streaming architecture patterns

Best practices for streaming applicationsO’Reilly WebcastJune 21st/22nd, 2016Mark Grover | @mark_grover | Software Engineer

Ted Malaska | @TedMalaska | Principal Solutions Architect

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About the presenters

• Principal Solutions Architect at Cloudera

• Done Hadoop for 6 years– Worked with > 70 companies in 8

countries • Previously, lead architect at FINRA • Contributor to Apache Hadoop,

HBase, Flume, Avro, Pig and Spark• Contributor to Apache Hadoop,

HBase, Flume, Avro, Pig and Spark• Marvel fan boy, runner

• Software Engineer at Cloudera, working on Spark

• Committer on Apache Bigtop, PMC member on Apache Sentry (incubating)

• Contributor to Apache Hadoop, Spark, Hive, Sqoop, Pig and Flume

Ted Malaska Mark Grover

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About the book

• @hadooparchbook• hadooparchitecturebook.com• github.com/hadooparchitecturebook• slideshare.com/hadooparchbook

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Goal

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Understand common use-cases for streaming and

their architectures

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What is streaming?

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When to stream, and when not to

Constant low milliseconds & under

Low milliseconds to seconds, delay in case

of failures

10s of seconds or more, re-run in case of

failures

Real-time Near real-time Batch

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When to stream, and when not to



of failures


failures


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No free lunch



of failures


failures


“Difficult” architectures, lower latency “Easier” architectures, higher latency

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Use-cases for streaming

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Use-case categories

• Ingestion• Simple transformations

– Decision (e.g. Anomaly detection)

• Simple counts– Lambda, etc.

• Advanced usage– Machine Learning– Windowing

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Ingestion & Transformations

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What is ingestion?

Source Systems Destination systemStreaming engine

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But there multiple sources

Ingest

Source System 1

Destination systemSource System 2

Source System 3

Ingest

Ingest

Streaming engine Ingest

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But..

• Sources, sinks, ingestion channels may go down• Sources, sinks producing/consuming at different rates (buffering)• Regular maintenance windows may need to be scheduled• You need a resilient message broker (pub/sub)

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Need for a message broker

Source System 1


Source System 3

Ingest

Ingest

Ingest Extract Streaming engine

Push

Message broker

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Kafka

Source System 1


Source System 3

Ingest

Ingest


Push

Message broker

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Destination systems

Source System 1


Source System 3

Ingest

Ingest


Push

Message broker

Most common “destination” is a storage system

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Architecture diagram with a broker

Source System 1

Storage systemSource System 2

Source System 3

Ingest

Ingest


Push

Message broker

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Streaming engines

Source System 1


Source System 3

Ingest

Ingest


Push

Kafka Connect

ApacheFlume

Message broker

Apache Beam (incubating)

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Storage options

Source System 1


Source System 3

Ingest

Ingest


Push

Kafka Connect

ApacheFlume

Message broker

Apache Beam (incubating)

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SemanticsAt most once, Exactly once, At least once

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Semantic types

• At most once– Not good for many cases– Only where performance/SLA is more important than accuracy

• Exactly once– Expensive to achieve but desirable

• At least once– Easiest to achieve

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Review

Source System 1


Source System 3

Ingest

Ingest


Push

Message broker

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Semantics of our architecture

Source System 1


Source System 3

Ingest

Ingest


Push

Message broker

At least once

At least onceOrderedPartitioned

It depends It depends

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Transforming data in flight

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Streaming architecture for ingestion

Source System 1


Source System 3

Ingest

Ingest

Ingest ExtractStreaming ingestion process

Push

Kafka connect

ApacheFlume

Message broker

Can be used to do simple

transformations

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Ingestion and/or Transformation

1. Zero Transformation– No transformation, plain ingest, no schema validation– Keep the original format - SequenceFiles, Text, etc.– Allows to store data that may have errors in the schema

2. Format Transformation– Simply change the format of field, for example– Structured Format e.g. Avro– Which does schema validation

3. Enrichment Transformation– Atomic– Contextual

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#3 - Enrichment transformations

Atomic• Need to work with one event at a

time• Mask a credit card number• Add processing time or offset to the

record

Contextual• Need to refer to external context• Example - convert zip code to state,

by looking up a cache

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Atomic transformations

• Require no context• All streaming engines support it

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Contextual transformations

• Well supported by many streaming engines• Need to store the context somewhere.

