Infinispan isn't just a scalable key/value grid platform: it simplifies execution of distributed query tasks in Map/Reduce style, and it integrates a powerful indexing engine to run full-text searches and efficiently extract information from your largest data collections. Infinispan's search engine is built around on Apache Lucene and directly exposes the Lucene API to its users to build powerful search applications which run on data sets residing in-memory. In this talk Navin - author of the Infinispan Query module - will demonstrate how you can leverage the strength of full-text queries to efficiently perform searches on your data.

1. Advanced Queries on the
Infinispan Data Grid
Navin Surtani
13th May 2015
GeeCon, Krakow

2. Who is Navin?
• Worked on Red Hat projects
since 2008
• Infinispan
• Hibernate Search
• Wildfly/JBoss EAP

3. Tweet your questions
@navssurtani
#advancedqueries

4. What are we talking about?
• What is Infinispan?
• The Query module
• Backend tech  Hibernate Search & Apache Lucene
• Setup and configuration
• Demo and code walkthrough

6. What is Infinispan?
• Distributed in-memory key/value data store
• Extension of java.util.Map
• Modes
• Library  Embed into EE/SE application
• Server  Connect remotely

7. Some features
• Fully transactional (JTA, XA)
• Hibernate 2nd level caching
• Full-text querying
• Non-JVM clients for server mode

8. How do I use it?
• Cache  Sit in front of your NoSQL data store
• In-memory DB  Primary data store is in memory
• Clusterability  Manage state that is distributed

9. … but we have a problem here
• How do I find my data?
• I don’t want to give out
keys
• I might not know what I
need to find

10. Query module to the rescue
• Allows searching of values in the cache
• Original project: JBoss Cache Searchable in 2008
• Integration between Infinispan and Hibernate Search
• Became Query module in 2009

11. Full-text search
• Library example:
• Is author name: Surname, Name?
• Name, Surname?
• How do I deal with …
• Special characters?
• Typos?

14. Lucene
• Scalable high-performance indexing
• Small RAM requirement  ~ 1MB heap
• Index size  ~ 20-30% size of data
• 100% open source and written in Java
• Apache Licensing
• Ports to other languages exist

15. Lucene
• Optimised for searching and querying
• Rich feature-set for query types
• Typo-tolerant searches
• Similar keywords
• Document structure
• Unstructured data
• Documents stored in-memory or on disk

16. Two features we will look at
Facets
• Obtain counts, or frequencies
of a result
• O(1) to obtain counts
• EBay counts
Filters
• Filters are:
• Declarative
• Stacking
• Reusable

18. How it all fits together

19. XML Configuration
<local-cache name="Votes">
<transaction mode="NONE"/>
<indexing index="ALL">
<property name="default.directory_provider">
ram
</property>
</indexing>
</local-cache>

20. Programmatic Configuration
ConfigurationBuilder cb = new ConfigurationBuilder();
cb.indexing()
.enable()
.indexLocalOnly() // Will only index local node
.withProperties(properties);
EmbeddedCacheManager cm = new DefaultCacheManager(cb.build());
// My key is an int and value is of type Person
Cache<int, Person> cache = cm.getCache();

21. Annotations required
• @Indexed
• @Field
• @IndexedEmbedded

22. Running queries
// I have a cache instance which is not empty
SearchManager sm = Search.getSearchManager(cache);
QueryBuilder qb = sm.buildQueryForClass(Person.class)
.get();
Query q = qb.keyword().onField(“name”).matching(“Surtani”)
.createQuery();
CacheQuery cq = sm.getQuery(q, Person.class);

24. How it all ties together …
• Web-application using Infinispan running on WildFly 9 CR
• App-server ships with Query module
• Use a web-form to vote in an ‘election’
• One vote for governor
• One vote for senator

25. Flow I: Query ‘warm-up’
• Story: ‘We don’t know who is running in the election’
• WebSocket endpoint to delegate to Worker object
• Worker object executes on CandidateCacheDao
• Returns results through WebSocket endpoint

26. Flow II: Voting form
• Story: ‘This is our ballot paper’
• Front-end creates JSON to go to WebSocket endpoint
• JSON gets parsed by BallotWorker object
• BallotWorker puts parsed JSON into Cache through VotingCacheDao

27. Flow III: Faceted search
• Story: ‘We want to know who has won the election’
• Front-end asks for the result of an election (governor or senator)
• ElectionResultWorker object runs a query through the
VotingCacheDao
• Result passed back to web-page as JSON

28. Flow III: Faceted search with Filter
• Story: ‘We would like to know who has received the most votes
in a particular region’
• Essentially the same workflow as III but we also pass a Filter to our query
• We are using the same query code, except we also filter out our results.

29. Demo time

30. Roadmap
• API:
• JDK 8 integration
• FunctionalCache interface
• Query:
• Query on Non-Indexed fields
• Continuous querying

31. Summary
• Query module 101
• Configuration
• Demo
• Basic query on multiple fields
• Faceted search with and without filter

32. Get in touch
Twitter:
• @navssurtani
• @infinispan
• @c2b2consulting
IRC:
• #infinispan on FreeNode
Blogs:
• navssurtani.blogspot.com
• blog.infinispan.org
• blog.c2b2.co.uk
Demo:
• github.com/navssurtani/query-
demo

33. Q&A

34. #thankyougeecon