Brian Oliver  Pimp My Data Grid
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Brian Oliver Pimp My Data Grid






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    Brian Oliver  Pimp My Data Grid Brian Oliver Pimp My Data Grid Presentation Transcript

    • <Insert Picture Here> Pimp My Data Grid Brian Oliver Senior Principal Solutions Architect ( Oracle Coherence | Oracle Fusion Middleware
    • Agenda • An Architectural Challenge • Enter the Data Grid • Architectural Patterns that Limit Application Scalability • Pimping Data Grid • Service Grids • Trading Exchange • Agile Groovy Grid • Unstoppable Spring (c) Copyright 2007. Oracle Corporation
    • An Architectural Challenge (c) Copyright 2008. Oracle Corporation
    • Scale this... • Domain: Retail Banking Infrastructure • Over 500 Banks • 100,000+ Teller Staff Desktops Applications • 10,000+ Cash Machines (ATMs) • 10,000,000’s of Internet Banking Transactions/day • Current Infrastructure • Java SE based (no J2EE – apart from Servlets) • Oracle RAC (not an issue – scaling across a WAN ☺ ) • Messaging (serious challenges) • Processing Business Tasks (challenges approaching) • 30,000,000+ Business Tasks a day – minimum. • must do 100,000,000 effortlessly per/day before going live (c) Copyright 2007. Oracle Corporation
    • Scale this... • Execution of Business Tasks • Account Balance, Credit/Debit, Funds Transfer, Statement Processing, Batch Processing, Payment Processing • Tasks arrive from a variety of clients (thin, rich, cross- platform, mainframes...) – variety of languages • Goal: • Tasks are executed by the “cloud” The • Don’t want to build own “cloud” software Cloud • Their knowledege: • Massive experience in scale-out. Could build it themselves, but budget (time/resources/money) will be saved by buying. (c) Copyright 2007. Oracle Corporation
    • Essentially want… interface Cloud { public <T> Future<T> execute(Task<T> task); } (c) Copyright 2007. Oracle Corporation
    • Constraints... • No Single Points of Failure • No Data or Task Loss • No Simple Points of Bottleneck • During failure • During server upgrade • No Service Registries • During scale out • No Masters + Workers • No Transactions (XA) • already got one that is partitioned into over 200 separate clusters • Support multiple versions • No Manual Partitioning • Predictable response times • Keep everything in Memory • Predictable scale out costs • Active + Active Sites • Manage via JMX, from any point • Across WAN in the “Cloud”. • Develop system on a note book • Pure Java Standard Edition • Scale to over 500 servers • Infrastructure add a maximum of • No reconfiguration outages 3ms latency to tasks. • No byte-code manipulation / • Integrate with existing proxies applications (Java 1.4.2+) (c) Copyright 2007. Oracle Corporation
    • Enter the Data Grid (c) Copyright 2008. Oracle Corporation
    • Enter the Data Grid • Data Grid ≈ Horizontally scalable in-memory data management • Goal • Eliminate data source contention by scaling out data management with commodity hardware • Underlying Philosophies… • Keep “data” in the “application-tier” (where it’s used) • “Disks are slow and databases are evil” • “Data Grids will solve your application scalability and performance problems” (c) Copyright 2008. Oracle Corporation
    • Essentially replace this… (c) Copyright 2008. Oracle Corporation
    • With this… Note to Marketing: Replace “Cloud” with Data Grid, Distributed Cache, Data Fabric, Information Fabric, Network Attached Storage, Java Space, Service Grid, Compute Grid, Object Grid, Shared Memory or other term ☺ (c) Copyright 2008. Oracle Corporation
    • Success! (c) Copyright 2008. Oracle Corporation
    • “What’s inside the Cloud?” (c) Copyright 2008. Oracle Corporation
    • Architectural Patterns that Limit Scalability (c) Copyright 2008. Oracle Corporation
    • Client + Server Pattern Server is point of contention Contention increases Server response time = increased Client latencies Client scale-out increases contention Not just Database related. Consider Store-and-Forward messaging systems and Spaces The server may be a “switch” Lesson: Avoid Single Points of Contention / Bottleneck (SPOB) (c) Copyright 2008. Oracle Corporation
    • Master + Worker Pattern Master is point of contention Contention increases Master response time = increases Worker (and requestor) Latencies Scale-out increases contention Lesson: Avoid Single Points of Contention / Bottleneck (SPOB) (c) Copyright 2008. Oracle Corporation
