bound to the enclosing application context life-cycle, that is it will automatically startup and shutdown along-side the application context.

Similar to the server, SHDP provides a dedicated namespace element for configuring a Hive client (that is Hive accessing a server node through the Thrift). Likewise, simply specify the host, the port (the defaults are localhost and 10000 respectively) and you're done:

<!-- by default, the definition name is 'hiveClientFactory' -->
<hdp:hive-client-factory host="some-other-host" port="10001" />

Note that since Thrift clients are not thread-safe, hive-client-factory returns a factory (named org.springframework.data.hadoop.hive.HiveClientFactory) for creating HiveClient new instances for each invocation. Furthermore, the client definition also allows Hive scripts (either declared inlined or externally) to be executed during initialization, once the client connects; this is quite useful for doing Hive specific initialization:

<hive-client-factory host="some-host" port="some-port" xmlns="http://www.springframework.org/schema/hadoop">
   <hdp:script>
     DROP TABLE IF EXITS testHiveBatchTable; 
     CREATE TABLE testHiveBatchTable (key int, value string);
   </hdp:script>
   <hdp:script location="classpath:org/company/hive/script.q">
       <arguments>ignore-case=true</arguments>
   </hdp:script>
</hive-client-factory>

In the example above, two scripts are executed each time a new Hive client is created (if the scripts need to be executed only once consider using a tasklet) by the factory. The first script is defined inline while the second is read from the classpath and passed one parameter. For more information on using parameters (or variables) in Hive scripts, see this section in the Hive manual.

Another attractive option for accessing Hive is through its JDBC driver. This exposes Hive through the JDBC API meaning one can use the standard API or its derived utilities to interact with Hive, such as the rich JDBC support in Spring Framework.

	Warning
	Note that the JDBC driver is a work-in-progress and not all the JDBC features are available (and probably never will since Hive cannot support all of them as it is not the typical relational database). Do read the official documentation and examples.

SHDP does not offer any dedicated support for the JDBC integration - Spring Framework itself provides the needed tools; simply configure Hive as you would with any other JDBC Driver:

<beans xmlns="http://www.springframework.org/schema/beans"
	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xmlns:c="http://www.springframework.org/schema/c"
	xmlns:context="http://www.springframework.org/schema/context"
	xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
      	http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd">
	
    <!-- basic Hive driver bean -->
    <bean id="hive-driver" class="org.apache.hadoop.hive.jdbc.HiveDriver"/>

    <!-- wrapping a basic datasource around the driver -->
    <!-- notice the 'c:' namespace (available in Spring 3.1+) for inlining constructor arguments, 
         in this case the url (default is 'jdbc:hive://localhost:10000/default') -->
    <bean id="hive-ds" class="org.springframework.jdbc.datasource.SimpleDriverDataSource"
       c:driver-ref="hive-driver" c:url="${hive.url}"/>

    <!-- standard JdbcTemplate declaration -->
    <bean id="template" class="org.springframework.jdbc.core.JdbcTemplate" c:data-source-ref="hive-ds"/>
	
    <context:property-placeholder location="hive.properties"/>
</beans>

And that is it! Following the example above, one can use the hive-ds DataSource bean to manually get a hold of Connections or better yet, use Spring's JdbcTemplate as in the example above.

Like the rest of the Spring Hadoop components, a runner is provided out of the box for executing Hive scripts, either inlined or from various locations through hive-runner element:

<hdp:hive-runner id="hiveRunner" run-at-startup="true">
   <hdp:script>
     DROP TABLE IF EXITS testHiveBatchTable; 
     CREATE TABLE testHiveBatchTable (key int, value string);
   </hdp:script>
   <hdp:script location="hive-scripts/script.q"/>
</hdp:hive-runner>

The runner will trigger the execution during the application start-up (notice the run-at-startup flag which is by default false). Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. Additionally the runner (as in fact do all runners in SHDP) allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. For more information on runners, see the dedicated chapter.

<hdp:hive-tasklet id="hive-script">
   <hdp:script>
     DROP TABLE IF EXITS testHiveBatchTable; 
     CREATE TABLE testHiveBatchTable (key int, value string);
   </hdp:script>
   <hdp:script location="classpath:org/company/hive/script.q" />
</hdp:hive-tasklet>

For those that need to programmatically interact with the Hive API, Spring for Apache Hadoop provides a dedicated template, similar to the aforementioned JdbcTemplate. The template handles the redundant, boiler-plate code, required for interacting with Hive such as creating a new HiveClient, executing the queries, catching any exceptions and performing clean-up. One can programmatically execute queries (and get the raw results or convert them to longs or ints) or scripts but also interact with the Hive API through the HiveClientCallback. For example:

<hdp:hive-client-factory ... />
<!-- Hive template wires automatically to 'hiveClientFactory'-->
<hdp:hive-template />
	
<!-- wire hive template into a bean -->
<bean id="someBean" class="org.SomeClass" p:hive-template-ref="hiveTemplate"/>

public class SomeClass {

  private HiveTemplate template;

  public void setHiveTemplate(HiveTemplate template) { this.template = template; }

  public List<String> getDbs() {
      return hiveTemplate.execute(new HiveClientCallback<List<String>>() {
         @Override
         public List<String> doInHive(HiveClient hiveClient) throws Exception {
            return hiveClient.get_all_databases();
         }
      }));
  }
}

The example above shows a basic container configuration wiring a HiveTemplate into a user class which uses it to interact with the HiveClient Thrift API. Notice that the user does not have to handle the lifecycle of the HiveClient instance or catch any exception (out of the many thrown by Hive itself and the Thrift fabric) - these are handled automatically by the template which converts them, like the rest of the Spring templates, into DataAccessExceptions. Thus the application only has to track only one exception hierarchy across all data technologies instead of one per technology.

