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Introduction to Apache Apex

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Apache Apex (incubating) is a next generation native Hadoop big data platform. This talk will cover details about how it can be used as a powerful and versatile platform for big data.

Presented by Pramod Immaneni at Data Riders Meetup hosted by Nexient on Apr 5th, 2016

Published in: Technology
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Introduction to Apache Apex

  1. 1. Pramod Immaneni <pramod@datatorrent.com> PPMC Member, Architect @DataTorrent Inc Apr 5th, 2016 The next generation native Hadoop platform Introduction to Apache Apex (incubating)
  2. 2. © 2015 DataTorrent What is Apex 2 • Platform and framework to build scalable and fault-tolerant distributed applications • Hadoop native • Build any custom logic in your application • Unobtrusive API to facilitate distributed application development • Runtime engine to ensure fault tolerance, scalability and data flow • Process streaming or batch big data • High throughput and low latency • Realtime applications
  3. 3. © 2015 DataTorrent Applications on Apex 3 • Distributed processing • Application logic broken into components called operators that run in a distributed fashion across your cluster • Natural programming model • Code as if you were writing normal Java logic • Maintain state in your application variables • Scalable • Operators can be scaled up or down at runtime according to the load and SLA • Fault tolerant • Automatically recover from node outages without having to reprocess from beginning • State is preserved • Long running applications • Operational insight – DataTorrent RTS • See how each operator is performing and even record data
  4. 4. © 2015 DataTorrent Apex Platform Overview 4
  5. 5. © 2015 DataTorrent Apache Malhar Library 5
  6. 6. © 2015 DataTorrent Native Hadoop Integration 6 • YARN is the resource manager • HDFS used for storing any persistent state
  7. 7. © 2015 DataTorrent Application Development Model 7  A Stream is a sequence of data tuples  A typical Operator takes one or more input streams, performs computations & emits one or more output streams • Each Operator is YOUR custom business logic in java, or built-in operator from our open source library • Operator has many instances that run in parallel and each instance is single-threaded  Directed Acyclic Graph (DAG) is made up of operators and streams Directed Acyclic Graph (DAG) Output Stream Tupl e Tupl e er Operator er Operator er Operator er Operator er Operator er Operator
  8. 8. © 2015 DataTorrent Advanced Windowing Support 8  Application window  Sliding window and tumbling window  Checkpoint window  No artificial latency
  9. 9. © 2015 DataTorrent Application in Java 9
  10. 10. © 2015 DataTorrent Operators 10
  11. 11. © 2015 DataTorrent Operators (contd) 11
  12. 12. © 2015 DataTorrent Partitioning and unification 12 NxM PartitionsUnifier 0 1 2 3 Logical DAG 0 1 2 1 1 Unifier 1 20 Logical Diagram Physical Diagram with operator 1 with 3 partitions 0 Unifier 1a 1b 1c 2a 2b Unifier 3 Physical DAG with (1a, 1b, 1c) and (2a, 2b): No bottleneck Unifier Unifier0 1a 1b 1c 2a 2b Unifier 3 Physical DAG with (1a, 1b, 1c) and (2a, 2b): Bottleneck on intermediate Unifier
  13. 13. © 2015 DataTorrent Advanced Partitioning 13 0 1a 1b 2 3 4Unifier Physical DAG 0 4 3a2a1a 1b 2b 3b Unifier Physical DAG with Parallel Partition Parallel Partition Container uopr uopr1 uopr2 uopr3 uopr4 uopr1 uopr2 uopr3 uopr4 dopr dopr doprunifier unifier unifier unifier Container Container NICNIC NICNIC NIC Container NIC Logical Plan Execution Plan, for N = 4; M = 1 Execution Plan, for N = 4; M = 1, K = 2 with cascading unifiers Cascading Unifiers 0 1 2 3 4 Logical DAG
  14. 14. © 2015 DataTorrent Dynamic Partitioning 14 • Partitioning change while application is running ᵒ Change number of partitions at runtime based on stats ᵒ Determine initial number of partitions dynamically • Kafka operators scale according to number of kafka partitions ᵒ Supports re-distribution of state when number of partitions change ᵒ API for custom scaler or partitioner 2b 2c 3 2a 2d 1b 1a1a 2a 1b 2b 3 1a 2b 1b 2c 3b 2a 2d 3a Unifiers not shown