    • Master + Worker Pattern Reality... Typically Master + Worker actually is also Client + Server! Lesson: Avoid patterns with SPOB! (c) Copyright 2008. Oracle Corporation
    • Master + Worker Pattern Continued... Typically Master + Worker actually is also Client + Server! Often the driving requirement for “Data Grid” in a “Compute Grid” Lesson: Avoid patterns with multiple SPOB! (c) Copyright 2008. Oracle Corporation
    • Increasing Resilience Increasing resilience increases latency Synchronously maintained resilience typically doubles latencies Asynchronously maintained resilience will always introduce data integrity issues Lesson: Resilience rarely has zero- latency properties Lesson: Resilience ≠ Persistence (c) Copyright 2008. Oracle Corporation
    • Partition for Parallelism Partition Data onto separate Masters to provide load-balancing and increase parallelism Not easy, especially if access patterns are dynamic and load is uneven “Joins” become very difficult, but queries work in parallel Lesson: Hot spots are inevitable Lesson: Partition failure may corrupt state. RAID is a better partitioning strategy Lesson: Avoid “registries” to locate data/services (ie: Masters) (c) Copyright 2008. Oracle Corporation
    • Summary • Avoid Single Points of Contention • Avoid moving data • Avoid Single Points of Failure • Exploit Data Affinity • Avoid Client + Server • Data + Data and Data + Compute • Avoid Master + Worker • Deploy code everywhere • It’s smaller • Active + Active better than Active • Dynamic code deployment is + Passive dangerous in transactional systems • Ensure fair utilization of resources • Exploit Parallelism • Resilience increases latency • Partition Data for Parallelism • Resilience ≠ Persistence • Hot Spots are unavoidable • Resilience = Redundancy • Pipeline architectures help significantly • RAID is a good pattern • Use Caching to reduce I/O • XML is not great • Cache Coherency is not free • Interoperability is best achieved at • Cache Coherency is essential for the binary level (hardest, but best) Data Integrity • Understand the underlying implementation of solutions! (c) Copyright 2008. Oracle Corporation
    • Achieving Scalability and High Performance means... 1. Doing something completely different architecturally... including inside the “Cloud”. 2. Avoiding patterns that limit scalability or performance 3. Ensuring each architectural component (from external) providers avoids the “limiting” patterns = knowing the internals of the provided solutions (c) Copyright 2008. Oracle Corporation
    • What about Coherence? (c) Copyright 2007. Oracle Corporation
    • Oracle Coherence • Provides… • Container-less peer-to-peer Clustering of Java Processes • Data Structures to manage Data across a Cluster / Grid • Other Stuff… • Real-Time Event Observation – Listener Pattern • Materialized Views of Data – Continuous Queries • Parallel Queries and Aggregation – Object-based Queries • Parallel Data Processing • Parallel Grid Processing • RemoteException Free Distributed Computing • Clustered JMX • MAN + WAN Connectivity
    • Oracle Coherence • Development Toolkit • Pure Java 1.4.2+ Libraries • Pure .Net 1.1 and 2.0 (Client Libraries) • No Third-Party Dependencies • No Open Source Dependencies • No Masters • No Registries • Other Libraries for… • Database and File System Integration • Top Link and Hibernate • Http Session Management, Spring, …
    • Oracle Coherence • Some uses… • Caching state in the Application-tier • Relieve load on lower-tier systems • Databases, Mainframes, Web Servers, Web Services • Reliably managing Application state in the Application-tier • Scaling out application state (in the application-tier) • In-Memory Http Session Management • Reliable and Automatically Partitioned Grid Processing • Temporary System of Record for Extreme Transaction Processing
    • Coherence Demonstration (c) Copyright 2007. Oracle Corporation
    • Pimping Oracle Coherence... (c) Copyright 2008. Oracle Corporation
    • Strategy • Business Tasks are regular Java objects (pojo) • Place Business Tasks into Coherence • Coherence dynamically distributes Tasks across the Cluster • Tasks are resilient in the Cluster • May use “affinity” to ensure related Tasks processed together • Register Backing Map Listeners in the Cluster members to execute Tasks • Scaling out Coherence = Scaling out Task Processing (c) Copyright 2008. Oracle Corporation