Like the rest of the Spring Hadoop components, a runner is provided out of the box for executing Pig scripts, either inlined or from various locations through pig-runner element:

<hdp:pig-runner id="pigRunner" run-at-startup="true">
   <hdp:script>
		A = LOAD 'src/test/resources/logs/apache_access.log' USING PigStorage() AS (name:chararray, age:int);
		...
   </hdp:script>
   <hdp:script location="pig-scripts/script.pig"/>
</hdp:pig-runner>

The runner will trigger the execution during the application start-up (notice the run-at-startup flag which is by default false). Do note that the runner will not run unless triggered manually or if run-at-startup is set to true. Additionally the runner (as in fact do all runners in SHDP) allows one or multiple pre and post actions to be specified to be executed before and after each run. Typically other runners (such as other jobs or scripts) can be specified but any JDK Callable can be passed in. For more information on runners, see the dedicated chapter.

<hdp:pig-tasklet id="pig-script">
   <hdp:script location="org/company/pig/handsome.pig" />
</hdp:pig-tasklet>

For those that need to programmatically interact directly with Pig , Spring for Apache Hadoop provides a dedicated template, similar to the aforementioned HiveTemplate. The template handles the redundant, boiler-plate code, required for interacting with Pig such as creating a new PigServer, executing the scripts, catching any exceptions and performing clean-up. One can programmatically execute scripts but also interact with the Hive API through the PigServerCallback. For example:

<hdp:pig-factory ... />
<!-- Pig template wires automatically to 'pigFactory'-->
<hdp:pig-template />
	
<!-- use component scanning-->
<context:component-scan base-package="some.pkg" /> 

public class SomeClass {
  @Inject
  private PigTemplate template;

  public Set<String> getDbs() {
      return pigTemplate.execute(new PigCallback<Set<String>() {
         @Override
         public Set<String> doInPig(PigServer pig) throws ExecException, IOException {
            return pig.getAliasKeySet();
         }
      });
  }
}

The example above shows a basic container configuration wiring a PigTemplate into a user class which uses it to interact with the PigServer API. Notice that the user does not have to handle the lifecycle of the PigServer instance or catch any exception - these are handled automatically by the template which converts them, like the rest of the Spring templates, into DataAccessExceptions. Thus the application only has to track only one exception hierarchy across all data technologies instead of one per technology.

For Spring Batch environments, SHDP provides a dedicated tasklet (similar to CascadeRunner above) for executing Cascade or Flow instances, on demand, as part of a batch or workflow. The declaration is pretty straightforward:

<casc:tasklet p:unit-of-work-ref="cascade" />

There are quite a number of DSLs built on top of Cascading, most noteably Cascalog (written in Clojure) and Scalding (written in Scala). This documentation will cover Scalding however the same concepts can be applied across the board to all DSLs.

As with the rest of the DSLs, Scalding offers a simplified, fluent syntax for creating units of code that build on top of Cascading. This in turn translates to Map Reduce jobs that get executed on Hadoop. Once compiled, the DSL gets translated into actual JVM classes that get executed by Scalding through its own Tool instance (namely com.twitter.scalding.Tool). One has the option of either deploy the Scalding jobs directly (by invoking the aforementioned Tool) or use Scalding's scald.rb script which does the same thing based on the various attributes passed to it. Both approaches can be used in SHDP, the former through the Tool support (described below) and the latter by invoking the scald.rb script directly through the scripting feature.

For example, to run the tutorial examples (say Tutorial1), one can issue the following command:

scripts/scald.rb --local tutorial/Tutorial1.scala

which compiles Tutorial1, creates a bundled jar and runs it on a local Hadoop instance. When using the Tool support, the compilation and the library provisioning are external tasks (just as in the case of typical Hadoop jobs). The SHDP configuration to run the tutorial looks as follows:

<!-- the tool automatically is injected with 'hadoopConfiguration' -->
<hdp:tool-runner id="scalding" tool-class="com.twitter.scalding.Tool">
   <hdp:arg value="tutorial/Tutorial1"/>
   <hdp:arg value="--local"/>
</hdp:tool-runner>

Besides dedicated configuration support, SHDP also provides read-only Tap implementations useful inside Spring environments. Currently they are meant for local use only such as testing or single-node Hadoop setups.

The Taps in org.springframework.data.hadoop.cascading.tap.local tap (pun intended) into the rich resource support from Spring Framework and Spring Integration allowing data to flow easily in and out of a Cascading flow.

Below is a list of the type of Taps available and their backing support.

Note the Taps do not require any special configuration and are fully compatible with the existing Cascading local Schemes. To wit:

<bean id="cp-txt-files" class="org.springframework.data.hadoop.cascading.tap.local.ResourceTap">
    <constructor-arg><bean class="cascading.scheme.local.TextLine"/></constructor-arg>
    <constructor-arg><value>classpath:/data/*.txt</value></constructor-arg>
</bean>

The Tap above reads all the text files in the classpath, under data folder, through Cascading TextLine. Simply wire that to a Cascading flow (as described in the previous section) and you are good to go.

