Three Key Concepts for Understanding JSR-352: Batch Programming for the Java Platform

Timothy C. Fanelli - Senior IT Specialist
23 September 2013

Three Key Concepts for
Understanding JSR-352:
Batch Applications for the
Java Platform

© 2013 IBM Corporation

Important Disclaimers
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2


About me
§ Timothy C. Fanelli
§ Senior IT Specialist, IBM - Mainframe
Workload Modernization
§ Instructor of Software Engineering Clarkson University, Potsdam NY
§ tim@timfanelli.com
§ tfanelli@us.ibm.com, tfanelli@clarkson.edu
§ Visit the IBM booth #5112 and meet other
IBM developers at JavaOne 2013
3


Agenda
§ Background
§ Three Key Concepts
– Implementation
– Orchestration
– Execution
§ An Example JSR 352 Application
§ Advanced Topics
– Splits and Flows
– Partitioning
– Java EE
§ Conclusion and Thoughts on What’s Next...

4


Background


Batch Processing
§ One of the oldest processing paradigms
§ Historically associated with mainframe computing
§ Still incredibly relevant today, with fresh challenges in an OLTP driven world

6


Java for Batch Processing?
§ Mainframe developers have shied away from Java
– Performance concerns over native languages
– Integration concerns for legacy data
– Disparate developer skill set between System Z and Java
§ Java and JavaEE have dominated the Online Transaction Processing world
§ Time to bridge the two worlds together
– IBM Java for zOS, IBM WebSphere, and Spring Batch paved new paths
– Just-in-Time Compilation, Garbage Collection optimizations proved it out
– Adoption is wide-spread!
§ Only remaining challenge was the lack of a standard
– The need for JSR-352 was obvious

7


JSR 352: Batch Applications for the Java Platform
§ Expert working group formed 29 November 2011
– IBM*, VMWare, RedHat, Oracle, Credit Suisse, Independent participants
– Broad range of talent with deep batch experience
§ Final Release 24 May 2013
§ Included in Java EE 7!

8


Three Key Concepts...


Three Key Concepts ...
§ JSR 352 defines
– Implementation: A programming model for
implementing the artifacts
– Orchestration: A Job Specification Language,
which orchestrates the execution of a batch
artifacts within a job.
– Execution: A runtime environment for executing
batch application, according to a defined
lifecycle.

Orchestrate

Implement

Execute

§ Note: “key” concepts, not “new” concepts!
– Roles and abstractions should be familiar to
SOA and JavaEE developers
10


Anatomy of JSR352
§ Those concepts define the anatomy of JSR 352: Batch
Applications for the Java Platform...

Listeners
Contexts

Listeners

Partitioning

Batchlet

Orchestrate

Implement

Job
Operator

Job

Reader

Step

Processor

Chunk

Writer

Execute

Job Repository
Chunk
Chunk
Chunk
Listeners

11


Implementation: The programming model
§ Chunk and Batchlet provide models for implementing
a step.
§ Contexts provide Job- and Step- level runtime
information, and provide interim data persistence.
§ Listeners provide callback hooks to respond to
lifecycle events on batch artifacts.

Listeners
Contexts

Listeners

Partitioning

Batchlet
Reader
Processor

Chunk

§ Partitioning provides a mechanism imposing parallel
processing on jobs and steps

Writer

Chunk
Chunk
Chunk
Listeners

12


Implementation: The programming model
Chunk vs Batchlet
§ Both are implementations of a step within a batch job
§ The chunk model
– Encapsulates a very common pattern: ETL
– Single “reader”, “processor” and “writer”
– Reader/Processor combination is invoked until
an entire “chunk” of data is processed
– Output “chunk” is written atomically
§ Batchlet provides a “roll your own” step type
– Invoked and runs to completion, producing a
return code upon exit.

13

Listeners
Contexts

Listeners

Partitioning

Batchlet
Reader
Processor

Chunk

Writer

Chunk
Chunk
Chunk
Listeners


Orchestration: The Job Specification Language (JSL)
§ The JSL defines a batch job as an XML document
§ Describes a step as an assemblage of batch artifacts

Job

Step

§ Provides for the description of steps, step groupings,
and execution sequencing

14


Execution: The JobOperator and Repository
§ JobOperator is the runtime interface for job
management, including start, stop, restart and job
repository related commands
§ The Job Repository holds information about
completed and executing jobs

Job
Operator

Job Repository

§ To start a batch job, get a JobOperator instance use
it to start a job described (described by JSL).

