Poor man's batch processing in JPA

java

Working with a lot of data using plain JPA or hibernate or spring-data is possible, it does work and usually, it doesn’t require a lot of extra coding. Starting small is the fastest way of getting things done but you must be aware of few things that might shoot you in the foot.

tl;dr

control session cache size. Call flush() and clear() regularly to reduce memory and time usage

control transaction size

figure out deterministic way of fetching data

don’t be afraid to remove guerilla solution and commit to framework/library designed for the job

In general

When working with big chunks of data it’s important to control entity manager size and flush results to the database regularly. Remember that entity manager should be flushed and then cleared. Flush doesn’t clear session cache! If you don’t clear entityManager regularly each entity you save (and flush) stays in entity manager and consumes RAM. What’s more, it’s not only about memory usage because you can always buy more RAM. Hibernate dirty checking will have to verify all items in the session cache before flushing data to the DB. So first time it will check 100 entities, next time it will check 200, next there will be 300 and it will grow unless the cache is evicted. Working with big data loads in JPA/hibernate forces you to control some things and it’s your job to deallocate memory and think ahead.

Another important thing is transaction management. In case of very long running tasks you might want to split single job across multiple transactions (for example one results page = one transaction). If the job is not required to be executed in a single transaction you should split it into the smallest possible units of work. There is nothing more annoying than transaction running for two hours and failing because someone updated minor detail, which updated a version field in the DB, which, caused batch processing transaction to roll back… On one side of the coin it is a good idea to keep transaction as small as possible but on the other side executing a lot of small transactions might be slower than few bigger ones. It is your decision and you should select transactions size just right for your application.

The last thing is entities order. If your job is running for some time you should decide how to handle new records or updated ones. Ids which are usually sequentially ascending numbers might be good starting point for data sort (won’t work with updates but it’s a start). The general idea is to have a deterministic way of fetching data and strategy on how to handle updates and new entities. Last time I was forced to do a bit of batch processing in plain JPA I’ve used simple last_updated_time which was already there and was perfect for the job. In more complex situations in which you must split your job across multiple transactions you might consider fetching list of all ids up front.

Transaction management and cache size control are application specific but you should be aware of them since the beginning because not thinking about it now might cost you a lot of time in the future. Another thing worth pointing out is that you should always verify if your application works fine with production like data. Writing some tests running on in memory DB is convenient but without load testing you will never be sure if you batch job will not fail after processing one million of entities.

If your batch processing is getting complicated and you feel like there is more and more corner cases you need to handle you should consider switching to something more sophisticated like spring-batch.

Example of data insert:

Data insert is very simple just remember about clearing entity manager and be aware of the transaction size which is ignored in this example ;) You can try and extract transactional methods which will actually insert portions of the data (which will be ugly because @Transactional requires public method) or fallback to plain TransactionTemplate which will allow to hide some of the implementation details.

@Component @RequiredArgsConstructor class DataInsert { private final EntityManager entityManager ; public void insertProducts ( Iterable < Product > products ) { int batch = 0 ; for ( Product product : products ) { entityManager . persist ( product ); batch ++; if ( batch % 100 == 0 ) { flushAndClear (); } } flushAndClear (); } private void flushAndClear () { entityManager . flush (); entityManager . clear (); } }

Note flush and clear (in that order, otherwise nothing will be persisted) executed once in awhile.

Examples of data fetch

Plain JPA

@Component @RequiredArgsConstructor class JpaIterable implements Iterable < Product > { private final EntityManager entityManager ; @Override public Iterator < Product > iterator () { return iterator ( 100 ); } public Iterator < Product > iterator ( int batchSize ) { return new ProductIterator ( batchSize ); } private class ProductIterator implements Iterator < Product > { private final int pageSize ; private int firstResult = 0 ; private Iterator < Product > productsIterator ; ProductIterator ( int pageSize ) { this . pageSize = pageSize ; } @Override public boolean hasNext () { if ( productsIterator == null || ! productsIterator . hasNext ()) { loadNextPage (); } return productsIterator . hasNext (); } @Override public Product next () { if (! hasNext ()) { throw new NoSuchElementException ( "No more elements" ); } return productsIterator . next (); } private void loadNextPage () { final List < Product > productsPage = entityManager . createQuery ( "from Product order by id" , Product . class ) . setFirstResult ( firstResult ) . setMaxResults ( pageSize ) . getResultList (); firstResult += productsPage . size (); productsIterator = productsPage . iterator (); } } }

We can create a stupid simple iterator which is responsible for fetching next page when we’ve fetched all data on the current page. Above is not a perfect implementation but will do for the simplest use cases.