HDFS layer provides file permissions designed to be similar to those present in *nix OS. The official guide explains the major components but in short, the access for each file (whether it's for reading, writing or in case of directories accessing) can be restricted to certain users or groups. Depending on the user identity (which is typically based on the host operating system), code executing against the Hadoop cluster can see or/and interact with the file-system based on these permissions. Do note that each HDFS or FileSystem implementation can have slightly different semantics or implementation.

SHDP obeys the HDFS permissions, using the identity of the current user (by default) for interacting with the file system. In particular, the HdfsResourceLoader considers when doing pattern matching, only the files that it's supposed to see and does not perform any privileged action. It is possible however to specify a different user, meaning the ResourceLoader interacts with HDFS using that user's rights - however this obeys the user impersonation rules. When using different users, it is recommended to create separate ResourceLoader instances (one per user) instead of assigning additional permissions or groups to one user - this makes it easier to manage and wire the different HDFS views without having to modify the ACLs. Note however that when using impersonation, the ResourceLoader might (and will typically) return restricted files that might not be consumed or seen by the callee.

Securing a Hadoop cluster can be a difficult task - each machine can have a different set of users and groups, each with different passwords. Hadoop relies on Kerberos, a ticket-based protocol for allowing nodes to communicate over a non-secure network to prove their identity to one another in a secure manner. Unfortunately there is not a lot of documentation on this topic out there. However there are some resources to get you started.

SHDP does not require any extra configuration - it simply obeys the security system in place. By default, when running inside a secure Hadoop, SHDP uses the current user (as expected). It also supports user impersonation, that is, interacting with the Hadoop cluster with a different identity (this allows a superuser to submit job or access hdfs on behalf of another user in a secure way, without leaking permissions). The major MapReduce components, such as job, streaming and tool as well as pig support user impersonation through the user attribute. By default, this property is empty, meaning the current user is used - however one can specify the different identity (also known as ugi) to be used by the target component:

<hdp:job id="jobFromJoe" user="joe" .../>

Note that the user running the application (or the current user) must have the proper kerberos credentials to be able to impersonate the target user (in this case joe).

To simplify configuration, SHDP provides a dedicated namespace for Yarn components. However, one can opt to configure the beans directly through the usual <bean> definition. For more information about XML Schema-based configuration in Spring, see this appendix in the Spring Framework reference documentation.

To use the SHDP namespace, one just needs to import it inside the configuration:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:yarn="http://www.springframework.org/schema/yarn"
  xmlns:yarn-int="http://www.springframework.org/schema/yarn/integration"
  xmlns:yarn-batch="http://www.springframework.org/schema/yarn/batch"
  xsi:schemaLocation="
    http://www.springframework.org/schema/beans
    http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/yarn
    http://www.springframework.org/schema/yarn/spring-yarn.xsd
    http://www.springframework.org/schema/yarn/integration
    http://www.springframework.org/schema/yarn/integration/spring-yarn-integration.xsd
    http://www.springframework.org/schema/yarn/batch
    http://www.springframework.org/schema/yarn/batch/spring-yarn-batch.xsd">

  <bean id ... >

  <yarn:configuration ...>
</beans>

Once declared, the namespace elements can be declared simply by appending the aforementioned prefix. Note that is possible to change the default namespace, for example from <beans> to <yarn>. This is useful for configuration composed mainly of Hadoop components as it avoids declaring the prefix. To achieve this, simply swap the namespace prefix declaration above:

<?xml version="1.0" encoding="UTF-8"?>
<beans:beans xmlns="http://www.springframework.org/schema/yarn"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:beans="http://www.springframework.org/schema/beans"
  xsi:schemaLocation="
    http://www.springframework.org/schema/beans
    http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/yarn
    http://www.springframework.org/schema/yarn/spring-yarn.xsd">
	    
    <beans:bean id ... >
	
    <configuration ...>
	
</beans:beans>

In order to use Hadoop and Yarn, one needs to first configure it namely by creating a YarnConfiguration object. The configuration holds information about the various parameters of the Yarn system.

	Note
	Configuration for <yarn:configuration> looks very similar than <hdp:configuration>. Reason for this is a simple separation for Hadoop's YarnConfiguration and JobConf classes.

In its simplest form, the configuration definition is a one liner:

<yarn:configuration />

The declaration above defines a YarnConfiguration bean (to be precise a factory bean of type ConfigurationFactoryBean) named, by default, yarnConfiguration. The default name is used, by conventions, by the other elements that require a configuration - this leads to simple and very concise configurations as the main components can automatically wire themselves up without requiring any specific configuration.

For scenarios where the defaults need to be tweaked, one can pass in additional configuration files:

<yarn:configuration resources="classpath:/custom-site.xml, classpath:/hq-site.xml">

In this example, two additional Hadoop configuration resources are added to the configuration.

	Note
	Note that the configuration makes use of Spring's Resource abstraction to locate the file. This allows various search patterns to be used, depending on the running environment or the prefix specified(if any) by the value - in this example the classpath is used.