15


Execution: JobInstance, JobExecution, and StepExecution
§ The state of a job is broken down into various parts,
and persisted in the repository
– Submitting a job creates a JobInstance, a
logical representation of a particular “run” of a
job.
– A JobExecution is a single attempt to run a
JobInstance. A restart attempt creates another
JobExecution
– Similarly, a StepExecution is a single attempt to
run a step within a job. It is created when a step
starts execution.
Job
Operator

16

Job

Step

*
JobInstance

*
JobExecution

*

*

StepExecution

Job Repository


An Example JSR 352
Application


The Application
‣ A typical “batch hello world”:
– Reads strings from an input file
– Performs some validation or transforms
– Writes validated or transformed string to an output file
‣ Key capabilities
– If something goes wrong, we don’t want to discard all the
prior work; and we want to pick up where we left off
– We want control over the transaction scoping so prevent
lock contention in high volume periods
– We want flexibility to “plug and play” where our records
come from
– For unit testing, development testing, and QA testing records may come from a variety of sources

18


The Design
‣ Let’s implement a string-transform in an extract-transform-load pattern
‣ We’ll use JSR352’s Chunk programming model
– Encapsulates the ETL pattern components as Reader, Processor, and Writer interfaces
– Loosely coupled artifacts will be orchestrated into a single-step job later
– “Free” checkpoint/restart capability
– Transaction scoping imposed externally in the job descriptor
‣ Job will be executed as a Java SE command line batch application

19


The Code
Implement

‣ An ItemReader encapsulates the data
access and deserialization of a record.
‣ No restriction on data access paradigm: use
DAO patterns, JDBC, JPA, Hibernate,
Spring Data, etc!
‣ Checkpoint/Restart data provided as
Serializable argument to “open” and from
“checkpointInfo” methods.
20


The Code
Implement

§ An ItemWriter is the output counterpart to
ItemReader
§ Primary difference is that writeItems
accepts a “chunk” of output objects (as a
list) to serialize.
§ Again, no restriction on data access
paradigm!

21


The Code
Implement

§ An ItemProcessor encapsulates the
business logic applied to each record
§ “main” here demonstrates the invocation of
a batch job, using the JobOperator
§ Would typically not be in the processor
implementation

22


The Batch Descriptor and Job Specification
Orchestrate

§ batch.xml defines and names
the batch artifacts in this
application archive
§ sample.xml is an example Job
Specification Language
document for SampleBatchApp

23


The Execution
Execute

§ Package the application as a standard JAR or WAR
for deployment in JavaSE or EE environments
– batch.xml goes in META-INF or WEB-INF/
classes/META-INF
– JSL may go in META-INF/batch-jobs, or
submitted from an external source (up to the
provider!)

24


The Execution
§ See it live?

25

Execute


Advanced Topics


Job Management - Restart, Stop, Abandon
§ Had something gone wrong, what then?
– The “main” program shown was too simple... only
“started” the job

Execute

§ JobOperator exposes APIs for a variety of job
management tasks: start, stop, abandon, restart
– Would have had to take advantage of these for
advanced job management capabilities.
§ The door is left open for more advanced batch job
management systems to be built!
– Integration into existing enterprise schedulers?
– New Java EE batch scheduling standard?
– Plenty of options, but currently left to the provider
to implement
27


Java EE Integration
§ JSR-352: Java Batch is included in Java EE 7

Execute

§ Provides EE clustering, security, resource
management, etc to Java Batch applications
§ Performance benefits to dispatching into longrunning, reusable container
– JIT compilation through the first couple runs
– Eliminates overhead of starting / stopping JVM

28


Parallel Job Processing
§ Splits and Flows provide a mechanism for executing
job steps concurrently at the orchestration layer

Orchestrate

§ A flow is a sequence of one or more steps which
execute sequentially, but as a single unit.
§ A Split is a collection of flows that may execute
concurrently
– A split may only contain “flows”; a step is not
implicitly a flow
§ This is done entirely in the JSL descriptor
– Imposed on the batch application with no code
changes!

29


§ Step-level parallelism can be achieved programmatically using
step partitioning

Implement

§ A partitioned step runs as multiple instances with distinct
property sets
§ PartitionMapper defines the number of partitions, and property
values for each partition
– Can be a fixed set of partitions in JSL
– Can be dynamic using a PartitionMapper implementation

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31


Wrap up...


Batch Processing
§ The oldest “new thing” in Java
§ JSR 352 applies the modern thinking and abstraction of Java EE and SOA and applies it to
sequential batch processing
§ The standardized programming model provides application developers vendor portability
§ Inclusion in Java EE 7 ensures wide spread availability

33


Questions?
Find this presentation and more!
http://ibm.co/JavaOne2013


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