an engineer's intro to oracle coherence

<Insert Picture Here>

Oracle Coherence Integration with WebLogic Server & WebLogic PortalAn engineer’s introduction to in-memory data grid development

PresenterTitle

Agenda

• What Is Coherence– Distributed Data Grid

• How Does It Work?• Use Cases

– Customer Examples

• Q&A

Oracle Coherence

• Development Toolkit– Pure Java 1.5+ 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…– 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

<Insert Picture Here>

Distributed Data Grid

<Insert Picture Here>

“A Data Grid is a system composed of multiple servers that work together to manage information

and related operations - such as computations - in a distributed environment.”

Cameron Purdy

VP of Development, Oracle

What is a Data Grid?

• What• In-Memory• Objects• Shared

• Benefits• Low response time• High throughput• Predictable scalability• Continuous availability• Information reliability

Scalability Chasm

Application Servers

Web

Servers

Data Demand

Ever Expanding Universe of Users

Data Supply

• Data Demand outpacing Data Supply

• Rate of growth outpacing ability to cost effectively scale applications

Performance Problem

A Performance Bottleneck

Application Database Tables

Object

JavaSQL server

Relational

• Volume

• Complexity

• Frequency of Data Access

Oracle Coherence as Data Broker

Application Servers

Web

Servers

Data Demand

Ever Expanding Universe of Users

Data Supply

• Oracle Coherence brokers Data Supply with Data Demand

• Scale out Data Grid in middle tier using commodity hardware

Data Sources

Objects

<Insert Picture Here>

Coherence Clustering

Coherence Clustering:Tangosol Clustered Messaging Protocol (TCMP)

• Completely asynchronous yet ordered messaging built on UDP multicast/unicast

• Truly Peer-to-Peer: equal responsibility for both producing and consuming the services of the cluster

• Self Healing - Quorum based diagnostics

• Linearly scalable mesh architecture.

• TCP-like features• Messaging throughput scales

to the network infrastructure.

Coherence Clustering:The Cluster Service

• Transparent, dynamic and automatic cluster membership management

• Clustered Consensus: All members in the cluster understand the topology of the entire grid at all times.

• Crowdsourced member health diagnostics

CoherenceDistributed data management for applications

• Development Library– Pure Java 1.4.2+– Pure .Net 1.1 and 2.0 (client) – C++ client (3.4)– No Third-Party Dependencies– No Open Source Dependencies– Proprietary Network Stack (Peer-To-Peer model)

• Other Libraries Support…– Database and File System Integration– TopLink and Hibernate– Http Session Management– WebLogic Portal Caches– Spring, Groovy

The Portable Object FormatAdvanced Serialization

• Simple Serialization Comparison– In XML• <date format=“java.util.Date”>2008-07-03</date>• 47 characters (possibly 94 bytes depending on encoding)

– In Java (as a raw long)• 64 bits = 8 bytes

– In Java (java.util.Date using ObjectOutputStream)• 46 bytes

– In ExternalizableLite (as a raw long)• 8 bytes

– In POF• 4F 58 1F 70 6C = 5 bytes

(c) Copyright 2008. Oracle Corporation

©2011 Oracle Corporation 16

Coherence Cache Types / Strategies

Replicated Cache

Optimistic Cache

Partitioned Cache

Near Cache backed by partitioned cache

LocalCache not clustered

Topology Replicated Replicated Partitioned Cache Local Caches + Partitioned Cache

Local Cache

Read Performance Instant Instant Locally cached: instant --Remote: network speed

Locally cached: instant --Remote: network speed

Instant

Fault Tolerance Extremely High Extremely High Configurable Zero to Extremely High

Configurable 4 Zero to Extremely High

Zero

Write Performance Fast Fast Extremely fast Extremely fast Instant

Memory Usage (Per JVM)

DataSize DataSize DataSize/JVMs x Redundancy

LocalCache + [DataSize / JVMs]

DataSize

Coherency fully coherent fully coherent fully coherent fully coherent n/a

Memory Usage (Total)

JVMs x DataSize JVMs x DataSize Redundancy x DataSize

[Redundancy x DataSize] + [JVMs x LocalCache]

n/a

Locking fully transactional none fully transactional fully transactional fully transactional

Typical Uses Metadata n/a (see Near Cache)

Read-write caches Read-heavy caches w/ access affinity

Local data

<Insert Picture Here>

Use Cases

Data Grid Uses

CachingApplications request data from the Data Grid rather than

backend data sources

AnalyticsApplications ask the Data Grid questions from simple queries to

advanced scenario modeling

TransactionsData Grid acts as a transactional System of Record, hosting

data and business logic

EventsAutomated processing based on event

<Insert Picture Here>

Code Examples

Cluster cluster = CacheFactory.ensureCluster();Cluster cluster = CacheFactory.ensureCluster();

