![Clustered Hello World!
public void main(String[] args) throws IOException {
NamedCache nc = CacheFactory.getCache(“test”);
nc.put(“key”, “Hello World”);
System.out.println(nc.get(“key”));
System.in.read(); //may throw exception
}
• Joins / Establishes a cluster
• Places an Entry (key, value) into the Cache “test” (notice no
configuration)
• Retrieves the Entry from the Cache.
• Displays it.
• “read” at the end to keep the application (and Cluster) from
terminating.](https://image.slidesharecdn.com/gsoraclebettingondatagrids-090714070606-phpapp02/85/Betting-On-Data-Grids-11-320.jpg)
Betting On Data Grids
- 2. <Insert Picture Here> Betting on Data Grids Dave Felcey Oracle Sales Consulting
- 3. Agenda • Oracle High Performance Computing • Oracle Coherence Architecture • Gaming Industry Challenges • Summary
- 4. Oracle High Performance Computing Comprehensive and Best of Breed • Oracle 11g WebLogic Server • Fastest Applicaton Server, delivering 7,311 SPECjAppServer2004 JOPS@Standard (jAppServer Operations per Second) • Oracle JRockit Real-Time JVM • Fastest JVM, delivering 537,116 SPECjbb2005 bops/JVM p/s • Oracle Complex Event Processing • Fraud detection, risk mitigation etc. • Oracle 11g Database • Used by Betfair for performance and scalability and one of top 5 busiest databases in the world • Oracle TimesTen In-Memeory Database • The Hong Kong Jockey Club uses TimesTen to perform very fast fraud detection processing • Oracle Identity Management (IdM) • Used by Shanda to manage ID of upto 2M concurrent users
- 5. Oracle High Performance Computing Comprehensive and Best of Breed Management WebCache WebLogic Server Tuxedo Tools Content Cache J2EE and Messaging Low Latency Monitoring Coherence Data Grid Complex Event TPM Processing SLA’s Low Latency and Mature Scalable Low Latency QoS and Resilient EQL Proven Diagnostics JRockit Real-Time JVM Real- Fast Low Latency Predictable Provisioning TimesTen Berkeley DB In-Memory Low Latency SQL XML Oracle RAC Embedded Commodity Hardware Scale Out Transactional
- 6. Oracle Coherence Data Grid Uses Caching Applications request data from the Data Grid rather than backend data sources Analytics Applications ask the Data Grid questions from simple queries to advanced scenario modeling Transactions Data Grid acts as a transactional System of Record, hosting data and business logic Events Automated processing based on event
- 7. The Coherence Approach… • Consensus is key • Communication is more efficient (peer-to-peer) • No outages for voting (no need – everyone is a peer) • No SPoF, SPoB • No need for broadcast traffic (yelling at each other) • You can do many things once you have “consensus”.
- 8. TCMP Provides the Foundations
- 9. What is Coherence? • Coherence (deployment perspective) • Single Library* • *Other libraries for integration (L2C, Spring…) • Configurable implementations of standard Map interfaces (called NamedCache’s) • Standard Java Archive “JAR” for Java • Standard Dynamically Linked Library “DLL” for .NET connectivity (.Net 1.1 and 2.0) • Standard DLL or .so for C++ clients • No 3rd party dependencies! • Minimal “invasion” on standard code* • “RemoteException” free distributed computing
- 10. Introduction to NamedCaches • Developers use NamedCaches to manage data • An composite interface which includes Map • NamedCache • Logically equivalent to a Database table • Store related types of information (trades, orders, sessions) • May be hundreds / thousands of per Application • May be dynamically created • May contain any data (no need to setup a schema) • No restriction on types (homogeneous and heterogeneous) • Not relational (but may be)
- 11. Clustered Hello World! public void main(String[] args) throws IOException { NamedCache nc = CacheFactory.getCache(“test”); nc.put(“key”, “Hello World”); System.out.println(nc.get(“key”)); System.in.read(); //may throw exception } • Joins / Establishes a cluster • Places an Entry (key, value) into the Cache “test” (notice no configuration) • Retrieves the Entry from the Cache. • Displays it. • “read” at the end to keep the application (and Cluster) from terminating.
- 12. Caching Strategies (schemes) Different cache implementations • Local • Local on-heap caching for non-clustered caching. • Replicated • Perfect for small, read-heavy caches. • Partitioned • True linear scalability for both read and write access. Data is automatically, dynamically and transparently partitioned across nodes. The distribution algorithm minimizes network traffic and avoids service pauses by incrementally shifting data. • Near Cache • Provides the performance of local caching with the scalability of distributed caching. Several different near-cache strategies provide varying tradeoffs between performance and synchronization guarantees.
