Pimp My Data Grid. Brian Oliver Senior Principal Solutions Architect <Insert Picture Here>

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2 <Insert Picture Here> Pimp My Data Grid Brian Oliver Senior Principal Solutions Architect Oracle Coherence Oracle Fusion Middleware

3 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 Oracle Corporation

4 An Architectural Challenge

5 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 Oracle Corporation

6 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, crossplatform, mainframes...) variety of languages Goal: Tasks are executed by the cloud Don t want to build own cloud software The Cloud Their knowledege: Massive experience in scale-out. Could build it themselves, but budget (time/resources/money) will be saved by buying. (c) Copyright Oracle Corporation

7 Essentially want interface Cloud { public <T> Future<T> execute(task<t> task); } (c) Copyright Oracle Corporation

8 Constraints... No Single Points of Failure No Simple Points of Bottleneck No Service Registries No Masters + Workers already got one that is partitioned into over 200 separate clusters No Manual Partitioning Keep everything in Memory Active + Active Sites Across WAN Develop system on a note book Scale to over 500 servers No reconfiguration outages No byte-code manipulation / proxies No Data or Task Loss During failure During server upgrade During scale out No Transactions (XA) Support multiple versions Predictable response times Predictable scale out costs Manage via JMX, from any point in the Cloud. Pure Java Standard Edition Infrastructure add a maximum of 3ms latency to tasks. Integrate with existing applications (Java ) (c) Copyright Oracle Corporation

9 Enter the Data Grid

10 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

11 Essentially replace this

12 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

13 Success!

14 What s inside the Cloud?

15 Architectural Patterns that Limit Scalability

16 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)

17 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)

18 Master + Worker Pattern Reality... Typically Master + Worker actually is also Client + Server! Lesson: Avoid patterns with SPOB!

19 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!

20 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 zerolatency properties Lesson: Resilience Persistence

21 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)

22 Summary Avoid Single Points of Contention Avoid Single Points of Failure Avoid Client + Server Avoid Master + Worker Active + Active better than Active + Passive Ensure fair utilization of resources Resilience increases latency Resilience Persistence Resilience = Redundancy RAID is a good pattern XML is not great Interoperability is best achieved at the binary level (hardest, but best) Avoid moving data Exploit Data Affinity Data + Data and Data + Compute Deploy code everywhere It s smaller Dynamic code deployment is dangerous in transactional systems Exploit Parallelism Partition Data for Parallelism Hot Spots are unavoidable Pipeline architectures help significantly Use Caching to reduce I/O Cache Coherency is not free Cache Coherency is essential for Data Integrity Understand the underlying implementation of solutions!

23 Achieving Scalability and High Performance means 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

24 What about Coherence? (c) Copyright Oracle Corporation

25 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

26 Oracle Coherence Development Toolkit Pure Java 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,

27 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

28 Coherence Demonstration (c) Copyright Oracle Corporation

29 Pimping Oracle Coherence...

30 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

31 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

32 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

33 Backing Map Listener Code public class ExampleBackingMapListener extends AbstractMultiplexingBackingMapListener { public ExampleBackingMapListener(BackingMapManagerContext context) { super(context); } System.out.println("Created our protected void onbackingmapevent(mapevent mapevent, Cause cause) { System.out.println("Cause:" + cause + ", Event:" + mapevent); } }

34 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>

35 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%

36 Trading Exchange

37 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)

38 Previous Next Generation Approach (failed to meet SLA s)

39 Current Solution

40 Pimp my Data Grid

41 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

42 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

43 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

44 Getting Oracle Coherence

45 Oracle Coherence Search: Search For: Coherence Download

46 Thanks (c) Copyright Oracle Corporation

47 Appendix

48 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.

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