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Parallel batch processing with spring batch slideshare
The document discusses lessons learned from using Spring Batch to process pension recalculations in parallel. It describes how the initial implementation used Hibernate in the item reader, which caused problems with concurrent sessions. The solution was to remove Hibernate from the reader and instead use JDBC to fetch primary keys and let the processor fetch related data using Hibernate. This improved performance significantly by allowing more parallelization and avoiding issues with concurrent sessions. The key lessons are to avoid Hibernate in the reader, test parallelization early, and monitor SQL performance.
In this document
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Overview of parallel processing in Spring Batch presented by Morten Andersen-Gott.
Introduction to problems faced in Spring Batch, supported by insights from the Norwegian Public Service Pension Fund.
Critical execution time requirements for SPK’s recalculation batch within a one-week timeframe.
Detailed structure of Spring Batch including steps, chunks, item reader, processor, and writer concepts.
Multi-threaded and partitioned step implementations for managing batch processes.
Fundamental concepts of Hibernate relevant to batch processing.
Detailed steps for pension recalculation batch processing using staging tables.
Challenges and solutions related to using Hibernate in the reader component of Spring Batch.
Strategies for managing errors in batch processing, emphasizing appropriate exception handling.Adoption of partitioned steps over multi-threaded steps to alleviate deadlock issues.
Implementing JDBC instead of Hibernate to improve performance and scalability in batch processing.
Achievement of performance goals and suggestions for best practices in batch processing.
Advice on efficient entity loading strategies in Spring Batch.
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Parallel batch processing with spring batch slideshare
- 1. Parallel processing with Spring Batch Lessons learned
- 2. Morten Andersen-Gott • Manager at Accenture Norway • 30 years old • Been using Spring Batch since 1.0M2 (2007) • Member of JavaZone program committee – http://tinyurl.com/javaever – http://tinyurl.com/thestreaming – http://tinyurl.com/ladyjava mortenag www.github.com/magott www.andersen-gott.com
- 3. A BoF focusingon the problems • Functional background for the batch • Short introduction to Spring Batch • Even shorter on Hibernate • The problems • The problems • The problems
- 4. Norwegian Public Service Pension Fund (SPK) • Norway’s main provider of public occupational pensions • Also provide housing loans and insurance schemes • Membership of the Norwegian Public Service Pension Fund is obligatory for government employees • Stats – 950,000 members across 1600 organisations – Approx 138,000 receive a retirement pension – Approx 58,000 receive a disability pension – 950,000 members have total accrued pension entitlements in the Norwegian Public Service Pension Fund of 339 billion kroner.
- 5. Background • Every yearthe parliament sets the basic amount of the national insurance • This amount is a constant used in calculation of all benefits • When the basic amount is changed, all benefits must be recalculated • It’s more complex than a constant in an algorithm – Rules are result of political games the last 50 years – Complex rules – Lots of exemptions
- 6. Execution time requirements •SPK’s recalculation batch must run – After the basic amount is set – After the Labour and Welfare Administration has done it’s calculation – Before the pensions are due next month • Window of 1 week – Will ideally only run during weekends – Can not run while case workers are doing their job
- 7. Spring Batch • Frameworkfor developing batch applications • Implements batch semantics – Steps, Chunks, Stop, Restart, Skips, Retries – Partitioning, Multithreading, Parallel steps
- 8.
- 9. The components • ItemReader – read() returns one row at the time – Step is completed once read()returns null • ItemProcessor – process(item) item is return value from read() – Business logic goes here – Items can be filtered out by returning null • ItemWriter – Write(list) list of items returned from process()
- 10. The code <job id=”foo”> <step id="fileImport"> <tasklet> <chunk commit-interval="10" reader=“reader" processor=“processor" writer=“writer“/> </tasklet> </step> </job> <bean id=“reader" class="..."> <bean id=“processor" class="..."> <bean id=“writer" class="...">
- 11. Chunk • A chunkis a unit of work – Executes within a transaction – Size is defined by number of items read • A step is divided into chunks by the framework • When x is the chunk size – read() is called x times – process() is called x times – write is called once with a list where list.size()==x (minus filtered items)
- 12.
- 13. Multi-threaded step Id Name 1 Paul 2 John 3 Lisa Execution Reader Step 4 Simon Processor T1..* 5 Rick Writer 6 Julia 7 Olivia 8 Scott 9 Martin
- 14. Partitioned Step Id Name Execution Reader Step 1 Paul T1 Processor 2 John Writer 3 Lisa Execution Reader Step 4 Simon Processor T2 5 Rick Writer 6 Julia Execution 7 Olivia Reader Step Processor T3 8 Scott Writer 9 Martin
- 15. Hibernate 101 • Proxy •Session cache • Flushing – Queries – Commit
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- 17. - Stored procedure Step1 Populate staging table - Read from staging - Fetch additional data Step 2 Rules - Call out to rules engine engine - Store result as pending pensions - Loop through pending Step 3 pensions and activate - Create manual tasks Step 4 for failed items
- 18. Staging table • Abatch processing pattern • A simple table with a identity column, functional key and processing status • Restart is easy – Select soc_sec from staging where processed=false • An easy way to get parallel processing capabilities
- 19.
- 20. Reader Updates family status Processor Step 2 ILog Sybase Writer
- 21. Hibernate in thereader • Using a separate statefull session in reader – Not bound to active transaction – Is never flushed – clear() is called before commit • Entities are written to database using a (different) transaction bound session – Used by processor and writer
- 23.
