<Insert Picture Here> Oracle Application Cache Solution: Coherence

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2 <Insert Picture Here> Oracle Application Cache Solution: Coherence 黃開印 Kevin Huang Principal Sales Consultant

3 Outline Oracle Data Grid Solution for Application Caching Use Cases Coherence Features Summary <Insert Picture Here>

4 Performance is Like a Ferrari Performance is like a Fast Car Designed for speed (not capacity) Improve engine and components (scale up)

5 Scalability is Like a Train Scalability is like a Locomotive Designed to handle load and capacity Add more cars and engines (scale out)

6 Scalability and Performance don t always come together You can t just add them together! They have to be designed.

7 Scalability and Performance Sometimes They Do Come Together Again They have to be designed.

8 Application Scalability Scaling the Application-Tier is difficult If it was easy it would be an IDE option Not possible! Scalability is a design option Requires knowledge, care and experience Developers have the option to consider building it in! It s not an IDE option Coherence is scalability infrastructure for the application-tier

9 How Can Coherence Help? Provides a reliable data tier with a single, consistent view of data Enables dynamic data capacity including fault tolerance and load balancing Ensures that data capacity scales with processing capacity Ever Expanding Universe of Users Web Servers Application Servers Data Queries Tx Processing Event Processing Data Supply Data Sources

10 Coherence Scalability Coherence: Designed to scale-out the Application-tier Standard Java Applications (JSE, non-jee, container-less) Web Applications (session state) Middle-tier Applications (JEE, container-based) Artifacts that can been scaled Application and User State (objects) Object Access (crud) State Mutation Notifications (events) Processing (updates, transactions)

11 What About Also Clustered Caching for Scale? Common uses for Clustered Caching HTTP Session Caching for stateful applications Page, Document and Segment Caching Application Data Caching: Your Own Java Objects (YOJOs;-) Load Balancing of Data Operations Information Fabrics Compute Farms Offloading XML Transformations Dramatically reduce database load by using read-through, write-through and write-behind caching There is no better way to increase scalability than to use caching to unload later tiers! 11

12 Cache Coherency is Also Important A cache that is coherent shows the same contents at every location within a distributed or clustered environment Caches of read-only data are automatically coherent! The choice for clustered caching of read/write data: Accept a certain amount of data staleness Maintain cache data coherency across the cluster Clustered data coherency implies a means to synchronize: Clustered concurrency control (like Java synchronized ) Distributed Transactional Caching Interposing the data caches between the application logic and the data source prevents loss of consistency 12

13 How Does Coherence Data Grid Work? Cluster of nodes holding % of primary data locally Back-up of primary data is distributed across all other nodes Logical view of all data from any node All nodes verify health of each other In the event a node is unhealthy, other nodes diagnose state X Unhealthy node isolated from cluster Remaining nodes redistribute primary and back-up responsibilities to healthy nodes

14 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

15 Clustered Hello World public void main(string[] args) throws IOException { NamedCache nc = CacheFactory.getCache( test ); System.out.println(nc.get( key )); } Joins / Establishes a cluster Retrieves the Entry from the Cache. Displays it Start as many applications as you like they all cluster the are able to share the values in the cache

16 Sharing data between Java,.Net and C++.Net Application Java Application C++ Application.Net Coherence Coherence Java C++ Coherence POF Coherence POF

17 Outline Oracle Data Grid Solution for Application Caching Use Cases Coherence Features Summary <Insert Picture Here>

18 Oracle Coherence Over 100 Direct Customers and 1,500+ Production Installations

19 Clustered Named Cache

20 Clustered across Multiple Platforms

21 Clustering Application Servers

22 Coherence*Web: Session State Management Java - Tomcat, Weblogic, WebSphere, Jboss, Jetty, Oracle Application Server, SunOne, Resin.NET IIS Web Application Coherence Web Java EE or Servlet Container Application State Router Web Application Web Tier Load Balanced Coherence Web Java EE or Servlet Container Application State Clustered Oracle, WebLogic, WebSphere, JBoss, Tomcat In Memory Coherence Data Grid for Session State

23 With Data Source Integration (Cache Stores)

24 Clustered Second Level Cache (for Hibernate)

25 Remote Clients connected to Coherence Cluster

26 Interconnected WAN Clusters

27 Your Issues are Solved! Need to load large data (over 2GB) in startup Reliable clustering for Tomcat/JBoss/Resin Share OBJECTs among.net, Java, and C++ apps Improve performance Remove Singleton issue in the cluster, eg. Counter Remove the cache size limitation

28 Outline Oracle Data Grid Solution for Application Caching Use Cases Coherence Features Summary <Insert Picture Here>

29 The Local Scheme

30 The Replicated Cache Scheme

31 Reliable by Design Distributed Stability: Finite State Cluster (FSC) One known state of the Data Grid Collective fault diagnosis with deterministic fault recovery Guaranteed Data Availability: Dynamic Data Partitioning One view of all Data Data location and fault-tolerance built on FSC foundation Rebalancing and failover/failback are simple state transitions Simple Programming Model Programs execute against the FSC Complete isolation from individual machines Designed for Lights Out Management / Zero Admin (LOM/ZA)

32 Partitioned Topology : Data Access Oracle Coherence provides many Topologies for Data Management Local, Near, Replicated, Overview, Disk, Off- Heap, Extend (WAN), Extend (Clients) Partitioned Topology Data spread and backed up across Members Transparent to developer Members have access to all Data All Data locations are known no lookup & no registry!

33 Partitioned Topology : Data Update Partitioned Topology Synchronous Update Avoids potential Data Loss & Corruption Predictable Performance Backup Partitions are partitioned away from Primaries for resilience No engineering requirement to setup Primaries or Backups Automatically and Dynamically Managed

34 Partitioned Topology : Recovery Partitioned Topology Membership changes (new members added or members leaving) Other members, in parallel, recover / repartition No in-flight operations lost Some latencies (due to higher priority of recovery) Reliability, Availability, Scalability, Performance are the priority Degrade performance of some requests

35 The Near Cache Scheme

36 Parallel Query Execution

37 Application of Indexes and Filters

38 InvocableMap Interface e.g. Aggregation

39 Listening to events

40 Outline Oracle Data Grid Solution for Application Caching Use Cases Coherence Features Summary <Insert Picture Here>

41 Why Oracle Coherence? 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

42 Why Oracle Coherence? Reliable Scalable Universal Data Built for continuous operation Data Fault Tolerance Self-Diagnosis and Healing Once and Only Once Processing Dynamically Expandable No data loss at any volume No interruption of service Leverage Commodity Hardware Single view of data Single management view Simple programming model Any Application Any Data Source Data Caching Analytics Transaction Processing Event Processing Cost Effective

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