Spring Batch – Partitioning

Learn to use Spring batch partitioning to use multiple threads to process a range of data sets in a spring boot application.

1. Parallel processing and Partitioning

1.1. Parallel processing

Mostly batch processing problems can be solved using single-threaded, but few complex scenarios like single-threaded processing taking a long time to perform tasks, where parallel processing is needed, can be achieved using multi-threaded models.

At a very high level, there are two modes of parallel processing:

  • Single process, multi-threaded
  • Multi-process

These break down into categories as well, as follows:

  • Multi-threaded Step (single process)
  • Parallel Steps (single process)
  • Remote Chunking of Step (multi process)
  • Partitioning a Step (single or multi process)

1.2. Partitioning in Spring batch

Partitioning uses multiple threads to process a range of data sets. The range of data sets can be defined programmatically. It’s on use case, how many threads we want to create to be used in a partition(s). The number of threads is purely based on the need/requirement.

Spring Batch is single threaded by default. In order to make the parallel processing, we need to Partition the steps of the batch job.

Partitioning is useful when we have millions of records to read from source systems and we can’t just rely on a single thread to process all records, which can be time-consuming. We want to use multiple threads to read and process data to effectively use the system resources.

2. Spring batch partitioning example

In this tutorial, we will read some data from a table and write it into another table. We can create millions of records into DB in order to experience how long the process would be if using Single thread batch processing. Here I have created few to understand how the program/concept works.

2.1. Maven dependencies

We used the latest version of Spring Boot as of today and by adding spring batch dependency, it will automatically pull the latest versions.




2.2. Partitioner

Partitioner is the central strategy interface for creating input parameters for a partitioned step in the form of ExecutionContext instances. The usual aim is to create a set of distinct input values, e.g. a set of non-overlapping primary key ranges, or a set of unique filenames.

Here we are querying the table to get the MAX and MIN id values (assuming they are sequential), and based on that we’re creating partitions between all records.

For partitioner, we have used gridSize = number of threads. Use your own custom value based on your requirements.

package com.howtodoinjava.batch.decorator.classifier;
import java.util.HashMap;
import java.util.Map;
import javax.sql.DataSource;
import org.springframework.batch.core.partition.support.Partitioner;
import org.springframework.batch.item.ExecutionContext;
import org.springframework.jdbc.core.JdbcOperations;
import org.springframework.jdbc.core.JdbcTemplate;

public class ColumnRangePartitioner implements Partitioner 
	private JdbcOperations jdbcTemplate;
	private String table;
	private String column;

	public void setTable(String table) {
		this.table = table;

	public void setColumn(String column) {
		this.column = column;

	public void setDataSource(DataSource dataSource) {
		jdbcTemplate = new JdbcTemplate(dataSource);

	public Map<String, ExecutionContext> partition(int gridSize) 
		int min = jdbcTemplate.queryForObject("SELECT MIN(" + column + ") FROM " + table, Integer.class);

		int max = jdbcTemplate.queryForObject("SELECT MAX(" + column + ") FROM " + table, Integer.class);

		int targetSize = (max - min) / gridSize + 1;

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

		int number = 0;
		int start = min;
		int end = start + targetSize - 1;
		while (start <= max) 
			ExecutionContext value = new ExecutionContext();
			result.put("partition" + number, value);
			if(end >= max) {
				end = max;
			value.putInt("minValue", start);
			value.putInt("maxValue", end);

			start += targetSize;
			end += targetSize;

		return result;

2.3. Job configuration

This is the job configuration class where we are creating the necessary bean to perform the job. In this example, we used SimpleAsyncTaskExecutor which is the simplest multi-threaded implementation of the TaskExecutorinterface.

We’ve used partitioner in the Step to create a partition step builder for a remote (or local) step.

Using these Step for each chunk of data ‘reading, processing and writing’, happens altogether in different threads. Hence, the processed records may not be in the same sequential order as fed into it.

Below are the things to look –

  • A throttle limit imposed on the task executor say, when it is backed by some thread pool. This limit defaults to 4 but can be configured differently.
  • Concurrency limits might comes on the resource used in the Step, say the DataSource used.
  • ColumnRangePartitioner – Central strategy interface for creating input parameters for a partitioned step in the form of ExecutionContext instances.
  • JdbcPagingItemReader – This bean read data using Pagination and accepts minValue and maxValue to be accept based on range to get those data only. Here we setFetchSize to 1000, however you can use any value and make it configurable from properties file.
  • JdbcBatchItemWriter – This bean will write the data into another table.
  • Step – This is step configure in the batch job. This is read data and write it into XML and JSON format
  • Job – Batch domain object representing a job
package com.howtodoinjava.batch.decorator.config;
import java.util.HashMap;
import java.util.Map;

