Stream Processing with Apache Apex

Pramod Immaneni <pramod@datatorrent.com>PPMC Member, Senior Architect @DataTorrent IncMar 2nd, 2016

Stream Processing with Apache ApexApache Apex (incubating)

© 2015 DataTorrent2

What is Apex• Platform and framework to build highly scalable and fault-tolerant distributed applications

• 100% 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

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Applications on Apex• Distributed processing

• Application logic broken into operators that run in a distributed fashion across your cluster

• Natural programming model• Code as if you were writing regular 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

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Apex Platform Overview

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Apache Malhar Library

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Native Hadoop Integration

• YARN is the resource manager

• HDFS used for storing any persistent state

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Application Development Model

A Stream is a sequence of data tuplesA 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)

Filtered

Stream

Output StreamTuple Tuple

Filtered Stream

Enriched Stream

Enriched

Stream

er

Operator

er

Operator

er

Operator

er

Operator

er

Operator

er

Operator

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Advanced Windowing Support

Application window Sliding window and tumbling window

Checkpoint window No artificial latency

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Application in Java

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Operators

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Operators (contd)

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Partitioning and unification

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Advanced Partitioning

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Dynamic Partitioning

• Partitioning change while application is runningᵒ Change number of partitions at runtime based on statsᵒ Determine initial number of partitions dynamically

• Kafka operators scale according to number of kafka partitionsᵒ Supports re-distribution of state when number of partitions changeᵒ API for custom scaler or partitioner

unifiers not shown

1a 2a

1b 2b

3

2b

1b 2c

3

2a

2d

1a 2b

1b 2c 3b

2a

2d

3a1a

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How tuples are partitioned• Tuple hashcode and mask used to determine destination

partitionᵒ Mask picks the last n bits of the hashcode of the tupleᵒ hashcode method can be overridden

• StreamCodec can be used to specify custom hashcode for tuplesᵒ Can also be used for specifying custom serialization

tuple: {Name, 24204842, San Jose}

Hashcode: 001010100010101

Mask (0x11)

Partition

00 1

01 2

10 3

11 4

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Custom partitioning• Custom distribution of tuples

ᵒ E.g.. Broadcast

tuple:{Name, 24204842, San Jose}

Hashcode: 001010100010101

Mask (0x00)

Partition

00 1

00 2

00 3

00 4

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Fault Tolerance• 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 stateᵒ Frequency configurable per operatorᵒ Asynchronous and distributed by defaultᵒ Default store is HDFS

• Automatic detection and recovery of failed operatorsᵒ Heartbeat mechanism

• Buffering mechanism to ensure replay of data from recovered point so that there is no loss of data

• Application master state checkpointed

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Processing GuaranteesAtleast once• On recovery data will be replayed from a previous checkpoint

ᵒ Messages will not be lostᵒ Default mechanism and is suitable for most applications

• Can be used in conjunction with following mechanisms to achieve exactly-once behavior in fault recovery scenariosᵒ Transactions with meta information, Rewinding output, Feedback from

external entity, Idempotent operationsAtmost 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

Windowed Exactly once• Operators checkpointed every window

ᵒ Can be combined with transactional mechanisms to ensure end-to-end exactly once behavior

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Stream Locality• By default operators are deployed in containers (processes)

randomly on different nodes across the Hadoop cluster

• Custom locality for streamsᵒ Rack local: Data does not traverse network switchesᵒ Node local: Data is passed via loopback interface and frees up

network bandwidthᵒ Container local: Messages are passed via in memory queues

between operators and does not require serializationᵒ Thread local: Messages are passed between operators in a same

thread equivalent to calling a subsequent function on the message

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Data Processing Pipeline ExampleApp Builder

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Monitoring ConsoleLogical View

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Monitoring ConsolePhysical View

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Real-Time DashboardsReal Time Visualization

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ResourcesApache Apex Community Page - http://apex.incubator.apache.org/

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Extra Slides

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Application Programming Model

A Stream is a sequence of data tuplesAn 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)

Filtered Stream

Output StreamTuple Tuple

Filtered Stream

Enriched Stream

Enriched

Stream

er

Operator

er

Operator

er

Operator

er

Operator

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Partitioning and Scaling Out

• Operators can be dynamically scaled• Flexible Streams split• Parallel partitioning

• MxN partitioning • Unifiers

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Fault Tolerance OverviewStateful 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

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Machine Data ApplicationLogical View

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Machine Data ApplicationPhysical View