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Hadoop Ecosystem and Low Latency Streaming Architecture
The document discusses the relevance of big data and the Hadoop ecosystem, emphasizing the need for low latency streaming architecture. It covers various components such as Flume, Kafka, and Storm, explaining their roles in data collection, messaging, and real-time processing. Additionally, it presents a demo using these technologies to analyze Twitter data for trending topics, showcasing their integration and practical application.
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Hadoop Ecosystem and Low Latency Streaming Architecture
- 1. Hadoop Ecosystem andLow Latency Streaming Architecture InSemble Inc. http://www.insemble.com
- 2. Agenda What is BigData and why it is relevant ?1 Flume, Kafka and Storm4 Reference Architecture for Low Latency Streaming3 Hadoop Ecosystem2 Demo5
- 3. Big Data Definitions •Wikipedia defines it as “Data Sets with sizes beyond the ability of commonly used software tools to capture, curate, manage and process data within a tolerable elapsed time” • Gartner defines it as Data with the following characteristics – High Velocity – High Variety – High Volume • Another Definition is “Big Data is a large volume, unstructured data which cannot be handled by traditional database management systems ”
- 4. Why a gamechanger • Schema on Read – Interpreting data at processing time – Key, Values are not intrinsic properties of data but chosen by person analyzing the data • Move code to data – With traditional, we bring data to code and I/O becomes a bottleneck – With distributed systems, we have to deal with our own checkpointing/recovery • More data beats better algorithms
- 5. Enterprise Relevance • MissedOpportunities – Channels – Data that is analyzed • Constraint was high cost – Storage – Processing • Future-proof your business – Schema on Read – Access pattern not as relevant – Not just future-proofing your architecture
- 6. Hadoop Ecosystem Source: ApacheHadoop Documentation
- 7. Hadoop 2 withYARN Source: Hadoop In Practice by Alex Holmes
- 8. Big Data Journey ➢Real time Insight from all channels ➢ IT is key differentiator for your business ➢ Perfect alignment of Business and IT ➢ Ad Hoc Data Exploration ➢ Batch, Interactive, Real time use cases ➢ Predictive Analytics, Machine Learning ➢ Consolidated Analytics ➢ ETL ➢ Time Constraints ➢ Security standards defined ➢ Governance Standards Defined ➢ Integrated with the Enterprise ➢ Evaluate Business Benefits ➢ Understand Ecosystem ➢ Identify Platform Aware of Benefits Execute Expand Managed Optimized - Scout for Opportunities - Pilot project - Multiple Use cases - Governance Model - Core competency Journey Over Time BusinessValue Effects GREAT GOOD
- 9. Real time StreamProcessing Architecture with Hadoop
- 10. Flume Architecture • Distributedsystem for collecting and aggregating from multiple data stores to a centralized data store • Agent is a JVM that hosts the Flume components • Channel will store message until picked by a sink • Different types of Flume sources • Source and Sink are decoupled
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- 13. Kafka Introduction • MessagingSystem which is distributed, partitioned and replicated • Kafka brokers run as a cluster • Producers and Consumers can be written in any language
- 14. Topic • Ordered, immutablesequence numbers • Retains messages until a period of time • “Offset” of where they are is controlled by the consumer • Each partition is replicated and has “leader” and 0 or more “follower”. R/W only done on leader
- 15. Producers and Consumers •Producer controls which partition messages goes to • Supports both Queuing and Pub/Sub – Abstraction called Consumer group • Ordering within Partition – Ordering for subscriber has to be done with only one subscriber to that partition
- 16. Storm Introduction • Distributedreal time computational system –Process unbounded streams of data –Can use multiple programming languages –Scalable, fault-tolerant and guarantees that data will be processed • Use Cases –Real time analytics, online machine learning –Continuous Computation –Distributed RPC –ETL • Concepts –Topology –Spouts –Bolts
- 17. Concepts • Storm Cluster –Master node(Nimbus) • Distributing code • Assigns tasks to machines • Monitors for failures – Worker nodes(Supervisor) • Starts/stops worker processes • Each worker process executes subset of a topology – Zookeeper • Coordinates between Nimbus and Supervisors • Nimbus and Supervisors completely stateless • State maintained by Zookeeper or local disks
- 18. Details • Stream – Unboundedsequence of tuples • Spout(write logic) – Source of stream. Emits tuples • Bolt(write logic) – Processes streams and emits tuples • Topology – DAG of spouts and bolts – Submit a topology to a Storm cluster – Each node runs in parallel and parallelism is controlled
- 19. Stream groupings • Tellsa topology how to send tuples between two components • Since tasks are executed in parallel, how do we control which tasks the tuples are being sent to
- 20. Why Use Twitteras Data Source
- 21. Demo - TwitterTopN Trending Topic • Method 1 — Flume with interceptor • Method 2 — Storm with custom Twitter Spout • Method 3 — Flume + Kafka + Storm
- 22. Demo - TwitterTopN Trending Topic • Use Flume Twitter Source to ingest data and publish event to Kafka topic • Use Kafka as messaging backbone • Use Storm as an Real-Time event processing system to calculate TopN trending topic • Use Redis to store the TopN Result • Use Node.js/JQuery for visualization
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- 24. Demo: Start RedisServer
- 25. Demo: Start Node.jsserver
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- 27. Demo: Start FlumeAgent
- 28. Demo: Storm ConsoleOutput
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- 30. Flume Agent —Source
- 31. Flume Agent —Channel
- 32. Flume Agent —Sink
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- 34. Submit Topology toStorm Production Cluster
- 35. Submit Topology toTest Cluster
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- 39. Questions? Vijay Mandava: vijay@insemble.com LanJiang: lan@insemble.com / @Lan_Jiang