An Introduction to Apache Geode (incubating)

1. A NEW PLATFORM FOR A NEW ERA

2. 2© Copyright 2015 Pivotal. All rights reserved. 2© Copyright 2015 Pivotal. All rights reserved. Open Sourcing GemFire An Introduction to Apache Geode (incubating) The open source distributed in-memory database Anthony Baker abaker@apache.org @metatype Dan Smith dsmith@pivotal.io

5. 5© Copyright 2015 Pivotal. All rights reserved. 2004 2008 2014 • Massive increase in data volumes • Falling margins per transaction • Increasing cost of IT maintenance • Need for elasticity in systems • Financial Services Providers (every major Wall Street bank) • Department of Defense • Real Time response needs • Time to market constraints • Need for flexible data models across enterprise • Distributed development • Persistence + In-memory • Global data visibility needs • Fast Ingest needs for data • Need to allow devices to hook into enterprise data • Always on • Largest travel Portal • Airlines • Trade clearing • Online gambling • Largest Telcos • Large mfrers • Largest Payroll processor • Auto insurance giants • Largest rail systems on earth Hybrid Transactional /Analytics grids Our GemFire Journey Over The Years

6. 6© Copyright 2015 Pivotal. All rights reserved. Apps at Scale Have Unique Needs Pivotal GemFire is the distributed, in-memory database for apps that need: 1. Elastic scale-out performance 2. High performance database capabilities in distributed systems 3. Mission critical availability and resiliency 4. Flexibility for developers to create unique applications 5. Easy administration of distributed data grids

7. 7© Copyright 2015 Pivotal. All rights reserved. 1. Elastic Scale-Out Performance Nodes Ops / SecLinear scalability Elastic capacity +/- Latency-minimizing data distribution China Railway Corporation “The system is operating with solid performance and uptime. Now, we have a reliable, economically sound production system that supports record volumes and has room to grow” Dr. Jiansheng Zhu, Vice Director of China Academy of Railway Sciences • 4.5 million ticket purchases & 20 million users per day. • Spikes of 15,000 tickets sold per minute, 40,000 visits per second.

8. 8© Copyright 2015 Pivotal. All rights reserved. 2. High Performance Database Capabilities Performance- optimized persistence Configurable consistency Partitioned Replicated Disabled Distributed & continuous queries Distributed transactions, indexing, archival Newedge “Our global deployment of Pivotal GemFire’s distributed cache gives me a single version of the trade – resolving hard-to-test-for synchronization issues that exist within any globally distributed business application architecture” Michael Benillouche, Global Head of Data Management

9. 9© Copyright 2015 Pivotal. All rights reserved. 3. Mission Critical Availability and Resiliency Cluster to cluster WAN connectivity Cluster resilience & failover XX Gire “We can track and collect money at our 4,000+ kiosks and branches – even without a reliable Internet connection. GemFire provides the core data grid and a significant amount of related functionality to help us handle this unreliable network problem” Gustavo Valdez, Chief of Architecture and Development • 19 million payment transactions per month • 4000+ points of sale with intermittent Internet connectivity

10. 10© Copyright 2015 Pivotal. All rights reserved. 4. Flexibility for Developers to Create Unique Apps • Data Structures: – User-defined classes – Documents (JSON) • Native language support: – Java, C++, C# • API’s – Java: Hashmap – Memcache – Spring Data GemFire – C++ Serialization API’s • Embedded query authoring – Object Query Language (OQL) • Publish & subscribe framework for continous query & reliable asynchronous event queues • App-server embedded functionality: – Web app session state caching – L2 Hibernate

11. 11© Copyright 2015 Pivotal. All rights reserved. 5. Easy Administration of Distributed Data Grids • Auto tuning of distributed computing resources to optimize performance • Cluster monitoring dashboard – Cluster and node status & performance • Offline performance statistics analysis tool – View historical logs and events to diagnose performance and resource bottlenecks • Command-line tool for taking action on clusters and nodes

12. 12© Copyright 2015 Pivotal. All rights reserved. What makes it fast?  Design center is RAM not HDD  Really demanding customers  Avoid and minimize, particularly on the critical read/write paths: – Network hops, copying data, contention, distributed locking, disk seeks, garbage  Lots and lots of testing – Establish and monitor performance baselines – Distributed systems testing is difficult!