- 24. What happened? org.hibernate.HibernateException: Illegal attempt to associate a collection with two open sessions
- 25. Why • Family.errors andFamily.persons are attached to reader’s session • Attempting to attach them to transaction bound session • Hibernate will have non of that!
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- 27.
- 28. Hibernate in thereader (Second attempt) • Let’s try stateless session • Default behavior in Spring Batch’s Hibernate ItemReaders – useSateless=”true” • LEFT JOIN FETCH for eager loading collections – Avoiding LazyLoadingExceptions
- 29. Hibernate in thereader (Second attempt)
- 30.
- 31. What happened? • org.hibernate.HibernateException:cannot simultaneously fetch multiple bags
- 32. Why • Hibernate isunable to resolve the Cartesian product – Throws exception to avoid duplicates
- 33. You are usingit wrong!
- 34. Curing the Problems? you are using it wrong syndrome
- 35. Hibernate in thereader (The third attempt) • Examine the object graph • Replace List with Set – Only one eagerly loaded collection may be of type list List • This works… – ..for a while – We’ll revisit Hibernate later…
- 36. Exception resilience • Newdemands sneak in – The batch should not abort • Not under any circumstance • The batch should deal with – Missing or functionally corrupt data – Programming errors – ...
- 37. Pseudo code try{ //do data and business operations }catch(Exception e){ //Add error to staging & continue family.addError(createError(e)); }
- 38.
- 39. Overzealous exception handling anassertion failure occurred (this may indicate a bug in Hibernate, but is more likely due to unsafe use of the session)
- 40. Overzealous exception handeling The solution • Some exception MUST result in a rollback – Ex. StaleStateException • Configure the framework to do retry/skip for these • Only catch exceptions you know you can handle in a meaningful way – Nothing new here – Do not succumb to crazy requirements
- 41. Time to turnon parallelization • We chose partitioned over mutli-threaded step – No need for a thread safe reader • Step scope • Each partition get a new instance of reader – Page lock contentions are less likely • Row 1 in partition 1 not adjacent to row 1 in partition 2
- 42. Deadlocks • Legacy databaseusing page locking • Normalized database • Relevant data for one person is spread across a number of tables • Different threads will access same data pages • Deadlocks will occur
- 43. Page locking ID NAME 1 Paul T1 2 John T2 waiting 3 Simon 4 Scott T1 waiting 5 Lisa T2 6 Jack 7 Nina 8 Linda T3
- 44.
- 45. Retry to therescue <step id=”step2"> <tasklet> <chunk reader="reader" processor="processor" writer="writer" commit-interval="10" retry-limit="10"> <retryable-exception-classes> <include class=”…DeadlockLoserDataAccessException"/> </retryable-exception-classes> </chunk> </tasklet> </step>
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- 47.
- 57.
- 58.
- 66.
- 67. What do wedo?
- 68. What we shouldhave done weeks ago
- 69. We ditch Hibernate …well,almost anyway
- 70. Removing Hibernate from reader • ItemReader is re-configured to use JDBC • Fetches primary key from family staging table • ItemProcesser fetches staging object graph – Uses primary key to fetch graph with hibernate • Primary keys are immutable and stateless
- 73. End result • Performance requirement was 48 hrs • Completed in 16 hrs • Used 12 threads • C-version used 1 thread and ran for 1 week – Stopped each morning, started each evening • A batch that scales with the infrastructure – Number of threads is configurable in .properties
- 74. What we wouldhave done differently • Switched from partioned to multi-threaded step – All work is shared among threads – All threads will run until batch completes – Avoid idle threads towards the end – With partitioning some partitions finished well before others
- 75. Recommendations • Do notuse Hibernate in the ItemReader • Test parallelization early • Monitor your SQLs – Frequently called – Long running • Become friends with your DBA • There is no reason to let Java be the bottle neck – Increase thread count until DB becomes the bottle neck
- 76.
- 77. Pro tip: @BatchSize •If one lazily loaded entity is fetched, they are all fetched – in one query
Editor's Notes
- #4 trials and tribulationsfairy tale
- #5 Offers a lot of different benefits for employees or former employees of the public sector in NorwayPlays a major part in the personal economy for a lot of Norwegian citizens
- #6 To reflect changes in consumer price indexBenefits
- #7 Labour and Wellfare Administration provides the minimum pensions for all Norwegian citizensWe rely on the calculations from the Wellfare Administration in our batch
- #9 2 loopsOuter: Loops while read returns dataInner: Loops over read+process according to commit-interval
- #15 Data is partitioned before step executesThreads are processing isolated data setsExamples of partitioning strategiesOne file per partitionPartitioning a tablePartition 1: Row 1 – Row 1000Partition 2: Row 1001 – Row 2000Partition 3: Row 2001 – Row 3000
- #48 Indicates that session contained stale dataThrown when a version number or timestamp check failsAlso occurs if we try delete or update a row that does not existStarted doing the numbers, how much can we do within 48 hours with a single thread?Weknewthattwothreadswould never accessthe same row. Same page, sure, but never same row.
- #62 The ItemReader is forward-onlyWe can’t roll back a cursor next()Spring batch stores all read items in a retry cache until transaction is committedUses the cache for retries
- #68 Hibernatethrows an exceptionwhenrealitydoes not match itsexpectations