Clustering Java Processes

• 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”

(c) Copyright 2007. Oracle Corporation

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

(c) Copyright 2007. Oracle Corporation

CacheFactory.shutdown();CacheFactory.shutdown();

Using a Cacheget, put, size & remove

• 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*

(c) Copyright 2007. Oracle Corporation

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”);

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”);

Coherence* Extensions

Using a CachekeySet, entrySet, containsKey

• 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)

(c) Copyright 2007. Oracle Corporation

NamedCache nc = CacheFactory.getCache(“mine”);

Set keys = nc.keySet();

Set entries = nc.entrySet();

boolean exists = nc.containsKey(“key”);

NamedCache nc = CacheFactory.getCache(“mine”);

Set keys = nc.keySet();

Set entries = nc.entrySet();

boolean exists = nc.containsKey(“key”);

Observing Cache ChangesObservableMap

• 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...

(c) Copyright 2007. Oracle Corporation

NamedCache nc = CacheFactory.getCache(“stocks”);

nc.addMapListener(new MapListener() {

public void onInsert(MapEvent mapEvent) {

}

public void onUpdate(MapEvent mapEvent) {

}

public void onDelete(MapEvent mapEvent) {

}

});

NamedCache nc = CacheFactory.getCache(“stocks”);

nc.addMapListener(new MapListener() {

public void onInsert(MapEvent mapEvent) {

}

public void onUpdate(MapEvent mapEvent) {

}

public void onDelete(MapEvent mapEvent) {

}

});

Querying CachesQueryMap

• 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

(c) Copyright 2007. Oracle Corporation

NamedCache nc = CacheFactory.getCache(“people”);

Set keys = nc.keySet( new LikeFilter(“getLastName”, “%Stone%”));

Set entries = nc.entrySet( new EqualsFilter(“getAge”,

35));

NamedCache nc = CacheFactory.getCache(“people”);

Set keys = nc.keySet( new LikeFilter(“getLastName”, “%Stone%”));

Set entries = nc.entrySet( new EqualsFilter(“getAge”,

35));

Continuous ObservationContinuous Query Caches

• 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.

(c) Copyright 2007. Oracle Corporation

NamedCache nc = CacheFactory.getCache(“stocks”);

NamedCache expensiveItems = new ContinuousQueryCache(nc, new GreaterThan(“getPrice”, 1000));

NamedCache nc = CacheFactory.getCache(“stocks”);

NamedCache expensiveItems = new ContinuousQueryCache(nc, new GreaterThan(“getPrice”, 1000));

Aggregating InformationInvocableMap

• 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

(c) Copyright 2007. Oracle Corporation

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”));

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 InformationInvocableMap

• 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

(c) Copyright 2007. Oracle Corporation

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; }

}

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; }

}

<Insert Picture Here>

Customer Examples

30

Amir Razmara Director, The Gap

Problem: Universal user profile, preferences, shopping cart and single sign-on across 4 brands

Shared sessions across all 4 brands leading to a need for a global session (each brand has its own cluster of servers)

Possible Solutions:

State repository with DB

Data Grid backed session management with Coherence*Web

Coherence*Web Solution:Create a “Global Cache Cloud” to maintain a brand agnostic session caching layer, enabling the global session

Any server from any brand is able to obtain a session from the global cache cloud, enabling SSO and shared bag

Session durability, immune from crashes in the application tier

Sessions maintained during the nightly cell switch for publishing content

*GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction

The Universal Experience: Sister Tabs

*GAP - OOW2008 S299392 - Beyond Performance: Pushing Transaction…

Customer Examples

• Telecommunications• Major Communications provider

• Home Subscriber Server (HSS) part of IMS platform• “Enterprise Data Grid” – Unified Data Access layer across

the enterprise• Active-Active data center replication across WAN

• Major Communications Provider• “Click-to-Chat” application – web chat between customers

and CSRs

• Major Financial Services Provider• User Session data in Coherence, access from Java and C++• User Session data replicated across WAN to alternate data

center• Mainframe MIPS cost mitigation• Mid-tier caching to aid migration off MS SQL Server

Oracle Coherence Advantage

• Protect the Database Investment– Ensure DB does what it does best, limit cost of rearchitecture

• Scale as you Grow– Cost effective: Start small with 3-5 servers, scale to hundreds

of servers as business grows

• Enables business continuity – Providing continuous data availability

AQ&Q U E S T I O N SQ U E S T I O N S

A N S W E R SA N S W E R S