Note usage of iterator and iterable interfaces. Squeezing implementation details into the standard is usually a good idea. In this case, you can replace fetch implementation without affecting processing logic. It is leaking because you need to remember about flush and clear, but it is still better than exposing implementation to the outside. With java8 you should consider using stream, but you can convert iterator to a stream at any time using StreamSupport and Spliterator

Hibernate

If you are using Hibernate anyway and don’t mind polluting your code with filthy implementation details (;)) you can take advantage of ScrollableResult which allows to do exactly the same we did in plain JPA.

@Component @RequiredArgsConstructor class HibernateIterable implements Iterable < Product > { private final EntityManager entityManager ; private class ProductIterator implements Iterator < Product >, Closeable { private final ScrollableResults scrollableResults ; private Object [] nextRow ; ProductIterator () { final Session session = entityManager . unwrap ( Session . class ); this . scrollableResults = session . createQuery ( "from Product order by id" ) . scroll ( ScrollMode . FORWARD_ONLY ); } @Override public boolean hasNext () { if (! hasNextRow ()) { return goToNextRow (); } return hasNextRow (); } @Override public Product next () { if (! hasNext ()) { throw new NoSuchElementException ( "No more results" ); } try { return ( Product ) nextRow [ 0 ]; } finally { goToNextRow (); } } @Override public void close () throws IOException { scrollableResults . close (); } private boolean goToNextRow () { scrollableResults . next (); nextRow = scrollableResults . get (); return hasNextRow (); } private boolean hasNextRow () { return nextRow != null ; } } }

Like in plain JPA example we still can squeeze and hide hibernate implementation in standard java interfaces (note that it is more complicated than plain JPA, but it is still worth considering when you decide to use StatelessSession). StatelessSession will detach any entity right after a fetch (if it’s what you want). It might come in handy when all you need to do is read data.

When it comes to hibernate there are a lot of configuration switches which will allow you to control things.

Spring-data

Spring data supports java8 streams so there is not much to there. Just create repository and make sure you’ll return stream of entities:

interface SpringDataStream extends Repository < Product , Long > { @Query ( "from Product order by id" ) Stream < Product > findProducts (); }

One thing worth mentioning is that once you are done with the stream it should be closed (you can close it manually in the final block, or wrap it in the try with the resource block).

From all of the above spring data saves us a lot of writing and testing, but sometimes it is good to know what’s going on under the hood.

Cache invalidation after data fetch

After loading data into hibernate’s session cache it is important to invalidate it so used memory can be released. How to perform cache clean depends on the use case. When working with iterators you have more control and can free some memory once you are done with the particular part of the task. For example, you can call em.clear() every 100 processed records or something like that, just remember that it might be tricky to keep iterator implementation independent from the processing logic. If processing logic starts to leak into iterator implementation or another way around you can fall back to Page or Slice (classes from the spring-data but you can do it on your own if you don’t want to use spring). Working with the streams might be a bit easier (depending on the problem) once you are done with one particular entity you can simply detach it from session cache and garbage collector will do the rest.

Data removal

I prefer to use plain sql/jpql/hql/whatever with where condition which will handle removal of everything I want to remove. Two things that are important about data removal using sql. One is to ensure that any pending entity manager state should be flushed before executing the update. Second is that you should clear session cache after updating statement (or just before it) because the query was executed directly on database and session cache might be not aligned with DB state after removal operation.

If plain sql is not possible because some additional business logic must be executed when deletion is performed then I’d approach in the same manner as an update.

samples

See Also If you've enjoyed or found this post useful you might also like: Java repositories testing

Optimistic locking pitfalls

16 May 2017 #spring #hibernate

Please enable JavaScript to view the comments powered by Disqus.