Devendra Tagare <devendrat@datatorrent.com>Software Engineer @DataTorrent Inc@devtagareJuly 6h, 2016

The next generation native Hadoop platformIntroduction to Apache Apex

What is Apex

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• Platform and runtime engine that enables development of scalable and fault-tolerant distributed applications

• Hadoop native• Process streaming or batch big data• High throughput and low latency• Library of commonly needed business logic• Write any custom business logic in your application

Applications on Apex

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• Distributed processing• Application logic broken into components called operators that run in a distributed

fashion across your cluster• 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

• Operators• Use library to build applications quickly• Write your own in Java using the API

• Operational insight – DataTorrent RTS• See how each operator is performing and even record data

Apex Stack Overview

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Apex Operator Library - Malhar

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Native Hadoop Integration

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• YARN is the resource manager

• HDFS used for storing any persistent state

Application Development Model

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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

Advanced Windowing Support

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Application window Sliding window and tumbling window Checkpoint window No artificial latency

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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NxM PartitionsUnifier

0 1 2 3

Logical DAG

0 1 2

1

1 Unifier

1

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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

Advanced Partitioning

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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

NIC

NIC

NIC

NIC

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

Dynamic Partitioning

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• 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

2b

2c3

2a

2d

1b

1a1a 2a

1b 2b

31a 2b

1b 2c 3b

2a

2d

3a

Unifiers not shown

How tuples are partitioned

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• 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

Custom partitioning

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• 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

Fault Tolerance

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• Operator state is checkpointed to a persistent storeᵒ Automatically performed by engine, no additional work needed by

operatorᵒ 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

• In-memory PubSub• Stores results emitted by operator until committed• Handles backpressure / spillover to local disk• Ordering, idempotency

Operator 1

Container 1

BufferServer

Node 1

Operator 2

Container 2

Node 2

Buffer Server

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Recovery Scenario… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1

sum0

… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1sum

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… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1sum10

… EW2, 1, 3, BW2, EW1, 4, 2, 1, BW1sum

7

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Processing Guarantees - Recovery

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Atleast 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

Exactly once• At least once + state recovery + operator logic to achieve end-

to-end exactly once

Stream Locality

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• 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

Next Gen Stream Data Processing• Data from variety of sources (IoT, Kafka, files, social media etc.)• Unbounded, continuous data streams

ᵒ Batch can be processed as stream (but a stream is not a batch)• (In-memory) Processing with temporal boundaries (windows)• Stateful operations: Aggregation, Rules, … -> Analytics• Results stored to variety of sinks or destinations

ᵒ Streaming application can also serve data with very low latency

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Browser

Web Server

Kafka Input(logs)

Decompress, Parse, Filter

Dimensions Aggregate Kafka

LogsKafka

Batch vs. Streaming

Credit: Gyula Fóra & Márton Balassi: Large-Scale Stream Processing in the Hadoop Ecosystem

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Architecture and FeaturesSpark Streaming Apex

Model micro-batch native streaming/data-in-motion

Language Java, Scala, client bindings Java (Scala)

API declarative compositional (DAG), declarative*

Locality data locality advanced processing locality

Latency high very low (millis)

Throughput very high very high

Scalability scheduler limit horizontal

Partitioning standard advanced (parallel pipes, unifiers)

Connector Library Limited (certification), externally maintained

Rich library of connectors and processors, part of Apex (Malhar)

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OperabilitySpark Streaming Apex

State Management RDD, user code checkpointing

Recovery RDD lineage incremental (buffer server)

Processing Sem. exactly-once* end-to-end exactly-once

Backpressure user configuration Automatic (buffer server memory + disk)

Elasticity yes w/ limited control yes w/ full user control

Dynamic topology no yes

Security Kerberos Kerberos, RBAC*, LDAP*

Multi-tennancy depends on cluster manager

YARN, full isolation

DevOps tools basic REST API, DataTorrent RTS25

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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Resources

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• Apache Apex - http://apex.apache.org/• Subscribe - http://apex.apache.org/community.html• Download - https://www.datatorrent.com/download/• Twitter

ᵒ @ApacheApex; Follow - https://twitter.com/apacheapexᵒ @DataTorrent; Follow – https://twitter.com/datatorrent

• Meetups - http://www.meetup.com/topics/apache-apex• Webinars - https://www.datatorrent.com/webinars/• Videos - https://www.youtube.com/user/DataTorrent• Slides - http://www.slideshare.net/DataTorrent/presentations • Startup Accelerator Program - Full featured enterprise product

ᵒ https://www.datatorrent.com/product/startup-accelerator/

Maximize Revenue w/ real-time insights

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PubMatic is the leading marketing automation software company for publishers. Through real-time analytics, yield management, and workflow automation, PubMatic enables publishers to make smarter inventory decisions and improve revenue performance

Business Need Apex based Solution Client Outcome

• Ingest and analyze high volume clicks & views in real-time to help customers improve revenue

- 200K events/second data flow• Report critical metrics for campaign

monetization from auction and client logs - 22 TB/day data generated

• Handle ever increasing traffic with efficient resource utilization

• Always-on ad network

• DataTorrent Enterprise platform, powered by Apache Apex

• In-memory stream processing• Comprehensive library of pre-built

operators including connectors• Built-in fault tolerance• Dynamically scalable• Management UI & Data Visualization

console

• Helps PubMatic deliver ad performance insights to publishers and advertisers in real-time instead of 5+ hours

• Helps Publishers visualize campaign performance and adjust ad inventory in real-time to maximize their revenue

• Enables PubMatic reduce OPEX with efficient compute resource utilization

• Built-in fault tolerance ensures customers can always access ad network

Industrial IoT applications

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GE is dedicated to providing advanced IoT analytics solutions to thousands of customers who are using their devices and sensors across different verticals. GE has built a sophisticated analytics platform, Predix, to help its customers develop and execute Industrial IoT applications and gain real-time insights as well as actions.

Business Need Apex based Solution Client Outcome

• Ingest and analyze high-volume, high speed data from thousands of devices, sensors per customer in real-time without data loss

• Predictive analytics to reduce costly maintenance and improve customer service

• Unified monitoring of all connected sensors and devices to minimize disruptions

• Fast application development cycle• High scalability to meet changing business

and application workloads

• Ingestion application using DataTorrent Enterprise platform

• Powered by Apache Apex• In-memory stream processing• Built-in fault tolerance• Dynamic scalability• Comprehensive library of pre-built operators• Management UI console

• Helps GE improve performance and lower cost by enabling real-time Big Data analytics

• Helps GE detect possible failures and minimize unplanned downtimes with centralized management & monitoring of devices

• Enables faster innovation with short application development cycle

• No data loss and 24x7 availability of applications

• Helps GE adjust to scalability needs with auto-scaling

We Are Hiring

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• jobs@datatorrent.com• Developers/Architects• QA Automation Developers• Information Developers• Build and Release• Community Leaders

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