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Apache Geode Meetup, Cork, Ireland at CIT
This document provides an introduction to Apache Geode (incubating), including: - A brief history of Geode and why it was developed - An overview of key Geode concepts such as regions, caching, and functions - Examples of interesting large-scale use cases from companies like Indian Railways - A demonstration of using Geode with Apache Spark and Spring XD for a stock prediction application - Information on how to get involved with the Geode open source project community
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Apache Geode Meetup, Cork, Ireland at CIT
- 1. Introduction to ApacheGeode (incubating) Cork, September 2015 Anthony Baker (@metatype) William Markito (@william_markito)
- 2. • Introduction toGeode • Geode concepts and usage • The Geode open source project • StockPrediction demo Agenda
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- 4. 4 "…an in-memory, distributeddatabase with strong consistency built to support low latency transactional applications at extreme scale.”
- 5. History 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 • 1000+ customers in production • Cutting edge use cases
- 6. Interesting use cases ChinaRailway Corporation 5,700 train stations 4.5 million tickets per day 20 million daily users 1.4 billion page views per day 40,000 visits per second *http://pivotal.io/big-data/pivotal-gemfire Indian Railways 7,000 stations 72,000 miles of track 23 million passengers daily 120,000 concurrent users 10,000 transactions per minute
- 7. Interesting use cases IndianRailwaysChina Railway Corporation World: ~7,349,000,000 ~36% of the world population Population: 1,251,695,6161,401,586,609
- 8. 8 Application Patterns • Cachingfor speed and scale ➡ Read-Through, Write-Through, Write-Behind • As the OLTP system of record ➡ Data in-memory for low-latency, on disk for durability • Parallel compute engine • Real-time analytics
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- 10. • Cache • Region •Member • Client Cache • Functions • Listeners Geode Concepts and Usage
- 11. • Cache • In-memorystorage and management for your data • Configurable through XML, Spring, Java API or CLI • Collection of Region Region Region Region Cache JVM Concepts
- 12. Concepts • Region • Distributedjava.util.Map on steroids (Key/Value) • Consistent API regardless of where or how data is stored • Observable (reactive) • Highly available, redundant on cache Member (s). • Querying Region Cache java.util.Map JVM Key Value K01 May K02 Tim
- 13. Concepts • Region • Local,Replicated or Partitioned • In-memory or persistent • Redundant • LRU • Overflow Region Cache java.util.Map JVM Key Value K01 May K02 Tim Region Cache java.util.Map JVM Key Value K01 May K02 Tim LOCAL LOCAL_HEAP_LRU LOCAL_OVERFLOW LOCAL_PERSISTENT LOCAL_PERSISTENT_OVERFLOW PARTITION PARTITION_HEAP_LRU PARTITION_OVERFLOW PARTITION_PERSISTENT PARTITION_PERSISTENT_OVERFLOW PARTITION_PROXY PARTITION_PROXY_REDUNDANT PARTITION_REDUNDANT PARTITION_REDUNDANT_HEAP_LRU PARTITION_REDUNDANT_OVERFLOW PARTITION_REDUNDANT_PERSISTENT PARTITION_REDUNDANT_PERSISTENT_OVERFLOW REPLICATE REPLICATE_HEAP_LRU REPLICATE_OVERFLOW REPLICATE_PERSISTENT REPLICATE_PERSISTENT_OVERFLOW REPLICATE_PROXY
- 14. • Persistent Regions •Durability • WAL for efficient writing • Consistent recovery • Compaction Concepts Modify k1->v5 Create k6->v6 Create k2->v2 Create k4->v4 Oplog2.crf Member 1 Modify k4->v7Oplog3.crf Put k4->v7 Region Cache java.util.Map JVM Key Value K01 May K02 Tim Region Cache java.util.Map JVM Key Value K01 May K02 Tim Server 1 Server N
- 15. • Member • Aprocess that has a connection to the system • A process that has created a cache • Embeddable within your application Concepts Client Locator Server
- 16. Concepts • Client cache •A process connected to the Geode server(s) • Can have a local copy of the data • Can be notified about events on the servers Application GemFire Server Region Region RegionClient Cache
- 17. Concepts • Functions • Usedfor distributed concurrent processing (Map/Reduce, stored procedure) • Highly available • Data oriented • Member oriented Submit (f1) f1 , f2 , … fn Execute Functions
- 18. Concepts • Functions Server Server FunctionService.onRegion.withFilter.execute ResultCollector.getResult ServerDistributed System execute Server Server 6 1 result execute execute result result 2 5 3 4 3 4 Server Partitioned Region Data Store - X Partitioned Region Data Store - Y Partitioned Region Data Store - Z Partitioned Region Data Accessor Partitioned Region Data Accessor filter = Keys X, Y Client Region
- 19. Concepts • Listeners • CacheWriter/ CacheListener • AsyncEventListener (queue / batch) • Parallel or Serial • Conflation
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- 21. Why Apache Geode? ©Copyright 2014 Pivotal. All rights reserved. Pivotal GemFire High Availability and Fault Tolerance in 6 acts Failing data copies are replaced transparently Data is replicated to other clusters and sites (WAN) Network segmentations are identified and fixed automatically Client and cluster disconnections are handled gracefully Data is persisted on local disk for ultimate durability “split brain” Failed function executions are restarted automatically restart
- 22. • Minimize copying •Minimize contention points • Flexible consistency model • Partitioning and parallelism • Avoid disk seeks What makes it fast?
