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An Engineer’s Introduction to Oracle Coherence
Brian Oliver
Senior Principal Solutions Architect | Oracle
Agenda
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What is Coherence?
Demonstration
Consensus
How it works
Code Examples
Architectural Patterns
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What is Coherence?
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Oracle Coherence
• Development Toolkit
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Pure Java 1.4.2+ 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, …
Oracle Coherence
• Provides…
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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
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
Coherence Demonstration
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How Coherence Works
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Distributed Data Management (access)
The Partitioned
Topology
(one of many)
In-Process
Data
Management
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Distributed Data Management (update)
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Distributed Data Management (failover)
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Distributed Data Management
• Members have logical access to all Entries
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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
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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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Data Distribution: Clients and Servers
“Clients”
with storage
disabled
“Servers”
with storage
enabled
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Near Caching (L1 + L2) Topology
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Observing Data Changes
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Parallel Queries
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Parallel Processing and Aggregation
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Data Source Integration (read-through)
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Data Source Integration (write-through)
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Data Source Integration (write-behind)
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Coherence Code Examples
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Clustering Java Processes
• Joins an existing cluster
or forms a new cluster
• Time “to join” configurable
• cluster contains
information about the
Cluster
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Cluster Name
Members
Locations
Processes
• No “master” servers
• No “server registries”
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Cluster cluster = CacheFactory.ensureCluster();
Leaving a Cluster
• 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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CacheFactory.shutdown();
Using a Cache
get, put, size & remove
• CacheFactory
resolves cache names
(ie: “mine”) to configured
NamedCaches
NamedCache nc = CacheFactory.getCache(“mine”);
Object previous = nc.put(“key”, “hello world”);
Object current = nc.get(“key”);
• NamedCache provides
data topology agnostic
access to information
int size = nc.size();
Object value = nc.remove(“key”);
• NamedCache interfaces
implement several
interfaces;
• java.util.Map, Jcache,
ObservableMap*,
ConcurrentMap*,
QueryMap*,
InvocableMap*
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Coherence* Extensions
Using a Cache
keySet, entrySet, containsKey
• Using a NamedCache is
like using a java.util.Map
NamedCache nc = CacheFactory.getCache(“mine”);
Set keys = nc.keySet();
• 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 inprocess (on heap)
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Set entries = nc.entrySet();
boolean exists = nc.containsKey(“key”);
Observing Cache Changes
ObservableMap
• Observe changes in
real-time as they occur
in a NamedCache
NamedCache nc = CacheFactory.getCache(“stocks”);
nc.addMapListener(new MapListener() {
public void onInsert(MapEvent mapEvent) {
}
• Options exist to optimize
events by using Filters,
(including pre and post
condition checking) and
reducing on-the-wire
payload (Lite Events)
public void onUpdate(MapEvent mapEvent) {
}
public void onDelete(MapEvent mapEvent) {
}
});
• Several MapListeners
are provided out-of-thebox.
• Abstract, Multiplexing...
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Querying Caches
QueryMap
• Query NamedCache keys
and entries across a cluster
(Data Grid) in parallel*
using Filters
NamedCache nc = CacheFactory.getCache(“people”);
• Results may be ordered
using natural ordering or
custom comparators
Set entries = nc.entrySet(
new EqualsFilter(“getAge”,
• 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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Set keys = nc.keySet(
new LikeFilter(“getLastName”,
“%Stone%”));
35));
Continuous Observation
Continuous Query Caches
• ContinuousQueryCache
provides real-time and inprocess 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 realtime local views (aka: Think
Client) of Data Grid
information.
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NamedCache nc = CacheFactory.getCache(“stocks”);
NamedCache expensiveItems =
new ContinuousQueryCache(nc,
new GreaterThan(“getPrice”, 1000));
Aggregating Information
InvocableMap
• 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 nonrelational data models
• Create your own
aggregators
* Requires Enterprise Edition or above
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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”));
Mutating Information
InvocableMap
• Invoke EntryProcessors
on zero or more entries in a
NamedCache across a
cluster (Data Grid) in
parallel* (using Filters) to
perform operations
NamedCache nc = CacheFactory.getCache(“stocks”);
nc.invokeAll(
new EqualsFilter(“getSymbol”, “ORCL”),
new StockSplitProcessor());
...
• Execution occurs where the
entries are managed in the
cluster, not in the thread
calling invoke
class StockSplitProcessor extends
AbstractProcessor {
Object process(Entry entry) {
Stock stock = (Stock)entry.getValue();
stock.quantity *= 2;
entry.setValue(stock);
return null;
}
• This permits Data +
Processing Affinity
* Requires Enterprise Edition or above
}
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Oracle Coherence
Architectural Patterns
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Single Application Process
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Clustered Processes
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Multi Platform Cluster
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Clustered Application Servers
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With Data Source Integration
(Cache Stores)
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Clustered Second Level Cache
(for Hibernate)
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Remote Clients connected to
Coherence Cluster
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Interconnected WAN Clusters
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Getting
Oracle Coherence
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Oracle Coherence
• Search:
• http://search.oracle.com
Search For:
Coherence
• Download
• http://www.oracle.com/technology/products/coherence
• Support
• http://forums.tangosol.com
• http://wiki.tangosol.com
• Read More
• http://www.tangosol.com/
Questions
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Appendix
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.