In addition to referencing configuration resources, one can tweak Hadoop settings directly through Java Properties. This can be quite handy when just a few options need to be changed:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:yarn="http://www.springframework.org/schema/yarn"
  xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/yarn http://www.springframework.org/schema/yarn/spring-yarn.xsd">
        
  <yarn:configuration>
    fs.defaultFS=hdfs://localhost:9000
    hadoop.tmp.dir=/tmp/hadoop
    electric=sea
  </yarn:configuration>
</beans>

One can further customize the settings by avoiding the so called hard-coded values by externalizing them so they can be replaced at runtime, based on the existing environment without touching the configuration:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:yarn="http://www.springframework.org/schema/yarn"
  xmlns:context="http://www.springframework.org/schema/context"
  xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
    http://www.springframework.org/schema/yarn http://www.springframework.org/schema/yarn/spring-yarn.xsd">
        
  <yarn:configuration>
    fs.defaultFS=${hd.fs}
    hadoop.tmp.dir=file://${java.io.tmpdir}
    hangar=${number:18}
  </yarn:configuration>
     
  <context:property-placeholder location="classpath:hadoop.properties" />     
</beans>

Through Spring's property placeholder support, SpEL and the environment abstraction (available in Spring 3.1). one can externalize environment specific properties from the main code base easing the deployment across multiple machines. In the example above, the default file system is replaced based on the properties available in hadoop.properties while the temp dir is determined dynamically through SpEL. Both approaches offer a lot of flexbility in adapting to the running environment - in fact we use this approach extensivly in the Spring for Apache Hadoop test suite to cope with the differences between the different development boxes and the CI server.

Additionally, external Properties files can be loaded, Properties beans (typically declared through Spring's util namespace). Along with the nested properties declaration, this allows customized configurations to be easily declared:

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
  xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
  xmlns:yarn="http://www.springframework.org/schema/yarn"
  xmlns:context="http://www.springframework.org/schema/context"
  xmlns:util="http://www.springframework.org/schema/util"
  xsi:schemaLocation="http://www.springframework.org/schema/beans http://www.springframework.org/schema/beans/spring-beans.xsd
    http://www.springframework.org/schema/context http://www.springframework.org/schema/context/spring-context.xsd
    http://www.springframework.org/schema/util http://www.springframework.org/schema/util/spring-util.xsd
    http://www.springframework.org/schema/yarn http://www.springframework.org/schema/yarn/spring-yarn.xsd">

  <!-- merge the local properties, the props bean and the two properties files -->        
  <yarn:configuration properties-ref="props" properties-location="cfg-1.properties, cfg-2.properties">
    star=chasing
    captain=eo
  </yarn:configuration>
     
  <util:properties id="props" location="props.properties"/>     
</beans>

When merging several properties, ones defined locally win. In the example above the configuration properties are the primary source, followed by the props bean followed by the external properties file based on their defined order. While it's not typical for a configuration to refer to use so many properties, the example showcases the various options available.

	Note
	For more properties utilities, including using the System as a source or fallback, or control over the merging order, consider using Spring's PropertiesFactoryBean (which is what Spring for Apache Hadoop Yarn and util:properties use underneath).

It is possible to create configuration based on existing ones - this allows one to create dedicated configurations, slightly different from the main ones, usable for certain jobs (such as streaming - more on that below). Simply use the configuration-ref attribute to refer to the parent configuration - all its properties will be inherited and overridden as specified by the child:

<!-- default name is 'yarnConfiguration' -->
<yarn:configuration>
  fs.defaultFS=${hd.fs}
  hadoop.tmp.dir=file://${java.io.tmpdir}
</yarn:configuration>
     
<yarn:configuration id="custom" configuration-ref="yarnConfiguration">
  fs.defaultFS=${custom.hd.fs}
</yarn:configuration>     

...

Make sure though you specify a different name since otherwise, since both definitions will have the same name, the Spring container will interpret this as being the same definition (and will usually consider the last one found).

Last but not least a reminder that one can mix and match all these options to her preference. In general, consider externalizing configuration since it allows easier updates without interfering with the application configuration. When dealing with multiple, similar configuration use configuration composition as it tends to keep the definitions concise, in sync and easy to update.

When Application Master or any other Container is run in a hadoop cluster, there are usually dependencies to various application and configuration files. These files needs to be localized into a running Container by making a physical copy. Localization is a process where dependent files are copied into node's directory structure and thus can be used within the Container itself. Yarn itself tries to provide isolation in a way that multiple containers and applications would not clash.

In order to use local resources, one needs to create an implementation of ResourceLocalizer interface. In its simplest form, resource localizer can be defined as:

<yarn:localresources>
  <yarn:hdfs path="/path/in/hdfs/my.jar"/>
</yarn:localresources>

The declaration above defines a ResourceLocalizer bean (to be precise a factory bean of type LocalResourcesFactoryBean) named, by default, yarnLocalresources. The default name is used, by conventions, by the other elements that require a reference to a resource localizer. It's explained later how this reference is used when container launch context is defined.

It is also possible to define path as pattern. This makes it easier to pick up all or subset of files from a directory.

<yarn:localresources>
  <yarn:hdfs path="/path/in/hdfs/*.jar"/>
</yarn:localresources>

Behind the scenes it's not enough to simple have a reference to file in a hdfs file system. Yarn itself when localizing resources into container needs to do a consistency check for copied files. This is done by checking file size and timestamp. This information needs to passed to yarn together with a file path. Order to do this the one who defines these beans needs to ask this information from hdfs prior to sending out resouce localizer request. This kind of behaviour exists to make sure that once localization is defined, Container will fail fast if dependant files were replaced during the process.

On default the hdfs base address is coming from a Yarn configuration and ResourceLocalizer bean will use configuration named yarnLocalresources. If there is a need to use something else than the default bean, configuration parameter can be used to make a reference to other defined configurations.

