spring batch partitioning

Photo Credit : Spring Source

In Spring Batch, “Partitioning” is “multiple threads to process a range of data each”. For example, assume you have 100 records in a table, which has “primary id” assigned from 1 to 100, and you want to process the entire 100 records.

Normally, the process starts from 1 to 100, a single thread example. The process is estimated to take 10 minutes to finish.


Single Thread - Process from 1 to 100

In “Partitioning”, we can start 10 threads to process 10 records each (based on the range of ‘id’). Now, the process may take only 1 minute to finish.


Thread 1 - Process from 1 to 10
Thread 2 - Process from 11 to 20
Thread 3 - Process from 21 to 30
......
Thread 9 - Process from 81 to 90
Thread 10 - Process from 91 to 100

To implement “Partitioning” technique, you must understand the structure of the input data to process, so that you can plan the “range of data” properly.

1. Tutorial

In this tutorial, we will show you how to create a “Partitioner” job, which has 10 threads, each thread will read records from the database, based on the provided range of ‘id’.

Tools and libraries used

  1. Maven 3
  2. Eclipse 4.2
  3. JDK 1.6
  4. Spring Core 3.2.2.RELEASE
  5. Spring Batch 2.2.0.RELEASE
  6. MySQL Java Driver 5.1.25

P.S Assume “users” table has 100 records.

users table structure

id, user_login, user_passs, age

1,user_1,pass_1,20
2,user_2,pass_2,40
3,user_3,pass_3,70
4,user_4,pass_4,5
5,user_5,pass_5,52
......
99,user_99,pass_99,89
100,user_100,pass_100,76

2. Project Directory Structure

Review the final project structure, a standard Maven project.

spring-batch-partitioner-before

3. Partitioner

First, create a Partitioner implementation, puts the “partitioning range” into the ExecutionContext. Later, you will declare the same fromId and tied in the batch-job XML file.

In this case, the partitioning range is look like the following :


Thread 1 = 1 - 10
Thread 2 = 11 - 20
Thread 3 = 21 - 30
......
Thread 10 = 91 - 100
RangePartitioner.java

package com.mkyong.partition;

import java.util.HashMap;
import java.util.Map;

import org.springframework.batch.core.partition.support.Partitioner;
import org.springframework.batch.item.ExecutionContext;

public class RangePartitioner implements Partitioner {

	@Override
	public Map<String, ExecutionContext> partition(int gridSize) {

		Map<String, ExecutionContext> result 
                       = new HashMap<String, ExecutionContext>();

		int range = 10;
		int fromId = 1;
		int toId = range;

		for (int i = 1; i <= gridSize; i++) {
			ExecutionContext value = new ExecutionContext();

			System.out.println("\nStarting : Thread" + i);
			System.out.println("fromId : " + fromId);
			System.out.println("toId : " + toId);

			value.putInt("fromId", fromId);
			value.putInt("toId", toId);

			// give each thread a name, thread 1,2,3
			value.putString("name", "Thread" + i);

			result.put("partition" + i, value);

			fromId = toId + 1;
			toId += range;

		}

		return result;
	}

}

4. Batch Jobs

Review the batch job XML file, it should be self-explanatory. Few points to highlight :

  1. For partitioner, grid-size = number of threads.
  2. For pagingItemReader bean, a jdbc reader example, the #{stepExecutionContext[fromId, toId]} values will be injected by the ExecutionContext in rangePartitioner.
  3. For itemProcessor bean, the #{stepExecutionContext[name]} values will be injected by the ExecutionContext in rangePartitioner.
  4. For writers, each thread will output the records in a different csv files, with filename format - users.processed[fromId]}-[toId].csv.
job-partitioner.xml

<?xml version="1.0" encoding="UTF-8"?>
<beans xmlns="http://www.springframework.org/schema/beans"
	xmlns:batch="http://www.springframework.org/schema/batch"
	xmlns:util="http://www.springframework.org/schema/util"
	xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
	xsi:schemaLocation="http://www.springframework.org/schema/batch 
	http://www.springframework.org/schema/batch/spring-batch-2.2.xsd
	http://www.springframework.org/schema/beans 
	http://www.springframework.org/schema/beans/spring-beans-3.2.xsd
	http://www.springframework.org/schema/util 
	http://www.springframework.org/schema/util/spring-util-3.2.xsd
	">

