2. An Engineer’s Introduction to Oracle Coherence
Brian Oliver Senior Principal Solutions Architect | Oracle

3. Agenda What is Coherence? Demonstration Consensus How it works
Code Examples
Architectural Patterns
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4. What is Coherence? (c) Copyright 2007. Oracle Corporation

5. Oracle Coherence Development Toolkit Other Libraries for…
Pure Java Libraries
Pure .Net 1.1 and 2.0 (Client Libraries)
No Third-Party Dependencies
No Open Source Dependencies
Other Libraries for…
Database and File System Integration
Top Link and Hibernate
Http Session Management, Spring, …

6. Oracle Coherence Provides… Container-less Clustering of Java Processes
Data Structures to manage Data across a Cluster / Grid
Real-Time Event Observation – Listener Pattern
Materialized Views of Data
Parallel Queries and Aggregation – Object-based Queries
Parallel Data Processing
Parallel Grid Processing
RemoteException Free Distributed Computing
Clustered JMX
MAN + WAN Connectivity
Client + Data Grid Deployment Models

7. Oracle Coherence Some uses…
Application Clustering and Reliable Data Sharing
Caching state in the Application-tier
Relieve load on lower-tier systems
Databases, Mainframes, Web Servers, Web Services
Scaling out application state (in the application-tier)
In-Memory Http Session Management
Resilient Processing Engine
Temporary System of Record for Extreme Transaction Processing

8. Coherence Demonstration
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9. How Coherence Works (c) Copyright 2007. Oracle Corporation