<yarn:localresources configuration="yarnConfiguration">
  <yarn:hdfs path="/path/in/hdfs/my.jar"/>
</yarn:localresources>

For example, client defining a launch context for Application Master needs to access dependent hdfs entries. The one defining and using ResourceLocalizer bean may have a different hdfs address than the Node Manager preparing the Container. Effectively hdfs entry given to resource localizer needs to be accessed from a Node Manager.

To overcome this problem, parameters local and remote can be used to define a different hdfs base entries.

<yarn:localresources local="hdfs://0.0.0.0:9000" remote="hdfs://10.10.10.10:9000">
  <yarn:hdfs path="/app/multi-context/multi-context-1.0.0.M1.jar"/>
  <yarn:hdfs path="/app/spring-yarn-core-1.0.0.BUILD-SNAPSHOT.jar"/>
</yarn:localresources>

Yarn resource localizer is using additional parameters to define entry type and visibility. Usage is described below:

<yarn:localresources>
  <yarn:hdfs path="/path/in/hdfs/my.jar" type="FILE" visibility="APPLICATION"/>
</yarn:localresources>

For convenience it is possible to copy files into hdfs during the localization process using a yarn:copy tag. Currently base staging directory is /syarn/staging/xx where xx is a unique identifier per application instance.

<yarn:localresources>
  <yarn:copy src="file:/local/path/to/files/*jar" staging="true"/>
  <yarn:hdfs path="/*" staging="true"/>
</yarn:localresources>

One central concept in Yarn is to use environment variables which then can be read from a container. While it's possible to read those variable at any time it is considered bad design if one chooce to do so. Spring Yarn will pass variable into application before any business methods are executed, which makes things more clearly and testing becomes much more easier.

<yarn:environment/>

The declaration above defines a Map bean (to be precise a factory bean of type EnvironmentFactoryBean) named, by default, yarnEnvironment. The default name is used, by conventions, by the other elements that require a reference to a environment variables.

For conveniance it is possible to define a classpath entry directly into an environment. Most likely one is about to run some java code with libraries so classpath needs to be defined anyway.

<yarn:environment include-system-env="false">
  <yarn:classpath default-yarn-app-classpath="true" delimiter=":">
    ./*
  </yarn:classpath>
</yarn:environment>

If default-yarn-app-classpath parameter is set to true(default value) a default yarn entries will be added to classpath automatically. Resulting entries are shown below:

$HADOOP_CONF_DIR:
$HADOOP_COMMON_HOME/*:
$HADOOP_COMMON_HOME/lib/*:
$HADOOP_COMMON_HOME/share/hadoop/common/*:
$HADOOP_COMMON_HOME/share/hadoop/common/lib/*:
$HADOOP_HDFS_HOME/*:
$HADOOP_HDFS_HOME/lib/*:
$HADOOP_HDFS_HOME/share/hadoop/hdfs/*:
$HADOOP_HDFS_HOME/share/hadoop/hdfs/lib/*:
$YARN_HOME/*:
$YARN_HOME/lib/*:
$HADOOP_YARN_HOME/share/hadoop/yarn/*:
$HADOOP_YARN_HOME/share/hadoop/yarn/lib/*

	Note
	Be carefull if passing environment variables between different systems. For example if running a client on Windows and passing variables to Application Master running on Linux, execution wrapper in Yarn may silently fail.

Client is always your entry point when interacting with a Yarn system whether one is about to submit a new application instance or just querying Resource Manager for running application(s) status. Currently support for client is very limited and a simple command to start Application Master can be defined. If there is just a need to query Resource Manager, command definition is not needed.

<yarn:client app-name="customAppName">
  <yarn:master-command>
    <![CDATA[
      /usr/local/java/bin/java
      org.springframework.yarn.am.CommandLineAppmasterRunner
      appmaster-context.xml
      yarnAppmaster
      container-count=2
      1><LOG_DIR>/AppMaster.stdout
      2><LOG_DIR>/AppMaster.stderr
    ]]>
  </yarn:master-command>
</yarn:client>

The declaration above defines a YarnClient bean (to be precise a factory bean of type YarnClientFactoryBean) named, by default, yarnClient. It also defines a command launching an Application Master using <master-command> entry which is also a way to define the raw commands. If this yarnClient instance is used to submit an application, its name would come from a app-name attribute.

<yarn:client app-name="customAppName">
  <yarn:master-runner/>
</yarn:client>

For a convinience entry <master-runner> can be used to define same command entries.

<yarn:client app-name="customAppName">
  <util:properties id="customArguments">
    container-count=2
  </util:properties>
  <yarn:master-runner
    command="java"
    context-file="appmaster-context.xml"
    bean-name="yarnAppmaster"
    arguments="customArguments"
    stdout="<LOG_DIR>/AppMaster.stdout"
    stderr="<LOG_DIR>/AppMaster.stderr" />
</yarn:client>

All previous three examples are effectively identical from Spring Yarn point of view.

	Note
	The <LOG_DIR> refers to Hadoop's dedicated log directory for the running container.

<yarn:client app-name="customAppName"
  configuration="customConfiguration"
  resource-localizer="customResources"
  environment="customEnv"
  priority="1"
  virtualcores="2"
  memory="11"
  queue="customqueue">
  <yarn:master-runner/>
</yarn:client>

If there is a need to change some of the parameters for the Application Master submission, memory and virtualcores defines the container settings. For submission, queue and priority defines how submission is actually done.

Application master is responsible for container allocation, launching and monitoring.