import javax.sql.DataSource;

import org.springframework.batch.core.Job;
import org.springframework.batch.core.Step;
import org.springframework.batch.core.configuration.annotation.JobBuilderFactory;
import org.springframework.batch.core.configuration.annotation.StepBuilderFactory;
import org.springframework.batch.core.configuration.annotation.StepScope;
import org.springframework.batch.item.database.BeanPropertyItemSqlParameterSourceProvider;
import org.springframework.batch.item.database.JdbcBatchItemWriter;
import org.springframework.batch.item.database.JdbcPagingItemReader;
import org.springframework.batch.item.database.Order;
import org.springframework.batch.item.database.support.MySqlPagingQueryProvider;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.beans.factory.annotation.Value;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.core.task.SimpleAsyncTaskExecutor;

import com.example.domain.Customer;
import com.example.mapper.CustomerRowMapper;

public class JobConfiguration {
	private JobBuilderFactory jobBuilderFactory;

	private StepBuilderFactory stepBuilderFactory;

	private DataSource dataSource;

	public ColumnRangePartitioner partitioner() 
		ColumnRangePartitioner columnRangePartitioner = new ColumnRangePartitioner();
		return columnRangePartitioner;

	public JdbcPagingItemReader<Customer> pagingItemReader(
			@Value("#{stepExecutionContext['minValue']}") Long minValue,
			@Value("#{stepExecutionContext['maxValue']}") Long maxValue) 
		System.out.println("reading " + minValue + " to " + maxValue);

		Map<String, Order> sortKeys = new HashMap<>();
		sortKeys.put("id", Order.ASCENDING);
		MySqlPagingQueryProvider queryProvider = new MySqlPagingQueryProvider();
		queryProvider.setSelectClause("id, firstName, lastName, birthdate");
		queryProvider.setFromClause("from customer");
		queryProvider.setWhereClause("where id >= " + minValue + " and id < " + maxValue);
		JdbcPagingItemReader<Customer> reader = new JdbcPagingItemReader<>();
		reader.setRowMapper(new CustomerRowMapper());
		return reader;
	public JdbcBatchItemWriter<Customer> customerItemWriter()
		JdbcBatchItemWriter<Customer> itemWriter = new JdbcBatchItemWriter<>();
		itemWriter.setSql("INSERT INTO NEW_CUSTOMER VALUES (:id, :firstName, :lastName, :birthdate)");

			(new BeanPropertyItemSqlParameterSourceProvider<>());
		return itemWriter;
	// Master
	public Step step1() 
		return stepBuilderFactory.get("step1")
				.partitioner(slaveStep().getName(), partitioner())
				.taskExecutor(new SimpleAsyncTaskExecutor())
	// slave step
	public Step slaveStep() 
		return stepBuilderFactory.get("slaveStep")
				.<Customer, Customer>chunk(1000)
				.reader(pagingItemReader(null, null))
	public Job job() 
		return jobBuilderFactory.get("job")

2.4 Entity and Mapper class

This is a business model class.

package com.howtodoinjava.batch.decorator.model;

import lombok.AllArgsConstructor;
import lombok.Builder;
import lombok.Data;
import lombok.NoArgsConstructor;

public class Customer 
	private Long id;
	private String firstName;
	private String lastName;
	private String birthdate;

CustomerRowMapper class is used to map the resultset into the Customer domain object.

package com.howtodoinjava.batch.decorator.mapper;

import java.sql.ResultSet;
import java.sql.SQLException;
import org.springframework.jdbc.core.RowMapper;
import com.howtodoinjava.batch.decorator.model.Customer;

public class CustomerRowMapper implements RowMapper<Customer> {

	public Customer mapRow(ResultSet rs, int rowNum) throws SQLException {
		return Customer.builder()

2.5. application.properties

Configuration to create DB connection with MySQL DB.


#Prevents running the job during application context creation 

2.6. JDBC config and schema files

These are schema and SQL data files.

CREATE TABLE customer (
	firstName VARCHAR(255) NULL,
	lastName VARCHAR(255) NULL,
	birthdate VARCHAR(255) NULL

