IO is slow, memory is fast. For many applications, the main performance and scalability bottleneck is disk and network access. This session will cover strategies that can help you utilize your RAM efficiently even in a distributed environment. Discover how a clustering solution like Terracotta can help you reduce overall application latency.

1. Riding The Memory Bus Ophir Radnitz

2. Agenda RAM vs. IO Tools of the Trade Introducing Terracotta RAM Patterns with Terracotta

3. Anatomy of an Application Get a DB connection, start transactioning Start a request Network Get a bunch of data Access disk, Serialization Release resources Render stuff

4. I/O Issues Scalability issues Excessive network chatter How do you scale the database? Only as fast as your slowest query Implies serialization/deserialization

5. Bandwidths (1 of 2)

6. Bandwidths (1 of 2)

7. RAM vs. Disk Internet: ~80ms

8. RAM Goodness RAM = High Bandwidth & Low Latency No serialization costs Less network

9. Use Cases Low level cache SQL, HTTP Application cache Hibernate 2nd level cache Short lived entities Queues

10. Generic Tools Memcached A high-performance, distributed memory caching system Used EVRYWHERE: Facebook, Twitter, Digg, Wikipedia, Slashdot, LiveJournal, Sourceforge etc. Squid Routing and load balancing HTTP cache

11. Memcached Limitations for Java Remote cache Serialization 2Gb limit

12. Java In-Memory Tools Prevayler, Space4J Persist objects in memory, supports ACID and file system journaling Db4O in memory, Perst Object oriented database Jofti A high-performance object indexing and searching solution. Supports Map.

13. In-Memory Considerations Reliability requires redundancy And/or durability In a cluster state must be synced Requires state distribution

14. Java Distribution Tools Coherence GigaSpace Ehcache Infinispan Terracotta Hazelcast

15. Introducing Terracotta Network-Attached Memory infrastructure for the JVM Open source (TPL, based on Mozilla License) Current version: 3.0.1

16. Terracotta Customers

17. Introducing Terracotta Highly Available Highly Scalable Avoids excessive state replication Hub and spoke architecture

18. A Nice Diagram Application Application Application Terracotta Server Terracotta Server Terracotta Server Application Application

19. How Terracotta Works Application Terracotta Client Bootjarinstrumenting your application Terracotta Libraries JVM Terracotta Server TC handles JVM locks and data access

20. How Terracotta Works, take 2 Application Application Application Terracotta Server Reference Actual instance

21. Terracotta Features Cross-JVM object identity Automatic Persistence to disk Virtual Memory spill heap to TC and/or to disk, transparently Cluster profiling & visualization JMX-based monitoring and management

22. Terracotta Advantages Easy to integrate, Maven support Great scalability No proprietary API (optional) Ready made integration modules Great diagnostics In/out of process

23. Terracotta Development Console

24. Integrating Terracotta Download & extract Write a tc-config.xml Create a bootjar Run your app with the bootjar Add elements & refactor

25. TIM - Terracotta Integration Modules Packaged functionality Easy to integrate Handles locks details Examples: Concurrent collections, Ehcache Lucene/Compass, Spring Security Pipes, Wicket, Tomcat, Jetty Hibernate, JBoss, Spring etc.

26. Terracotta Elements HTTP Session Replication Cache Evictor Queues Master-Worker Asynchronous Processor Cluster membership events

27. Terracotta Patterns Data Cache Intermediate persistence Queues / work distribution Write behind to database

28. Data Cache Considerations Figure out data lifecycle Read-only, metadata, configuration, session Where cache is applicable Single point of update Set eviction policies LRU, FIFO

29. Data Cache Session cache is easier, entity cache needs real distribution Use a generic cache ConcurrentMap + Map Evictor (+ Jofti) Or a Hibernate 2nd level cache ehcache, TC-Cache

30. Where Should My Objects Live? Memory Database Appropriateness dies quickly stays forever Data Lifetime

31. Intermediate Data Lifetime Some data doesn’t have to be stored in the database Session information Messages / Tokens Transaction Unfinished stuff Data Lifetime

32. TIM Pipes: Queues Execute time consuming tasks asynchronously In a reliable way Load balanced Examples: Send emails, process images Supports pub/sub, routing No need for JMS

33. CommonJ – Work Distribution A joint API spec by Oracle and IBM Provides interfaces for: Worker / WorkItem / WorkManager Scheduler etc… TIM-Messaging provides an implementation Pipes Master / Worker

34. Write Behind to System Of Record Asynchronously persist to DB Work queue “1.5 phase commit” Flags objects as dirty Avoids syncing dirty objects Flushes objects Flags objects as clean

35. Terracotta Drawbacks Concurrency awareness Massive scalability is a commercial feature Requires another machine(s) Sharing objects across applications requires configuration TIMs documentation

36. Terracotta Pros Concurrency awareness Fits into your application Provides great visibility Very versatile usage Scales massively Doesn’t use serialization Great community

37. Summary Ask yourself: Does it belong in the DB? Do I have to go to the DB for that? Do I have to do it now?

38. Terracotta Pros Any Questions?