<yarn:master>
  <yarn:container-allocator hosts="host1,host2" racks="rack1,rack2" virtualcores="1" memory="64" priority="0"/>    
  <yarn:container-launcher username="whoami"/>    
  <yarn:container-command>
    <![CDATA[
      /usr/local/java/bin/java
      org.springframework.yarn.container.CommandLineContainerRunner
      container-context.xml
      1><LOG_DIR>/Container.stdout
      2><LOG_DIR>/Container.stderr
    ]]>
  </yarn:container-command>
</yarn:master>

The declaration above defines a YarnAppmaster bean (to be precise a bean of type StaticAppmaster) named, by default, yarnAppmaster. It also defines a command launching a Container(s) using <container-command> entry, parameters for allocation using <container-allocator> entry and finally a launcher parameter using <container-launcher> entry.

Currently there is a simple implementation of StaticAppmaster which is able to allocate and launch a number of containers. These containers are monitored by querying resource manager for container execution completion.

<yarn:master>
  <yarn:container-runner/>
</yarn:master>

For a convinience entry <container-runner> can be used to define same command entries.

<yarn:master>
  <util:properties id="customArguments">
    some-argument=myvalue
  </util:properties>
  <yarn:container-runner
    command="java"
    context-file="container-context.xml"
    bean-name="yarnContainer"
    arguments="customArguments"
    stdout="<LOG_DIR>/Container.stdout"
    stderr="<LOG_DIR>/Container.stderr" />
</yarn:master>

There is very little what Spring Yarn needs to know about the Container in terms of its configuration. There is a simple contract between org.springframework.yarn.container.CommandLineContainerRunner and a bean it's trying to run on default. Default bean name is yarnContainer.

There is a simple interface org.springframework.yarn.container.YarnContainer which container needs to implement.

public interface YarnContainer {
  void run();
  void setEnvironment(Map<String, String> environment);
  void setParameters(Properties parameters);
}

There are few different ways how Container can be defined in Spring xml configuration. Natively without using namespaces bean can be defined with a correct name:

<bean id="yarnContainer" class="org.springframework.yarn.container.TestContainer">	

Spring Yarn namespace will make it even more simpler. Below example just defines class which implements needed interface.

<yarn:container container-class="org.springframework.yarn.container.TestContainer"/>

It's possible to make a reference to existing bean. This is usefull if bean cannot be instantiated with default constructor.

<bean id="testContainer" class="org.springframework.yarn.container.TestContainer"/>
<yarn:container container-ref="testContainer"/>

It's also possible to inline the bean definition.

<yarn:container>
  <bean class="org.springframework.yarn.container.TestContainer"/>
</yarn:container>

It is fairly easy to create an application which launches a few containers and then leave those to do their tasks. This is pretty much what Distributed Shell example application in Yarn is doing. In that example a container is configured to run a simple shell command and Application Master only tracks when containers have finished. If only need from a framework is to be able to fire and forget then that's all you need, but most likely a real-world Yarn application will need some sort of collaboration with Application Master. This communication is initiated either from Application Client or Application Container.

Yarn framework itself doesn't define any kind of general communication API for Application Master. There are APIs for communicating with Container Manager and Resource Manager which are used on within a layer not necessarily exposed to a user. Spring Yarn defines a general framework to talk to Application Master through an abstraction and currently a JSON based rpc system exists.

This chapter concentrates on developer concepts to create a custom services for Application Master, configuration options for built-in services can be found from sections below - Appmaster Service and Appmaster Service Client.

public interface AppmasterService {
  int getPort();
  boolean hasPort();
  String getHost();
}

<bean id="yarnAmservice" class="AppmasterServiceImpl" />
<bean id="yarnClientAmservice" class="AppmasterClientServiceImpl" />

public interface AppmasterServiceClient {
}

<yarn-int:amservice
  service-impl="org.springframework.yarn.integration.ip.mind.TestService"
  default-port="1234"/>
<yarn-int:amservice-client
  service-impl="org.springframework.yarn.integration.ip.mind.DefaultMindAppmasterServiceClient"
  host="localhost"
  port="1234"/>

@Autowired
AppmasterServiceClient appmasterServiceClient;

@Test
public void testServiceInterfaces() throws Exception {
  SimpleTestRequest request = new SimpleTestRequest();
  SimpleTestResponse response =
  (SimpleTestResponse) ((MindAppmasterServiceClient)appmasterServiceClient).
    doMindRequest(request);
  assertThat(response.stringField, is("echo:stringFieldValue"));
}

<bean id="mapper" 
  class="org.springframework.yarn.integration.support.Jackson2ObjectMapperFactoryBean" />	
	
<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindObjectToHolderConverter">
    <constructor-arg ref="mapper"/>
  </bean>
</yarn-int:converter>

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindHolderToObjectConverter">
    <constructor-arg ref="mapper"/>
    <constructor-arg value="org.springframework.yarn.batch.repository.bindings"/>
  </bean>
</yarn-int:converter>

This section of this document is about configuration, more about general concepts for see a Section 11.8, “Application Master Services”.

Currently Spring Yarn have support for services using Spring Integration tcp channels as a transport.