  <!-- spring batch core settings -->
  <import resource="../config/context.xml" />
	
  <!-- database settings -->
  <import resource="../config/database.xml" />

  <!-- partitioner job -->
  <job id="partitionJob" xmlns="http://www.springframework.org/schema/batch">
	    
    <!-- master step, 10 threads (grid-size)  -->
    <step id="masterStep">
	<partition step="slave" partitioner="rangePartitioner">
		<handler grid-size="10" task-executor="taskExecutor" />
	</partition>
    </step>
		
  </job>

  <!-- each thread will run this job, with different stepExecutionContext values. -->
  <step id="slave" xmlns="http://www.springframework.org/schema/batch">
	<tasklet>
		<chunk reader="pagingItemReader" writer="flatFileItemWriter"
			processor="itemProcessor" commit-interval="1" />
	</tasklet>
  </step>

  <bean id="rangePartitioner" class="com.mkyong.partition.RangePartitioner" />

  <bean id="taskExecutor" class="org.springframework.core.task.SimpleAsyncTaskExecutor" />

  <!-- inject stepExecutionContext -->
  <bean id="itemProcessor" class="com.mkyong.processor.UserProcessor" scope="step">
	<property name="threadName" value="#{stepExecutionContext[name]}" />
  </bean>

  <bean id="pagingItemReader"
	class="org.springframework.batch.item.database.JdbcPagingItemReader"
	scope="step">
	<property name="dataSource" ref="dataSource" />
	<property name="queryProvider">
	  <bean
		class="org.springframework.batch.item.database.support.SqlPagingQueryProviderFactoryBean">
		<property name="dataSource" ref="dataSource" />
		<property name="selectClause" value="select id, user_login, user_pass, age" />
		<property name="fromClause" value="from users" />
		<property name="whereClause" value="where id >= :fromId and id <= :toId" />
		<property name="sortKey" value="id" />
	  </bean>
	</property>
	<!-- Inject via the ExecutionContext in rangePartitioner -->
	<property name="parameterValues">
	  <map>
		<entry key="fromId" value="#{stepExecutionContext[fromId]}" />
		<entry key="toId" value="#{stepExecutionContext[toId]}" />
	  </map>
	</property>
	<property name="pageSize" value="10" />
	<property name="rowMapper">
		<bean class="com.mkyong.UserRowMapper" />
	</property>
  </bean>

  <!-- csv file writer -->
  <bean id="flatFileItemWriter" class="org.springframework.batch.item.file.FlatFileItemWriter"
	scope="step" >
	<property name="resource"
		value="file:csv/outputs/users.processed#{stepExecutionContext[fromId]}-#{stepExecutionContext[toId]}.csv" />
	<property name="appendAllowed" value="false" />
	<property name="lineAggregator">
	  <bean
		class="org.springframework.batch.item.file.transform.DelimitedLineAggregator">
		<property name="delimiter" value="," />
		<property name="fieldExtractor">
		  <bean
			class="org.springframework.batch.item.file.transform.BeanWrapperFieldExtractor">
			<property name="names" value="id, username, password, age" />
		  </bean>
		</property>
	  </bean>
	</property>
  </bean>

</beans>

The item processor class is used to print out the processing item and current running "thread name" only.

UserProcessor.java - item processor

package com.mkyong.processor;

import org.springframework.batch.item.ItemProcessor;
import com.mkyong.User;

public class UserProcessor implements ItemProcessor<User, User> {

	private String threadName;

	@Override
	public User process(User item) throws Exception {

		System.out.println(threadName + " processing : " 
			+ item.getId() + " : " + item.getUsername());

		return item;
	}

	public String getThreadName() {
		return threadName;
	}

	public void setThreadName(String threadName) {
		this.threadName = threadName;
	}

}

5. Run It

Loads everything and run it... 10 threads will be started to process the provided range of data.