CREATE TABLE new_customer (
	firstName VARCHAR(255) NULL,
	lastName VARCHAR(255) NULL,
	birthdate VARCHAR(255) NULL
INSERT INTO customer VALUES ('1', 'John', 'Doe', '10-10-1952 10:10:10');
INSERT INTO customer VALUES ('2', 'Amy', 'Eugene', '05-07-1985 17:10:00');
INSERT INTO customer VALUES ('3', 'Laverne', 'Mann', '11-12-1988 10:10:10');
INSERT INTO customer VALUES ('4', 'Janice', 'Preston', '19-02-1960 10:10:10');
INSERT INTO customer VALUES ('5', 'Pauline', 'Rios', '29-08-1977 10:10:10');
INSERT INTO customer VALUES ('6', 'Perry', 'Burnside', '10-03-1981 10:10:10');
INSERT INTO customer VALUES ('7', 'Todd', 'Kinsey', '14-12-1998 10:10:10');
INSERT INTO customer VALUES ('8', 'Jacqueline', 'Hyde', '20-03-1983 10:10:10');
INSERT INTO customer VALUES ('9', 'Rico', 'Hale', '10-10-2000 10:10:10');
INSERT INTO customer VALUES ('10', 'Samuel', 'Lamm', '11-11-1999 10:10:10');
INSERT INTO customer VALUES ('11', 'Robert', 'Coster', '10-10-1972 10:10:10');
INSERT INTO customer VALUES ('12', 'Tamara', 'Soler', '02-01-1978 10:10:10');
INSERT INTO customer VALUES ('13', 'Justin', 'Kramer', '19-11-1951 10:10:10');
INSERT INTO customer VALUES ('14', 'Andrea', 'Law', '14-10-1959 10:10:10');
INSERT INTO customer VALUES ('15', 'Laura', 'Porter', '12-12-2010 10:10:10');
INSERT INTO customer VALUES ('16', 'Michael', 'Cantu', '11-04-1999 10:10:10');
INSERT INTO customer VALUES ('17', 'Andrew', 'Thomas', '04-05-1967 10:10:10');
INSERT INTO customer VALUES ('18', 'Jose', 'Hannah', '16-09-1950 10:10:10');
INSERT INTO customer VALUES ('19', 'Valerie', 'Hilbert', '13-06-1966 10:10:10');
INSERT INTO customer VALUES ('20', 'Patrick', 'Durham', '12-10-1978 10:10:10');

2.7. Demo

Run the application as Spring boot application.

import java.util.Date;
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.configuration.annotation.EnableBatchProcessing;
import org.springframework.batch.core.launch.JobLauncher;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.boot.CommandLineRunner;
import org.springframework.boot.SpringApplication;
import org.springframework.boot.autoconfigure.SpringBootApplication;

public class LocalPartitioningApplication implements CommandLineRunner
	private JobLauncher jobLauncher;

	private Job job;
	public static void main(String[] args) {
		SpringApplication.run(LocalPartitioningApplication.class, args);

	public void run(String... args) throws Exception {
		JobParameters jobParameters = new JobParametersBuilder()
                .addString("JobId", String.valueOf(System.currentTimeMillis()))
				.addDate("date", new Date())
		JobExecution execution = jobLauncher.run(job, jobParameters);

		System.out.println("STATUS :: "+execution.getStatus());

The application will read the data from one database using partitions that we created and write it into another table.

2019-12-13 15:03:42.408   ---  c.example.LocalPartitioningApplication   : Started LocalPartitioningApplication 
in 3.504 seconds (JVM running for 4.877)

2019-12-13 15:03:42.523   ---  o.s.b.c.l.support.SimpleJobLauncher      : Job: [SimpleJob: [name=job]] 
launched with the following parameters: [{JobId=1576229622410, date=1576229622410, time=1576229622410}]

2019-12-13 15:03:42.603   ---  o.s.batch.core.job.SimpleStepHandler     : Executing step: [step1]

reading 1 to 5
reading 11 to 15
reading 16 to 20
reading 6 to 10

2019-12-13 15:03:42.890   --- [cTaskExecutor-2] o.s.batch.core.step.AbstractStep         : Step: [slaveStep:partition0] executed in 173ms
2019-12-13 15:03:42.895   --- [cTaskExecutor-1] o.s.batch.core.step.AbstractStep         : Step: [slaveStep:partition3] executed in 178ms
2019-12-13 15:03:42.895   --- [cTaskExecutor-3] o.s.batch.core.step.AbstractStep         : Step: [slaveStep:partition1] executed in 177ms
2019-12-13 15:03:42.901   --- [cTaskExecutor-4] o.s.batch.core.step.AbstractStep         : Step: [slaveStep:partition2] executed in 182ms

2019-12-13 15:03:42.917   ---  o.s.batch.core.step.AbstractStep         : Step: [step1] executed in 314ms

2019-12-13 15:03:42.942   ---  o.s.b.c.l.support.SimpleJobLauncher      : Job: [SimpleJob: [name=job]] completed 
with the following parameters: [{JobId=1576229622410, date=1576229622410, time=1576229622410}] 
and the following status: [COMPLETED] in 374ms


3. Conclusion

In this spring batch step partitioner tutorial, we learned to use partitioning to process bulk data using multiple threads. It enables the application to utilize the full potential of machine hardware and operating system capabilities.

Happy Learning !!

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