<bean id="mapper" 
  class="org.springframework.yarn.integration.support.Jackson2ObjectMapperFactoryBean" />
	
<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindObjectToHolderConverter">
    <constructor-arg ref="mapper"/>
  </bean>
</yarn-int:converter>

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindHolderToObjectConverter">
    <constructor-arg ref="mapper"/>
    <constructor-arg value="org.springframework.yarn.integration.ip.mind"/>
  </bean>
</yarn-int:converter>
	
<yarn-int:amservice
  service-impl="org.springframework.yarn.integration.ip.mind.TestService"/>

If there is a need to manually configure the server side dispatch channel, a little bit more configuration is needed.

<bean id="serializer"
  class="org.springframework.yarn.integration.ip.mind.MindRpcSerializer" />
<bean id="deserializer"
  class="org.springframework.yarn.integration.ip.mind.MindRpcSerializer" />
<bean id="socketSupport"
  class="org.springframework.yarn.integration.support.DefaultPortExposingTcpSocketSupport" />

<ip:tcp-connection-factory id="serverConnectionFactory"
  type="server"
  port="0"
  socket-support="socketSupport"
  serializer="serializer"
  deserializer="deserializer"/>

<ip:tcp-inbound-gateway id="inboundGateway"
  connection-factory="serverConnectionFactory"
  request-channel="serverChannel" />
  
<int:channel id="serverChannel" />
  
<yarn-int:amservice
  service-impl="org.springframework.yarn.integration.ip.mind.TestService"
  channel="serverChannel"
  socket-support="socketSupport"/>

This section of this document is about configuration, more about general concepts for see a Section 11.8, “Application Master Services”.

Currently Spring Yarn have support for services using Spring Integration tcp channels as a transport.

<bean id="mapper" 
  class="org.springframework.yarn.integration.support.Jackson2ObjectMapperFactoryBean" />
	
<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindObjectToHolderConverter">
    <constructor-arg ref="mapper"/>
  </bean>
</yarn-int:converter>

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindHolderToObjectConverter">
    <constructor-arg ref="mapper"/>
    <constructor-arg value="org.springframework.yarn.integration.ip.mind"/>
  </bean>
</yarn-int:converter>
	
<yarn-int:amservice-client
  service-impl="org.springframework.yarn.integration.ip.mind.DefaultMindAppmasterServiceClient"
  host="${SHDP_AMSERVICE_HOST}"
  port="${SHDP_AMSERVICE_PORT}"/>

If there is a need to manually configure the server side dispatch channel, a little bit more configuration is needed.

<bean id="serializer"
  class="org.springframework.yarn.integration.ip.mind.MindRpcSerializer" />
<bean id="deserializer"
  class="org.springframework.yarn.integration.ip.mind.MindRpcSerializer" />

<ip:tcp-connection-factory id="clientConnectionFactory"
  type="client"
  host="localhost"
  port="${SHDP_AMSERVICE_PORT}"
  serializer="serializer"
  deserializer="deserializer"/>

<ip:tcp-outbound-gateway id="outboundGateway"
  connection-factory="clientConnectionFactory"
  request-channel="clientRequestChannel"
  reply-channel="clientResponseChannel" />

<int:channel id="clientRequestChannel" />
<int:channel id="clientResponseChannel" >
  <int:queue />
</int:channel>
		
<yarn-int:amservice-client
  service-impl="org.springframework.yarn.integration.ip.mind.DefaultMindAppmasterServiceClient"
  request-channel="clientRequestChannel"
  response-channel="clientResponseChannel"/>

In this chapter we assume you are fairly familiar with concepts using Spring Batch. Many batch processing problems can be solved with single threaded, single process jobs, so it is always a good idea to properly check if that meets your needs before thinking about more complex implementations. When you are ready to start implementing a job with some parallel processing, Spring Batch offers a range of options. At a high level there are two modes of parallel processing: single process, multi-threaded; and multi-process.

Spring Hadoop contains a support for running Spring Batch jobs on a Hadoop cluster. For better parallel processing Spring Batch partitioned steps can be executed on a Hadoop cluster as remote steps.

<bean id="transactionManager" class="org.springframework.batch.support.transaction.ResourcelessTransactionManager"/>

<bean id="jobRepository" class="org.springframework.batch.core.repository.support.MapJobRepositoryFactoryBean">
  <property name="transactionManager" ref="transactionManager"/>
</bean>

<bean id="jobLauncher" class="org.springframework.batch.core.launch.support.SimpleJobLauncher">
  <property name="jobRepository" ref="jobRepository"/>
</bean>

<bean id="yarnEventPublisher" class="org.springframework.yarn.event.DefaultYarnEventPublisher"/>
	  
<yarn-batch:master/>

<bean id="hello" class="org.springframework.yarn.examples.PrintTasklet">
  <property name="message" value="Hello"/>
</bean>

<batch:job id="job">
  <batch:step id="master">
    <batch:tasklet transaction-manager="transactionManager" ref="hello"/>
  </batch:step>
</batch:job>

11.11.2 Partitioning

Let's take a quick look how Spring Batch partitioning is handled. Concept of running a partitioned job involves three things, Remote steps, Partition Handler and a Partitioner. If we do a little bit of oversimplification a remote step is like any other step from a user point of view. Spring Batch itself does not contain implementations for any proprietary grid or remoting fabrics. Spring Batch does however provide a useful implementation of PartitionHandler that executes Steps locally in separate threads of execution, using the TaskExecutor strategy from Spring. Spring Hadoop provides implementation to execute Steps remotely on a Hadoop cluster.

	Note
	For more background information about the Spring Batch Partitioning, read the Spring Batch reference documentation.