  15. 15. © 2015 DataTorrent How tuples are partitioned 15 • Tuple hashcode and mask used to determine destination partition ᵒ Mask picks the last n bits of the hashcode of the tuple ᵒ hashcode method can be overridden • StreamCodec can be used to specify custom hashcode for tuples ᵒ Can also be used for specifying custom serialization tuple: { Name, 24204842, San Jose } Hashcode: 00101010001 0101 Mask (0x11) Partition 00 1 01 2 10 3 11 4
  16. 16. © 2015 DataTorrent Custom partitioning 16 • Custom distribution of tuples ᵒ E.g.. Broadcast tuple:{ Name, 24204842, San Jose } Hashcode: 00101010001 0101 Mask (0x00) Partition 00 1 00 2 00 3 00 4
  17. 17. © 2015 DataTorrent Fault Tolerance 17 • Operator state is checkpointed to a persistent store ᵒ Automatically performed by engine, no additional work needed by operator ᵒ In case of failure operators are restarted from checkpoint state ᵒ Frequency configurable per operator ᵒ Asynchronous and distributed by default ᵒ Default store is HDFS • Automatic detection and recovery of failed operators ᵒ Heartbeat mechanism • Buffering mechanism to ensure replay of data from recovered point so that there is no loss of data • Application master state checkpointed
  18. 18. © 2015 DataTorrent Processing Guarantees 18 Atleast once • On recovery data will be replayed from a previous checkpoint ᵒ Messages will not be lost ᵒ Default mechanism and is suitable for most applications • Can be used in conjunction with following mechanisms to achieve exactly-once behavior in fault recovery scenarios ᵒ Transactions with meta information, Rewinding output, Feedback from external entity, Idempotent operations Atmost once • On recovery the latest data is made available to operator ᵒ Useful in use cases where some data loss is acceptable and latest data is sufficient Exactly once • At least once + state recovery + operator logic to achieve end-to-end exactly once
  19. 19. © 2015 DataTorrent Stream Locality 19 • By default operators are deployed in containers (processes) randomly on different nodes across the Hadoop cluster • Custom locality for streams ᵒ Rack local: Data does not traverse network switches ᵒ Node local: Data is passed via loopback interface and frees up network bandwidth ᵒ Container local: Messages are passed via in memory queues between operators and does not require serialization ᵒ Thread local: Messages are passed between operators in a same thread equivalent to calling a subsequent function on the message
  20. 20. © 2015 DataTorrent Data Processing Pipeline Example App Builder 20
  21. 21. © 2015 DataTorrent Monitoring Console Logical View 21
  22. 22. © 2015 DataTorrent Monitoring Console Physical View 22
  23. 23. © 2015 DataTorrent Real-Time Dashboards Real Time Visualization 23
  24. 24. © 2015 DataTorrent Resources 24 Apache Apex Community Page - http://apex.incubator.apache.org/
  25. 25. End 25
  26. 26. © 2015 DataTorrent Extra Slides
  27. 27. © 2015 DataTorrent Application Programming Model 27  A Stream is a sequence of data tuples  An Operator takes one or more input streams, performs computations & emits one or more output streams • Each Operator is YOUR custom business logic in java, or built-in operator from our open source library • Operator has many instances that run in parallel and each instance is single-threaded  Directed Acyclic Graph (DAG) is made up of operators and streams Directed Acyclic Graph (DAG) Output StreamTuple Tuple er Operator er Operator er Operator er Operator
  28. 28. © 2015 DataTorrent Partitioning and Scaling Out 28 • Operators can be dynamically scaled • Flexible Streams split • Parallel partitioning • MxN partitioning • Unifiers
  29. 29. © 2015 DataTorrent Fault Tolerance Overview 29 Stateful Fault Tolerance Processing Semantics Data Locality  Supported out of the box – Application state – Application master state – No data loss  Automatic recovery  Lunch test  Buffer server  At least once  At most once  Exactly once  Stream locality for placement of operators  Rack local – Distributed deployment  Node local – Data does not traverse NIC  Container local – Data doesn’t need to be serialized  Thread local – Operators run in same thread  Data locality
  30. 30. © 2015 DataTorrent Machine Data Application Logical View 30
  31. 31. © 2015 DataTorrent Machine Data Application Physical View 31

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