13. 13© Copyright 2015 Pivotal. All rights reserved. Horizontal Scaling for GemFire (Geode) Reads With Consistent Latency and CPU • Scaled from 256 clients and 2 servers to 1280 clients and 10 servers • Partitioned region with redundancy and 1K data size 0 2 4 6 8 10 12 14 16 18 0 1 2 3 4 5 6 2 4 6 8 10 Speedup Server Hosts speedup latency (ms) CPU %

15. 15© Copyright 2015 Pivotal. All rights reserved. Roadmap  HDFS persistence  Off-heap storage  Lucene indexes  Spark integration  Cloud Foundry service …and other ideas from the Geode community!

17. 17© Copyright 2015 Pivotal. All rights reserved. “Low touch” Usage Patterns Simple template for TCServer, TC, App servers Shared nothing persistence, Global session state HTTP Session management Set Cache in hibernate.cfg.xml Support for query and entity caching Hibernate L2 Cache plugin Servers understand the memcached wire protocol Use any memcached client Memcached protocol <bean id="cacheManager" class="org.springframework.data.gemfire.support.GemfireCacheManager"Spring Cache Abstraction

18. 18© Copyright 2015 Pivotal. All rights reserved. Application Patterns  Caching for speed and scale – Read-through, Write-through, Write-behind  Geode as the OLTP system of record – Data in-memory for low latency, on disk for durability  Parallel compute engine  Real-time analytics

19. 19© Copyright 2015 Pivotal. All rights reserved. Development Patterns  Configure the cluster programmatically or declaratively using cache.xml, gfsh, or SpringDataGemFire <cache> <region name="turbineSensorData" refid="PARTITION_PERSISTENT"> <partition-attributes redundant-copies="1" total-num-buckets="43"/> </region> </cache>

20. 20© Copyright 2015 Pivotal. All rights reserved. Development Patterns  Write key-value data into a Region using { create | put | putAll | remove } – The value can be flat data, nested objects, JSON, … Region sensorData = cache.getRegion("TurbineSensorData"); SensorKey key = new SensorKey(31415926, "2013-05-19T19:22Z"); // turbineId, timestamp sensorData.put(key, new TurbineReading() .setAmbientTemp(75) .setOperatingTemp(80) .setWindDirection(0) .setWindSpeed(30) .setPowerOutput(5000) .setRPM(5));

21. 21© Copyright 2015 Pivotal. All rights reserved. Development Patterns  Read values from a Region by key using { get | getAll } TurbineReading data = sensorData.get(new SensorKey(31415926,"2013-05- 19T19:22Z"));  Query values using OQL // finds all sensor readings for the given turbine SELECT * from /TurbineSensorData.entrySet WHERE key.turbineId = 31415926 // finds all sensor readings where the operating temp exceeds a threshold SELECT * from /TurbineSensorData.entrySet WHERE value.operatingTemp > 120  Apply indexes to optimize queries

22. 22© Copyright 2015 Pivotal. All rights reserved. Development Patterns  Execute functions to operate on local data in parallel  Respond to updates using CacheListeners and Events  Automatic redundancy, partitioning, distribution, consistency, network partition detection & recovery, load balancing, …

24. 24© Copyright 2015 Pivotal. All rights reserved. Why OSS? Why Now? Why Apache?  Open Source Software is fundamentally changing buying patterns – Developers have to endorse product selection (No longer CIO handshake) – Community endorsement is key to product visibility – Open source credentials attract the best developers – Vendor credibility directly tied to street credibility of product  Align with the tides of history – Customers increasingly asking to participate in product development – Allow product development to happen with full transparency  Apache is where you go to build Open Source street cred – Transparent, meritocracy which puts developers in charge – Roman keeps shouting “Apache!” every few hours

25. 25© Copyright 2015 Pivotal. All rights reserved. Geode Will Be A Significant Apache Project  Over a 1000 person years invested into cutting edge R&D  Thousands of production customers in very demanding verticals  Cutting edge use cases that have shaped product thinking  Tens of thousands of distributed , scaled up tests that can randomize every aspect of the product  A core technology team that has stayed together since founding  Performance differentiators that are baked into every aspect of the product

26. 26© Copyright 2015 Pivotal. All rights reserved. Geode or GemFire?  Geode is a project, GemFire is a product  We donated everything but the kitchen sink*  More code drops imminent; going forward all development happens OSS-style (“The Apache Way”) * Multi-site WAN replication, continuous queries, and native (C/C++) client driver