    • Backing Map Listener is what? • Coherence distributes, manages and stores state (objects) using “Backing Maps” • Backing Map... • Class that is responsible for managing state. • Can be replaced to change how state is managed. • Eg: in heap, off heap, hibernate, BDB, toplink, wan, file system, memory mapped files across a wan. • May be replaced, composed and customized. • Backing Map Listener... • Class that receives data events from Backing Maps (c) Copyright 2008. Oracle Corporation
    • Strategy • As Tasks enter the “Cloud” Coherence notifies BML • Our BML implementation schedules, manages, executes the Tasks (using Java 5 Executor) • Cleans up Tasks when executed • Deals with Task recovery (idempotent with status) • BML is written in standard Java • No Transactions • Fault Tolerant • Distributed + Scalable + Event Driven Architecture (c) Copyright 2008. Oracle Corporation
    • Backing Map Listener Code public class ExampleBackingMapListener extends AbstractMultiplexingBackingMapListener { public ExampleBackingMapListener(BackingMapManagerContext context) { super(context); System.out.println(quot;Created our ExampleBackingMapListenerquot;); } @Override protected void onBackingMapEvent(MapEvent mapEvent, Cause cause) { System.out.println(quot;Cause:quot; + cause + quot;, Event:quot; + mapEvent); } } (c) Copyright 2008. Oracle Corporation
    • Backing Map Listener Configuration <distributed-scheme> <scheme-name>distributed-cache-scheme</scheme-name> <backing-map-scheme> <local-scheme> <listener> <class-scheme> <class-name>ExampleBackingMapListener</class-name> </class-scheme> </listener> </local-scheme> </backing-map-scheme> </distributed-scheme> (c) Copyright 2008. Oracle Corporation
    • Results • While submitting Tasks (regular system load) • Test 1: Scale from 1 server to over 400 • No reconfiguration • Test 2: Randomly kill servers • No reconfiguration • Test 3: Kill 1, 2, 4, 8, 16, 32, 64, 128, 160 servers at once • Any data loss? • Can it be identified? • Possible 1,200,000,000 Tasks execution capacity per/day • Client may reduce current hardware by 75% (c) Copyright 2008. Oracle Corporation
    • Trading Exchange (c) Copyright 2008. Oracle Corporation
    • Trading Exchange • Similar requirements and constraints • Order processing (Foreign Exchange) • 1,000’s per second (initial) per currency pair • No manual partitioning • No transactions • 10ms max latency for full accept, validate, match, respond • Achieved with Coherence using BMLs (< 3ms) • 14 weeks development (start to go live) (c) Copyright 2008. Oracle Corporation
    • Previous Next Generation Approach (failed to meet SLA’s) (c) Copyright 2008. Oracle Corporation
    • Current Solution (c) Copyright 2008. Oracle Corporation
    • Pimp my Data Grid (c) Copyright 2008. Oracle Corporation
    • Pimp it! • Most Data Grids, especially Coherence are a fully pluggable • Coherence provides peer-to-peer JVM clustering, resilient data management with events to support distributed EDA. • You’re generally only limited by your creativity (c) Copyright 2008. Oracle Corporation
    • Pimp it – with Groovy • Instead of building object-based queries, why not use Groovy expressions? • Eg: Filters, Queries and Agents are completely customizable in Coherence • new GroovyFilter(“entry.value in [...]”); • Serious projects are looking to use Groovy across the Data Grid to provide processing agility (c) Copyright 2008. Oracle Corporation
    • Pimp it – with Spring • Instead of Spring wrapping your Data Grid, embed Spring applications in a Data Grid to; • Virtualize them • Make then resilient to failure • Scale them out • Coherence is pure Java, so it plays well with Spring • Use Coherence as clustering infrastructure for Spring – make it unstoppable ☺ (c) Copyright 2008. Oracle Corporation
    • Getting Oracle Coherence (c) Copyright 2008. Oracle Corporation
    • Oracle Coherence • Search: Search For: • Coherence • Download •
    • Thanks (c) Copyright 2007. Oracle Corporation
    • Appendix (c) Copyright 2008. Oracle Corporation
    • The preceding is intended to outline general product use and 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. (c) Copyright 2008. Oracle Corporation