package com.mkyong;

import org.springframework.batch.core.Job;
import org.springframework.batch.core.JobExecution;
import org.springframework.batch.core.JobParameters;
import org.springframework.batch.core.JobParametersBuilder;
import org.springframework.batch.core.launch.JobLauncher;
import org.springframework.context.ApplicationContext;
import org.springframework.context.support.ClassPathXmlApplicationContext;

public class PartitionApp {

  public static void main(String[] args) {
	PartitionApp obj = new PartitionApp ();
	obj.runTest();
  }

  private void runTest() {

	String[] springConfig = { "spring/batch/jobs/job-partitioner.xml" };

	ApplicationContext context = new ClassPathXmlApplicationContext(springConfig);

	JobLauncher jobLauncher = (JobLauncher) context.getBean("jobLauncher");
	Job job = (Job) context.getBean("partitionJob");

	try {

	  JobExecution execution = jobLauncher.run(job, new JobParameters());
	  System.out.println("Exit Status : " + execution.getStatus());
	  System.out.println("Exit Status : " + execution.getAllFailureExceptions());

	} catch (Exception e) {
		e.printStackTrace();
	}

	  System.out.println("Done");

  }
}

Console output


Starting : Thread1
fromId : 1
toId : 10

Starting : Thread2
fromId : 11
toId : 20

Starting : Thread3
fromId : 21
toId : 30

Starting : Thread4
fromId : 31
toId : 40

Starting : Thread5
fromId : 41
toId : 50

Starting : Thread6
fromId : 51
toId : 60

Starting : Thread7
fromId : 61
toId : 70

Starting : Thread8
fromId : 71
toId : 80

Starting : Thread9
fromId : 81
toId : 90

Starting : Thread10
fromId : 91
toId : 100

Thread8 processing : 71 : user_71
Thread2 processing : 11 : user_11
Thread3 processing : 21 : user_21
Thread10 processing : 91 : user_91
Thread4 processing : 31 : user_31
Thread6 processing : 51 : user_51
Thread5 processing : 41 : user_41
Thread1 processing : 1 : user_1
Thread9 processing : 81 : user_81
Thread7 processing : 61 : user_61
Thread2 processing : 12 : user_12
Thread7 processing : 62 : user_62
Thread6 processing : 52 : user_52
Thread1 processing : 2 : user_2
Thread9 processing : 82 : user_82
......

After the process is completed, 10 CSV files will be created.

spring-batch-partitioner-after
users.processed1-10.csv

1,user_1,pass_1,20
2,user_2,pass_2,40
3,user_3,pass_3,70
4,user_4,pass_4,5
5,user_5,pass_5,52
6,user_6,pass_6,69
7,user_7,pass_7,48
8,user_8,pass_8,34
9,user_9,pass_9,62
10,user_10,pass_10,21

6. Misc

6.1 Alternatively, you can inject the #{stepExecutionContext[name]} via annotation.

UserProcessor.java - Annotation version

package com.mkyong.processor;

import org.springframework.batch.item.ItemProcessor;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Scope;
import org.springframework.stereotype.Component;
import com.mkyong.User;

@Component("itemProcessor")
@Scope(value = "step")
public class UserProcessor implements ItemProcessor<User, User> {

	@Value("#{stepExecutionContext[name]}")
	private String threadName;

	@Override
	public User process(User item) throws Exception {

		System.out.println(threadName + " processing : " 
                     + item.getId() + " : " + item.getUsername());

		return item;
	}
	
}

Remember, enable the Spring component auto scanning.


	<context:component-scan base-package="com.mkyong" />

6.2 Database partitioner reader - MongoDB example.

job-partitioner.xml

  <bean id="mongoItemReader" class="org.springframework.batch.item.data.MongoItemReader"
	scope="step">
	<property name="template" ref="mongoTemplate" />
	<property name="targetType" value="com.mkyong.User" />
	<property name="query"
	  value="{ 
		'id':{$gt:#{stepExecutionContext[fromId]}, $lte:#{stepExecutionContext[toId]} 
	  } }" 
	/>
	<property name="sort">
		<util:map id="sort">
			<entry key="id" value="" />
		</util:map>
	</property>
  </bean>

Done.

Download Source Code

References

  1. Spring Batch Partitioning Guide
  2. Partitioner JavaDoc
  3. ExecutionContext JavaDoc
  4. Wiki : Database Partition