- 23. Benchmarks and Testing 0 2 4 6 8 10 12 14 16 18 0 1 2 3 4 5 6 24 6 8 10 Speedup Server(Hosts speedup latency4(ms) CPU4%
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- 25. Why Open Source?Why ASF? • Open source is fundamentally changing software buying patterns • Customers get transparency and co-development of features • It’s the community that matters • ASF provides a framework for open source
- 26. Geode Will Bea Significant Apache Project • 1M+ LOC, 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 • A core technology team that has stayed together since founding • Performance differentiators that are baked into every aspect of the product
- 27. Geode versus GemFire •Geode is a project supported by the OSS community • GemFire is product from Pivotal, based on Geode source • We donated everything but the kitchen sink* • Development process follows “The Apache Way” * Multi-site WAN replication, continuous queries, and native (C/C++/.NET) client
- 28. "Talk is cheap,show me the code" • Clone & Build git clone https://github.com/apache/incubator-geode cd incubator-geode ./gradlew build -Dskip.tests=true • Start a server cd gemfire-assembly/build/install/apache-geode ./bin/gfsh gfsh> start locator --name=locator gfsh> start server --name=server gfsh> create region --name=myRegion --type=REPLICATE
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- 30. Stock Predictions with Apache Geode, Spark, and SpringXD
- 31. • RDD • Dataframe •Driver • Worker Quick intro to Apache Spark "An RDD in Spark is simply an immutable distributed collection of objects. Each RDD is split into multiple partitions, which may be computed on different nodes of the cluster. RDDs can contain any type of Python, Java, or Scala objects, including user-defined classes."
- 32. • RDD • Dataframe •Driver • Worker Quick intro to Apache Spark “A dataframe is a distributed collection of rows organized into named columns. An abstraction for selecting, filtering and plotting structured data (pandas), previously known as SchemaRDD."
- 33. • RDD • Dataframe •Driver • Worker Quick intro to Apache Spark
- 34. Live Data Apache Geode/ GemFire 1- Live data is ingested into the grid 2 - Trained ML model compares new data to historical patterns 3 - Results are pushed immediately to deployed applications Machine Learning model 4 - Re-training is triggered, updating the model with the latest historical data Spring XD Spring XD Data Temperature Hot Warm
- 35. Machine Learning Concepts medium avg(x+1) relative strength (x) medium avg (x) price(x) Machine Learning Model (e.g. Linear Regression)
- 36. medium avg (x+1) relative strength (x) mediumavg (x) price(x) Machine Learning Model (e.g. Linear Regression) Features Label
- 37. Transform Sink SpringXD Extensible Open-Source Fault-Tolerant Horizontally Scalable Cloud-Native MachineLearning Enrich Filter Split Dashboard Indicators 1 2 Predict 3 Real data Simulator /Stocks /TechIndicators /Predictions
- 38. • Off-heap memorystorage • HDFS persistence • Lucene indexes • Spark connector • Cloud Foundry service • Distributed transactions …and other ideas from the Geode community! Roadmap
- 39. How to GetInvolved • http://geode.incubator.apache.org • Join the mailing lists; ask a question, answer a question, learn dev@geode.incubator.apache.org user@geode.incubator.apache.org • File a bug in JIRA • Update the wiki, website, or documentation • Create example applications • Use it in your project! We need you!
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