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Tale of two stream processing frameworks

Apache Storm & Apache Flink

Karthik Deivasigamani

@WalmartLabs

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Streaming

• Stream

– Continuous flow

• Streaming Data

– Streaming data is data that is continuously

generated by different sources.

– Unbounded data

• Stream Processing

– processing of data in motion, or in other

words, computing on data directly as it is

produced or received

– data processing engine that is designed with

infinite data sets in mind

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Retail Data

• Catalog Data

• Pricing Data

• Clickstream logs

• Payments

• Order Data

• Inventory

• Delivery Logistics

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Not so long ago..

• Data submitted as feeds

• Periodic Data Collection

• Data Processed In Batches

• Runs offline

• Delay between actual time &

processing time

• Failures

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Need For Speed – Fast Data

• Catalog Updates

• Price Updates

• Fraud Detection

• Out of stock

• Delivery alerts

• Personalization

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Catalog Use Case

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Catalog Functions

• Normalization

• Classification

• Product Matching

• Shelving

• Attribute Extraction

• Grouping

• Image

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Characteristics of ingestion pipeline

• Zero message loss

• Fault Tolerance

• Source based priority queue

• Scale to millions of product updates/hour

• Near Real Time Updates

• Checkpoint at various stages

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Apache Storm

• Created by Nathan Marz

• Stream Abstraction

• Spouts, Bolts, Topology

• Trident

• Kafka Integration

• Message processing

guarantees

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Storm Cluster

• Nimbus

– distributing code

– assigning tasks to machines

– monitoring for failures

• Supervisor

– communicates with Nimbus

through Zookeeper

– starts and stops workers

according to signals from Nimbus

• Zookeeper

– Coordinates the storm cluster

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Key Concepts

• Tuples

– Named list of values where each

value can be any type.

• Stream

– unbounded sequence of tuples

• Spout

– sources of streams in a

computation

• Bolts

– process input streams and

produce output streams

• Topology

– DAG - network of spouts and

bolts

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Stream Grouping

• Shuffle Grouping

• Fields Grouping

• All grouping

• Global Grouping

• Local or Shuffle grouping

• Direct Grouping

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Parallelism of a Storm Topology

• Worker processes

– Executes a subset of a topology

• Executors (Threads)

– Is a thread that is spawned by a

worker process.

– It may run one or more tasks for

the same component (spout or

bolt).

• Tasks

– performs the actual data processing

— each spout or bolt that you

implement in your code executes as

many tasks across the cluster

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Guaranteeing Message Processing

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Micro Service vs Bolt

• Choice of language

• Teams operate independently

• Platform with pluggable services

Bolt

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Catalog Pipeline

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Challenges

• Validations at various stages

• Async IO using RxJava, Hystrix

• Hystrix Circuit Breaker

• Failing Tuples

• Fetch-size, increase workers,

increase bolt parallelism

• Data Errors

• Services taking longer

• Service outage

• Fatal Errors

• Spike in traffic

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Lessons Learnt

• Things will fail

• Monitor everything

• Automation

• Scale is not a feature

• Logs don’t lie

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Pricing Use Case

• Competitive pricing (EDLP)

• Seller price updates

• Handle spike during holidays

• Promotions

• Anomaly Detection

• Accuracy

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Characteristics of ingestion pipeline

• Exactly Once

• Order Guarantee

• Stateful

• Handle tens of millions of

updates/hour

• NRT price update on website

• Traceability

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Apache Flink

• Project Stratosphere in

Universities around Berlin

• data Artisans founded in 2014

• Process Unbounded and

Bounded Data

• Exactly Once

• Stateful & Flexible API

• Alibaba was using it at scale

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Apache Flink - Overview

• Data source: Incoming data that Flink processes

• Transformations: The processing step, when Flink modifies incoming data

• Data sink: Where Flink sends data after processing

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Apache Flink - Runtime

Footer

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Stateful Stream Processing

• "state" is shared between events.

• Past events can influence the way current

events are processed.

• Embedded database (Rocks DB) for state.

• Local state needs to be protected against

failures to avoid data loss.

• Checkpointing to guarantee persistence of

state.

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Flink Checkpointing (Chandy-Lamport Algorithm)

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Exactly Once - Explained

• The label ―exactly-once‖ is misleading in

describing what is done exactly once.

• No Stream Processing can guarantee

exactly-once event processing.

• Flink guarantees exactly-once state

updates.

• Flink uses Chandy and Lamport Algorithm,

to draw consistent snapshots of current

state to create a checkpoint.

• Flink restarts an application using the most

recently completed checkpoint as a starting

point.

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Duplicate Events

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Pricing Pipeline

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Challenges

• HTTP/DB lookup calls

• Huge payload choking network

• Isolation

• Buffer bloat

• Async I/O Operator

• Operator Chaining

• Mesos / YARN

• taskmanager.memory.segment-size

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What we learnt • Flink is fast, APIs are super easy to use.

• Avoid network shuffle and use forward / operator

chaining.

• Use accumulators to monitor the progress of your

application.

• Checkpoint failures indicate that your application is

running slow.

• Monitor everything – lag, checkpoints, latency etc

• For application inherently slow configure your

buffers to accommodate for buffer bloat, so that

checkpoints don’t fail.

• Join the flink users mailing list and ask questions!

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Apache Storm vs Apache Flink

Feature

Winner

True streaming Yes Yes Tie

Speed Fast Amazingly fast

Overall maturity Very stable, haven’t really encountered storm bugs that hit us in production.

Little behind – ran into lots of fink bugs, some of it is addressed now.

API Used to be very primitive with until 1.0

Rich API and you can achieve lot by writing very few lines of code.

Windowing, Join They added support in 1.2 Excellent out of the box support for windowing and join.

Tie

Monitoring / Deployment Better isolation of jobs with the process model

You need YARN/Mesos to get better isolation.

Tie (assumes you are running Flink on YARN)

Stateful Stream processing WIP (apache storm 2.0) Supported with rocksdb. You can also query the state outside your stream processing system.

Message Processing Guarantee Supports - At least once, At most once, Exactly once (need trident)

Supports - At least once, At most once, Exactly Once (state is touched exactly once)

Tie

Backpressure Max spout pending can be used to adjust

Handle automatically

Async IO support No native support Out of the box

Streaming SQL WIP (apache storm 2.0) Very early stage -

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What should I pick

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Future of streaming - Cloud

Amazon Kinesis Streams

Functions as stream processors

Cloud Flow

Confluent Cloud

Event Hub – Kafka Compatible

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Thank You!

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