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Optimizing mass indexing
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Reindexing your entire data set or a significant part of your data set can be a long process if you do it the wrong way. This section will help you to make it as efficient as possible. Indexing a huge data set involves two distinct but interwoven operations: You need to read objects from the database, and then you need to index them. Most of the performance problems arise on the first operation. Hibernate Search needs to have access to the object and all its associated indexed objects during the indexing operation. The object, its @IndexedEmbedded associations, and its @ContainedIn associations (recursively) form the object graph used during indexing. Use the following techniques to load this graph as efficiently as possible:
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Write the appropriate query using joins. Use @BatchSize or FetchMode.SUBSELECT when a direct join cannot be used. Load lazy properties if they are indexed (for example, use the fetch all properties syntax in HQL).
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If you don t pay attention, Hibernate Search might trigger some n+1 load problems while indexing your data. Listing 9.2 is an example of the query needed to load Item objects provided that their Distributor association is marked as @IndexedEmbedded.
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Listing 9.2 When indexing objects, load using a query to minimize n+1 problems
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Criteria query = session.createCriteria( Item.class ) .setFetchMode( "distributor", FetchMode.JOIN ) .setResultTransformer( CriteriaSpecification.DISTINCT_ROOT_ENTITY );
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B Load necessary associations C
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Distinct them (collection load)
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Performance considerations
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You need to fetch associations involved in the indexing process for this entity by using FetchMode B (you can also use static mappings such as @BatchSize). If you load collections, make sure you distinct the results to avoid indexing the same object multiple times C. (SQL loads the data for your main object for each element in your collection.)
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How to make sure all necessary data is fetched
Several strategies are available to make sure you haven t missed an association. The first one is to enable Hibernate Core logging and check queries going through. An n+1 pattern is readily recognizable; after a given query, you ll see a second query executed many, many times. The second approach is to use the Hibernate Core statistics API (available in SessionFactory.getStatistics()). When an entity is loaded under the cover, Hibernate increases getEntityFetchCount(). Be sure to enable statistics before testing by using statistics.setStatisticsEnabled(true).
The second most common problem occurs because your indexed data set cannot fit all data in memory (usually). If you attempt to load all objects, then index them, you ll likely face an OutOfMemoryException before you even finish loading objects. Make sure you use a scrollable result set and flush your index work queue and clear the session on a regular basis. This will limit the number of objects loaded into memory at a given time. Listing 9.3 demonstrates a typical mass indexing routine.
Listing 9.3 Mass indexing routine limiting objects loaded into memory
private static final int BATCH_SIZE = 1000; private static final int FETCH_SIZE = 100; public void indexAllItems() {
Run in a transaction
FullTextSession session = SessionHolder.getFullTextSession(); Criteria query = session.createCriteria( Item.class ) .setFetchMode("distributor", FetchMode.JOIN) .setResultTransformer( Minimize cache CriteriaSpecification.DISTINCT_ROOT_ENTITY) interaction .setCacheMode(CacheMode.IGNORE) .setFetchSize(FETCH_SIZE) Align batch .setFlushMode(FlushMode.MANUAL); size and JDBC
Disable
flush ScrollableResults scroll = query.scroll( ScrollMode.FORWARD_ONLY); Scroll in forward mode only int batch = 0; scroll.beforeFirst(); while ( scroll.next() ) { batch++; session.index( scroll.get(0) ); if (batch % BATCH_SIZE == 0) {
C fetch size
Optimizing indexing
//no need to session.flush() //we don't change anything session.flushToIndexes(); session.clear(); } } //the remaining non flushed index work //processed at commit time }
E Flush indexclear works and
the session
B It is a good practice to ignore the cache when dealing with a huge amount of data. The benefit doesn t overcome the cost of maintaining cache coherence. C Align JDBC
fetch size with the batch window size so that FETCH_SIZE = BATCH_SIZE/n to minimize memory consumption and database round trips. D Scrolling in forward mode loads objects on demand and makes sure objects (and their JDBC row representation) are garbage collectable. E After every BATCH_SIZE operations, flush changes to the indexes (flushToIndexes()) and free objects from the session (clear()). This will limit memory consumption. Be sure to run the indexing process in a transaction. If you don t, Hibernate Search needs to apply index changes when fullTextSession.index() is called, leading to expensive Lucene index opening and closing for each Item.
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