34. 34© Copyright 2015 Pivotal. All rights reserved. “User” Use Case – Save Objects public interface PersonRepository extends CrudRepository<Person, String> { } @Autowired PersonRepository people; public static void main(String[] args) { people.save(new Person(name)); posts.save(new Post(new PostId(name, date), text)); } Nested Objects, Compound Keys

35. 35© Copyright 2015 Pivotal. All rights reserved. “User” Use Case – Save Objects public interface PersonRepository extends CrudRepository<Person, String> { } @Autowired PersonRepository people; public static void main(String[] args) { people.save(new Person(name)); posts.save(new Post(new PostId(name, date), text)); } Automatically Serialized With PDX

36. 36© Copyright 2015 Pivotal. All rights reserved. Configuration <gfe:partitioned-region id="people" copies="1"/> <bean id="pdxSerializer” class="com.gemstone.gemfire.pdx.ReflectionBasedAutoSerializer"> <constructor-arg value="io.pivotal.happysocial.model.*"/> </bean> <gfe:cache pdx-serializer-ref="pdxSerializer"/>

38. 38© Copyright 2015 Pivotal. All rights reserved. First try – Just use a Query public interface PostRepository extends GemfireRepository<Post, PostId> { @Query("select * from /posts where id.person=$1") public Collection<Post> findPosts(String personName); } Collection<Post> posts = postRepository.findPosts(personName); String sentiment = sentimentAnalyzer.analyze(posts);

39. 39© Copyright 2015 Pivotal. All rights reserved. First try – Just use a Query public interface PostRepository extends GemfireRepository<Post, PostId> { @Query("select * from /posts where id.person=$1") public Collection<Post> findPosts(String personName); } Collection<Post> posts = postRepository.findPosts(personName); String sentiment = sentimentAnalyzer.analyze(posts); Query Nested Objects

41. 41© Copyright 2015 Pivotal. All rights reserved. Still could be more efficient Client Server 1 Server 2 Server 3 Joe: LOL!! Joe: LOL!! EJ: arrg Maya: Hii Jess: sup Jess: ok Hitting multiple Nodes Bringing too much Data to the client

42. 42© Copyright 2015 Pivotal. All rights reserved. Colocate the data Client Server 1 Server 2 Server 3 Joe: LOL!! Joe: LOL!! EJ: arrgMaya: Hii Jess: sup Jess: ok <gfe:partitioned-region id="posts" copies="1" colocated-with="people”> <gfe:partition-resolver ref="partitionResolver"/> </gfe:partitioned-region>

43. 43© Copyright 2015 Pivotal. All rights reserved. Send behavior to data Client Server 1 Server 2 Server 3 Joe: LOL!! Joe: LOL!! EJ: arrgMaya: Hii Jess: sup Jess: ok Execution function getSentiment On Joe, Jess Execute on Joe Execute on Jess

44. 44© Copyright 2015 Pivotal. All rights reserved. Sample Function – Client Side @Component @OnRegion(region = "posts") public interface FunctionClient { public List<SentimentResult> getSentiment(@Filter Set<String> people); }

45. 45© Copyright 2015 Pivotal. All rights reserved. Sample Function – Server Side @GemfireFunction(HA=true) public SentimentResult getSentiment(Region<PostId, Post> localPosts, @Filter Set<String> personNames) throws Exception { String personName = personNames.iterator().next(); Collection<Post> posts = localPosts.query("id.person='" personName + "'"); String sentiment = sentimentAnalyzer.analyze(posts); return new SentimentResult(sentiment, personName); }

50. 50© Copyright 2015 Pivotal. All rights reserved. Persistence – Design Goals  High availability  Low latency, high throughput  Durability  Minimal impact to in memory speed – Minimize IO operations – Minimize locking – Minimize deserialization

51. 51© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 3Server 2 Bucket 3 *primary* Bucket 1 secondary Bucket 3 secondary Bucket 2 *primary* Server 1 Bucket 2 secondary Bucket 1 *primary*

52. 52© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 1 Server 3Server 2 Bucket 2 secondary Bucket 1 *primary* Bucket 3 *primary* Bucket 1 secondary Bucket 3 secondary Bucket 2 *primary* What if a server crashes?