10. Distributed Data Management (access)
The Partitioned Topology
(one of many)
In-Process Data Management
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11. Distributed Data Management (update)
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12. Distributed Data Management (failover)
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13. Distributed Data Management
Members have logical access to all Entries
At most 2 network operations for Access
At most 4 network operations for Update
Regardless of Cluster Size
Deterministic access and update behaviour (performance can be improved with local caching)
Predictable Scalability
Cache Capacity Increases with Cluster Size
Coherence Load-Balances Partitions across Cluster
Point-to-Point Communication (peer to peer)
No multicast required (sometimes not allowed)
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14. Data Distribution: Clients and Servers
“Clients” with storage disabled
“Servers” with storage enabled
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15. Near Caching (L1 + L2) Topology
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16. Observing Data Changes
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17. Parallel Queries
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18. Parallel Processing and Aggregation
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19. Data Source Integration (read-through)
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20. Data Source Integration (write-through)
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21. Data Source Integration (write-behind)
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22. Coherence Code Examples
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23. Clustering Java Processes
Cluster cluster = CacheFactory.ensureCluster();
Joins an existing cluster or forms a new cluster
Time “to join” configurable
cluster contains information about the Cluster
Cluster Name
Members
Locations
Processes
No “master” servers
No “server registries”
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24. Leaving a Cluster Leaves the current cluster
CacheFactory.shutdown();
Leaves the current cluster
shutdown blocks until “data” is safe
Failing to call shutdown results in Coherence having to detect process death/exit and recover information from another process.
Death detection and recovery is automatic
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25. Using a Cache get, put, size & remove
NamedCache nc = CacheFactory.getCache(“mine”);
Object previous = nc.put(“key”, “hello world”);
Object current = nc.get(“key”);
int size = nc.size();
Object value = nc.remove(“key”);
CacheFactory resolves cache names (ie: “mine”) to configured NamedCaches
NamedCache provides data topology agnostic access to information
NamedCache interfaces implement several interfaces;
java.util.Map, Jcache, ObservableMap*, ConcurrentMap*, QueryMap*, InvocableMap*
Coherence* Extensions
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26. Using a Cache keySet, entrySet, containsKey
NamedCache nc = CacheFactory.getCache(“mine”);
Set keys = nc.keySet();
Set entries = nc.entrySet();
boolean exists = nc.containsKey(“key”);
Using a NamedCache is like using a java.util.Map
What is the difference between a Map and a Cache data-structure?
Both use (key,value) pairs for entries
Map entries don’t expire
Cache entries may expire
Maps are typically limited by heap space
Caches are typically size limited (by number of entries or memory)
Map content is typically in-process (on heap)
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27. Observing Cache Changes ObservableMap
NamedCache nc = CacheFactory.getCache(“stocks”);
nc.addMapListener(new MapListener() {
public void onInsert(MapEvent mapEvent) { }
public void onUpdate(MapEvent mapEvent) {
public void onDelete(MapEvent mapEvent) {
});
Observe changes in real-time as they occur in a NamedCache
Options exist to optimize events by using Filters, (including pre and post condition checking) and reducing on-the-wire payload (Lite Events)
Several MapListeners are provided out-of-the-box.
Abstract, Multiplexing...
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28. Querying Caches QueryMap
NamedCache nc = CacheFactory.getCache(“people”);
Set keys = nc.keySet( new LikeFilter(“getLastName”, “%Stone%”));
Set entries = nc.entrySet( new EqualsFilter(“getAge”,
35));
Query NamedCache keys and entries across a cluster (Data Grid) in parallel* using Filters
Results may be ordered using natural ordering or custom comparators
Filters provide support almost all SQL constructs
Query using non-relational data representations and models
Create your own Filters
* Requires Enterprise Edition or above
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29. Continuous Observation Continuous Query Caches
NamedCache nc = CacheFactory.getCache(“stocks”);
NamedCache expensiveItems = new ContinuousQueryCache(nc, new GreaterThan(“getPrice”, 1000));
ContinuousQueryCache provides real-time and in-process copy of filtered cached data
Use standard or your own custom Filters to limit view
Access to “view”of cached information is instant
May use with MapListeners to support rendering real-time local views (aka: Think Client) of Data Grid information.
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30. Aggregating Information InvocableMap
NamedCache nc = CacheFactory.getCache(“stocks”);
Double total = (Double)nc.aggregate( AlwaysFilter.INSTANCE, new DoubleSum(“getQuantity”));
Set symbols = (Set)nc.aggregate( new EqualsFilter(“getOwner”, “Larry”), new DistinctValue(“getSymbol”));
Aggregate values in a NamedCache across a cluster (Data Grid) in parallel* using Filters
Aggregation constructs include; Distinct, Sum, Min, Max, Average, Having, Group By
Aggregate using non-relational data models
Create your own aggregators
* Requires Enterprise Edition or above
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31. Mutating Information InvocableMap
NamedCache nc = CacheFactory.getCache(“stocks”);
nc.invokeAll( new EqualsFilter(“getSymbol”, “ORCL”), new StockSplitProcessor()); ...
class StockSplitProcessor extends AbstractProcessor { Object process(Entry entry) { Stock stock = (Stock)entry.getValue(); stock.quantity *= 2; entry.setValue(stock); return null; } }
Invoke EntryProcessors on zero or more entries in a NamedCache across a cluster (Data Grid) in parallel* (using Filters) to perform operations
Execution occurs where the entries are managed in the cluster, not in the thread calling invoke
This permits Data + Processing Affinity
* Requires Enterprise Edition or above
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32. Oracle Coherence Architectural Patterns
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33. Single Application Process
Coherence as “Data Structure”. Single applications may use Coherence java.util.Map interface implementations (and extensions) for high-performance, highly configurable caching. Clustering is not required!
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34. Clustered Processes
Coherence ensures that there is a consistent view of the data in-memory to all processes.
This is sometimes referred to as a “single-system-image”
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35. Multi Platform Cluster
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36. Clustered Application Servers
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37. With Data Source Integration (Cache Stores)
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38. Clustered Second Level Cache (for Hibernate)
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39. Remote Clients connected to Coherence Cluster
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40. Interconnected WAN Clusters
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41. Getting Oracle Coherence
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42. Oracle Coherence Search: Download Support Read More
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Support
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Coherence
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43. Questions
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44. Appendix

45. The preceding is intended to outline our general product direction
The preceding is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.