Configuring Master

As we previously mentioned a step executed on a remote host also need to access a job repository. If job repository would be based on a database instance, configuration could be similar on a container compared to application master. In our configuration example the job repository is in-memory based and remote steps needs access for it. Spring Yarn Batch contains implementation of a job repository which is able to proxy request via json requests. Order to use that we need to enable application client service which is exposing this service.

<bean id="jobRepositoryRemoteService" class="org.springframework.yarn.batch.repository.JobRepositoryRemoteService" >
  <property name="mapJobRepositoryFactoryBean" ref="&amp;jobRepository"/>
</bean>

<bean id="batchService" class="org.springframework.yarn.batch.repository.BatchAppmasterService" >
  <property name="jobRepositoryRemoteService" ref="jobRepositoryRemoteService"/>
</bean>

<yarn-int:amservice service-ref="batchService"/>

he declaration above defines JobRepositoryRemoteService bean named jobRepositoryRemoteService which is then connected into Application Master Service exposing job repository via Spring Integration Tcp channels.

As job repository communication messages are exchanged via custom json messages, converters needs to be defined.

<bean id="mapper" class="org.springframework.yarn.integration.support.Jackson2ObjectMapperFactoryBean" />

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindObjectToHolderConverter">
    <constructor-arg ref="mapper"/>
  </bean>
</yarn-int:converter>

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindHolderToObjectConverter">
    <constructor-arg ref="mapper"/>
    <constructor-arg value="org.springframework.yarn.batch.repository.bindings"/>
  </bean>
</yarn-int:converter>

Configuring Container

Previously we made a choice to use in-memore job repository running inside the application master. Now we need to talk to this repository via client service. We start by adding same converters as in application master.

<bean id="mapper" class="org.springframework.yarn.integration.support.Jackson2ObjectMapperFactoryBean" />

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindObjectToHolderConverter">
    <constructor-arg ref="mapper"/>
  </bean>
</yarn-int:converter>

<yarn-int:converter>
  <bean class="org.springframework.yarn.integration.convert.MindHolderToObjectConverter">
    <constructor-arg ref="mapper"/>
    <constructor-arg value="org.springframework.yarn.batch.repository.bindings"/>
  </bean>
</yarn-int:converter>

We use general client implementation able to communicate with a service running on Application Master.

<yarn-int:amservice-client
  service-impl="org.springframework.yarn.integration.ip.mind.DefaultMindAppmasterServiceClient"
  host="${SHDP_AMSERVICE_HOST}"
  port="${SHDP_AMSERVICE_PORT}" />

Remote step is just like any other step.

<bean id="hello" class="org.springframework.yarn.examples.PrintTasklet">
  <property name="message" value="Hello"/>
</bean>

<batch:step id="remoteStep">
  <batch:tasklet transaction-manager="transactionManager" start-limit="100" ref="hello"/>
</batch:step>

We need to have a way to locate the step from an application context. For this we can define a step locator which is later configured into running container.

<bean id="stepLocator" class="org.springframework.yarn.batch.partition.BeanFactoryStepLocator"/>

Spring Hadoop contains a custom job repository implementation which is able to talk back to a remote instance via custom json protocol.

<bean id="transactionManager" class="org.springframework.batch.support.transaction.ResourcelessTransactionManager"/>

<bean id="jobRepository" class="org.springframework.yarn.batch.repository.RemoteJobRepositoryFactoryBean">
  <property name="transactionManager" ref="transactionManager"/>
  <property name="appmasterScOperations" ref="yarnAmserviceClient"/>
</bean>

<bean id="jobExplorer" class="org.springframework.yarn.batch.repository.RemoteJobExplorerFactoryBean">
  <property name="repositoryFactory" ref="&amp;jobRepository" />
</bean>

Finally we define a Container understanding how to work with a remote steps.

<bean id="yarnContainer" class="org.springframework.yarn.batch.container.DefaultBatchYarnContainer">
  <property name="stepLocator" ref="stepLocator"/>
  <property name="jobExplorer" ref="jobExplorer"/>
  <property name="integrationServiceClient" ref="yarnAmserviceClient"/>
</bean>

Spring Batch integrates with a variety of job schedulers and is not a scheduling framework. There are many good enterprise schedulers available in both the commercial and open source spaces such as Quartz, Tivoli, Control-M, etc. It is intended to work in conjunction with a scheduler, not replace a scheduler. As a lightweight solution, you can use Spring's built in scheduling support that will give you cron-like and other basic scheduling trigger functionality. See the Task Execution and Scheduling documention for more info. A middle ground it to use Spring's Quartz integration, see Using the OpenSymphony Quartz Scheduler for more information. The Spring Batch distribution contains an example, but this documentation will be updated to provide some more directed examples with Hadoop, check for updates on the main web site of Spring for Apache Hadoop.

Spring Batch lets you attach listeners at the job and step levels to perform additional processing. For example, at the end of a job you can perform some notification or perhaps even start another Spring Batch job. As a brief example, implement the interface JobExecutionListener and configure it into the Spring Batch job as shown below.

<batch:job id="job1">
  <batch:step id="import" next="wordcount">
    <batch:tasklet ref="script-tasklet"/>
  </batch:step>
			
  <batch:step id="wordcount">
    <batch:tasklet ref="wordcount-tasklet" />
  </batch:step>

  <batch:listeners>
   <batch:listener ref="simpleNotificatonListener"/>
  </batch:listeners>

</batch:job>

<bean id="simpleNotificatonListener" class="com.mycompany.myapp.SimpleNotificationListener"/>