- 13. The Distributed Scheme - Get
- 14. The Distributed Scheme - Put
- 15. The Distributed Scheme - Failover
- 16. The Near Scheme • A composition of pluggable Front and Back schemes • Provides L1 and L2 caching (cache of a cache) • Why: • Partitioned Topology may always go across the wire • Need a local cache (L1) over the distributed scheme (L2) • Best option for scalable performance! • How: • Configure ‘front’ and ‘back’ topologies • Configurable Expiration Policies: • LFU, LRU, Hybrid (LFU+LRU), Time-based, Never, Pluggable
- 17. The Near Scheme - Get
- 18. Coherence*Extend
- 19. WAN Topology
- 20. Queries • Filters applied in parallel (in the Grid) • A large range of filters out-of-the-box: All, Always, And, Any, Array, Between, ContainsAll, ContainsAny, Contains, Equals, GreaterEquals, Greater, In, InKeySet, IsNotNull, IsNull, LessEquals, Less, Like, Limit, Never, NotEquals, Not, Or… Filter filter = new AndFilter( new EqualsFilter("getTrader", traderId), new EqualsFilter("getStatus", Status.OPEN)); Set setOpenTrades = mapTrades.entrySet(filter);
- 22. Real Time Events • Maintain real time visibility into data changes • Desktops • The usual example is the “Trader desktop” • Watch data change in near real time • Typically a few milliseconds • Servers • Monitoring data to trigger additional processing • Event Driven Architecture within the data grid • Very wide-ranging set of use cases • Not many common patterns of usage
- 23. Continuous Query Cache Coherence implements Continuous Query using a combination of its data fabric parallel query capability and its real-time event- filtering and streaming. The result is support for thousands of client application instances, such as trading desktops. Using the previous trading system example, it can be converted to a Continuous Query with only one a single line of code changed NamedCache mapTrades = ... Filter filter = new AndFilter(new EqualsFilter("getTrader", traderid), new EqualsFilter("getStatus", Status.OPEN)); NamedCache mapOpenTrades = new ContinuousQueryCache(mapTrades, filter);
- 24. Transaction Management • Explicit transaction management • Using the general pattern for pessimistic transactions is "lock -> read -> write -> unlock". For optimistic transactions, the sequence is "read -> lock & validate -> write -> unlock". • Implicit transaction management • Locking "by convention" – for example, requiring that all acessors lock only the "parent" Order object. Doing this can reduce the scope of the lock from table-level to order-level, enabling far higher scalability • Further transaction optimizations • Using EntryProcessors – sending the code to the data, so that operations are queued and all locking is local. Operations must be idempotent.
- 25. Cache Through Reading ahead or on-demand
- 26. Persisting Data Write-through, write-behind, coalescing and batching
- 27. HTTP Session Caching Overview • No code changes required to use • Portlet state can be cached • Built into WLS and WLP Benefits • Enables stateless middle tier • Better hardware utilization • Simpler network infrastructure • Facilitates modular application improvements • Scales out middle tier
- 28. Serialization Portable Object Format (POF) • Benefits • Can store more data • Can read/write and move data faster 5x Smaller 10x Faster De-Serialization Coherence Serialization Test Results Coherence Compression Test Results 12000 10078 1000 900 867 10000 800 8000 700 600 Time (ms) 6000 Bytes 500 400 4000 309 322 1625 2070 300 2360 1234 186 2000 200 484 100 0 734 De-serialization 547 0 Serialization Java Serialization Java ExternalizationLite XMLBean POF De-serialization ExternalizationLite XMLBean Se rialization M echanisum POF
- 29. Coherence Incubator Patterns • Pre-built examples • Used in production systems • Thoroughly tested • Extensible • Optimised • Incorporate best practice
- 30. Gaming Challenges • Extreme scalability 500k+ users • Reliability. Outages damage reputation and can cost £100k+ p/hr • Flexibility. Enable products to be quickly brought to market
- 31. Extreme Scalability • Scaling Users • 100k – 1M online users • Asynchronously update database so reduce latency, open connections etc. • Scaling Transactions and Processing • Betfair • INCERNO processed 5k TPS in simulation tests with no discernable deterioration in performance or reliability. • Scaling Data Capacity, >100 GB • Off-heap storage option in release 3.5 • Potential storage limit now > TB
- 32. Extreme Reliability • Non-Stop running • 2 years+ continuous running • Withstand database or link replication failure • Queue requests • Failure of multiple servers • No ‘Single Point Of Failure’ • Processing (as well as data) failover
- 33. Extreme Flexibility • Native Java, C++ and .NET clients • Simple Map and IDictionary API • Simple to install • Pre-built examples (Incubator Projects) • Seamless HTTP Session integration for J2EE and .NET • Support of Hibernate, JPA and Spring Support • Active forums and SIG’s • Well documented
- 34. Summary • Coherence™ is the leading product for high <Insert Picture Here> performance distributed in-memory data services • Proven technology, 100+ customers and 1500+ production systems • Offers a unique combination of features • Coherence™ is easy to use and delivers data performance, scalability and reliability