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Scaling Up Hibernate/JPA Applications with Infi...
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Galder Zamarreño
April 20, 2013
Technology
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Scaling Up Hibernate/JPA Applications with Infinispan Second-Level Cache
Galder Zamarreño
April 20, 2013
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Transcript
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Scaling Up Hibernate/JPA Applications with Infinispan Second-Level Cache ! Galder
Zamarreño Senior Software Engineer Red Hat, Inc ! 20th April 2013, São Paulo
Galder Zamarreño • R&D Engineer, Red Hat Inc. • Infinispan
developer • Creator of Infinispan second-level cache provider for Hibernate • Escalante founder and lead • Twitter: @galderz
Agenda • Bottlenecks in Hibernate/JPA applications • Applying caching techniques
sensibly • Distribute caches across multiple nodes • Caching in managed environments
Why do we care?
DB = bottleneck Most Hibernate/JPA applications involve intensive I/O workload
against database
Bottleneck problems Bottlenecks decrease throughput, increase latency... and make users
:(((
We care because we want to make users :))
How do we scale up Hibernate/JPA applications ?
Option 1: $$$ Buy/rent more powerful hardware to host the
DB... who can afford that?
Option 2: Caching Use second-level caching sensibly to reduce burden
on database
Second Level Cache JVM-level, or clustered, cache whose contents can
be shared between different transactions
Lesson 1: Don't apply caching blindly! Always measure first!
Caching cost! Local caches have a cost (memory, GC), clustered
caches add more cost (network, IO)
Demo 1: Measure performance
Lesson 2: Think what you are going to cache!
Think about reads Biggest performance gain comes from caching read-
mostly data
What kind of data? Entities, collections of entities, and query
results can be cached
Entity caching Mark entities cacheable, and define the cache concurrency
strategy
Query caching Speed up execution of repeated queries (w/ same
parameters) by caching their results
Query results validity Cached query results are valid as long
as none of the entity types involved are updated
Demo 2: Query cache in action
Lesson 3: How to select a caching provider
Cache Strategies • Read-only • data never updated • insert/delete
allowed • Read/write • data can be updated • with JBDC transactions or no transactions
Cache Strategies • Nonstrict read/write • data rarely updated •
with JBDC transactions or no transactions • Transactional • data can be updated • for JTA environments
Strategy Support Provider / Strategy read- only nonstrict r/w r/w
transactional Infinispan yes no no yes EhCache yes yes yes yes ConcurretHashMap (testing) yes yes yes no
Lesson 4: Scale up your Hibernate/JPA app going multi-node!
Caches form cluster Infinispan uses JGroups to form a peer-to-peer
cluster, providing discovery, failure detection...etc
Clustered Entities Recommended using invalidation to keep entities consistent accross
cluster (replication can be used too)
Queries / Timestamps Recommended keeping query cache local to each
node, but must replicate update timestamps cache
Demo 3: Clustered second-level cache in action!
Lesson 5: Scaling up JPA in managed environments
JPA Caching simplified Infinispan as default cache provider (with sensible
defaults), and ready to cluster!
shared-cache-mode Persistence unit configuration option that should be set to
ENABLE_SELECTIVE
Demo 4: JPA caching in JBoss AS 7
Summary Q - How do we scale up Hibernate/JPA applications?
Enable second level cache!!
Summary • Don't apply caching blindly! Always measure first! •
Think what you are going to cache! • How to select a caching provider • Scale up your app going multi-node! • Scaling up JPA in managed environments
Questions • http://infinispan.org • http://hibernate.org • http://github.com/galderz/secondlc • https://docs.jboss.org/author/display/ ISPN/Using+Infinispan+as+JPA-Hibernate
+Second+Level+Cache+Provider