This document compares the Apache Storm and Apache Flink streaming frameworks. Both frameworks can be used to process streaming data in real-time. Storm uses spouts and bolts to define topologies while Flink uses data sources, transformations, and sinks. Flink focuses on exactly-once processing semantics using checkpointing while Storm provides at-least-once guarantees. The document discusses using each framework for catalog and pricing use cases at a large retailer and highlights challenges faced and lessons learned. Overall, both frameworks are mature but Flink is seen as faster with richer APIs and better support for features like windowing, joins, and stateful stream processing.

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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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https://indiacareers.walmartlabs.com/