53. 53© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 1 Server 3Server 2 Bucket 2 secondary Bucket 1 *primary* Bucket 3 *primary* Bucket 1 *primary* Bucket 3 secondary Bucket 2 *primary* Bucket 2 secondary Primary Failover Restore Redundancy Bucket 1 secondary

54. 54© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 1 Server 3Server 2 Bucket 2 secondary Bucket 1 *primary* Bucket 3 *primary* Bucket 1 *primary* Bucket 3 secondary Bucket 2 *primary* Bucket 2 secondary Bucket 1 secondary What if they all crash?

55. 55© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 3Server 2 Bucket 3 *primary* Bucket 1 *primary* Bucket 3 secondary Bucket 2 *primary* Bucket 2 secondary Bucket 1 secondary Server 1 Bucket 2 secondary Bucket 1 *primary* Compare Persistent View On Restart

56. 56© Copyright 2015 Pivotal. All rights reserved. Persistence – Shared Nothing Server 1 Bucket 2 secondary Bucket 1 *primary* Server 3Server 2 Bucket 3 *primary* Bucket 1 secondary Bucket 3 secondary Bucket 2 *primary* Bucket 2 secondary Bucket 1 secondary Recover Latest Data & Rebalance

57. 57© Copyright 2015 Pivotal. All rights reserved. Modify k1->v5 Create k6->v6 Create k1->v1 Create k2->v2 Modify k1->v3 Create k4->v4 Modify k1->v5 Create k6->v6 Member 1 Persistence - Operation Logs Put k6->v6 Oplog2.crf Oplog1.crf Append to operation log

58. 58© Copyright 2015 Pivotal. All rights reserved. Modify k1->v5 Create k6->v6 Create k1->v1 Modify k1->v3 Member 1 Persistence - Compaction Oplog2.crf Oplog1.crf Create k2->v2 Create k4->v4 Copy live data forward

64. 64© Copyright 2015 Pivotal. All rights reserved. Efficient for queries SELECT p.name from /Person p WHERE p.pet.type = “dino” { id : 1, name : “Fred”, age : 42, pet : { name : “Barney”, type : “dino” } } single field deserialization

65. 65© Copyright 2015 Pivotal. All rights reserved. Easy to use  Access from Java, C#, C++, JSON  Domain objects not required  Automatic type definition – No IDL compiler or schema required – No hand-coded read/write methods

69. 69© Copyright 2015 Pivotal. All rights reserved. Distributed type registry Member A Member B Distributed Type Definitions Person p1 = … region.put(“Fred”, p1); automatic definition replicate serialized data containing typeId

70. 70© Copyright 2015 Pivotal. All rights reserved. Schema evolution PDX provides forwards and backwards compatibility, no code required Member A Member B Distributed Type Definitions v 1 v 2 Application #2 Application #1 v2 objects preserve data from missing fields v1 objects use default values to fill in new fields

71. 71© Copyright 2015 Pivotal. All rights reserved. Questions?  http://geode.incubator.apache.org  dev@geode.incubator.apache.org  user@geode.incubator.apache.org  http://github.com/apache/incubator-geode

74. 74© Copyright 2015 Pivotal. All rights reserved. Asynchronous Events – Design Goals  High availability  Low latency, high throughput  Deliver events to a receiver without impacting the write path

77. 77© Copyright 2015 Pivotal. All rights reserved. put Secondary Queue (Partition 1) LOL!! sup sup LOL!! Primary Queue (Partition 2) Parallel Queues Primary Queue (Partition 1) LOL!! sup sup LOL!! Word Word

78. 78© Copyright 2015 Pivotal. All rights reserved. LOL!! sup sup LOL!! Parallel Queues LOL!! sup sup LOL!! Word Word AsyncEventListener { processBatch() } AsyncEventListener { processBatch() } Parallel Dispatch

81. 81© Copyright 2015 Pivotal. All rights reserved. Client driver  Intelligent – understands data distribution for single hop network access  Caching – can be configured to locally cache data for even faster access  Events – registerInterest() in keys to receive push notifications

82. 82© Copyright 2015 Pivotal. All rights reserved. Client subscriptions  Useful for refreshing client cache, pushing events (“topics”) to multiple clients  Highly available & scalable via in-memory replicated queues  Events are ordered, at-least-once delivery  Durable subscriptions, conflation optional

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