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Where to store the context

1. Locally Broadcast Cached Dim Data– Local to Process (On Heap, Off Heap)– Local to Node (Off Process)

2. Partitioned Cache– Shuffle to move new data to partitioned cache

3. External Fetch Data (e.g. HBase, Memcached)

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#1a - Locally broadcast cached data

Could be On heap or Off heap

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#1b - Off process cached dataData is cached on the node, outside of process. Potentially in an external system like Rocks DB

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#2 - Partitioned cache data

Data is partitioned based on field(s) and then cached

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#3 - External fetch

Data fetched from external system

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A combination (partitioned cache + external)

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Anomaly detection using contextual transformations

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Storage systemsWhen to use which one?

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Storage Considerations

• Throughput• Access Patterns

– Scanning– Indexed– Reversed Indexed

• Transaction Level– Record/Document– File

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File Level

• HDFS• S3

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NoSql

• HBase• Cassandra• MongoDB

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Search

• SolR• Elastic Search

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NoSql-Sql

• Kudu

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Streaming enginesComparison

46© Cloudera, Inc. All rights reserved.

Tricks With Producers

•Send Source ID (requires Partitioning In Kafka)

•Seq

•UUID

•UUID plus time

•Partition on SourceID

•Watch out for repartitions and partition fail overs


Streaming Engines

•Consumer

•Flume, KafkaConnect, Streaming Engine

•Storm

•Spark Streaming

•Flink

•Kafka Streams


Consumer: Flume, KafkaConnect

•Simple and Works

•Low latency

•High throughput

•Interceptors

•Transformations

•Alerting

•Ingestions


Consumer: Streaming Engines

•Not so great at HDFS Ingestion

•But great for record storage systems

•HBase

•Cassandra

•Kudu

•SolR

•Elastic Search


Storm

•Old Gen

•Low latency

•Low throughput

•At least once

•Around for ever

•Topology Based


Spark Streaming

•The Juggernaut

•Higher Latency

•High Through Put

• Exactly Once

•SQL

•MlLib

•Highly used

•Easy to Debug/Unit Test

•Easy to transition from Batch

•Flow Language

•600 commits in a month and about 100 meetups


Spark Streaming

DStream

DStream

DStream

Single Pass

Source Receiver RDD

Source Receiver RDD

RDD

Filter Count Print

Source Receiver RDD

RDD

RDD

Single Pass

Filter Count Print

First Batch

Second Batch


DStream

DStream

DStream

Single Pass

Source Receiver RDD

Source Receiver RDD

RDD

Filter Count

Print

Source ReceiverRDD

partitions

RDDParition

RDD

Single Pass

Filter Count

Pre-first Batch

First Batch

Second Batch

Stateful RDD 1

Print

Stateful RDD 2

Stateful RDD 1

Spark Streaming


Flink

•I’m Better Than Spark Why Doesn’t Anyone use me

•Very much like Spark but not as feature rich

•Lower Latency•Micro Batch -> ABS

•Asynchronous Barrier Snapshotting

•Flow Language

•~1/6th the comments and meetups

•But Slim loves it ☺


Flink - ABS

Operator

Buffer


Operator

Buffer

Operator

Buffer

Flink - ABS

Barrier 1A Hit

Barrier 1B Still Behind


Operator

Buffer

Flink - ABS

Both Barriers Hit

Operator

Buffer

Barrier 1A Hit

Barrier 1B Still Behind

Check Point


Operator

Buffer

Flink - ABSBoth

Barriers Hit

Check Point

Operator

BufferBarrier is combined and can move on

Buffer can be flushed

out


Kafka Streams• The new Kid on the Block• When you only have Kafka• Low Latency• High Throughput• Not exactly once• Very Young• Flow Language• Very different hardware profile then others• Not widely supported• Not widely used• Worries about separation of concern


Summary about Engines• Ingestion

• Flume and KafkaConnect• Super Real Time and Special

• Consumer• Counting, MlLib, SQL

• Spark• Maybe future and cool

• Flink and KafkaStreams• Odd man out

• Storm


Abstractions

Code Abstractions

BeamSQL Abstraction

SQLUI Abstraction

StreamSets

Streaming Engines

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Counting

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Streaming and Counting

• Counting is easy right?• Back to Only once

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We started with Lambda

Pipe

Speed Layer

Batch Layer

Persist Results

Speed Results

Batch Results

Serving Layer

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Why did Streaming Suck

• Increments with Cassandra • Double increment• No strong consistency

• Storm without Kafka• Not only once• Not at least once

• Batch would have to re-process EVERY record to remove dups

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We have come a long way

• We don’t have to use Increments any more and we can have consistency• HBase

• We can have state in our streaming platform• Spark Streaming

• We don’t lose data• Spark Streaming• Kafka• Other options

• Full universe of Deduping• Again HBase with versions

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Increments

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Puts with State

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Advanced streamingWhen to use which one?

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Advanced Streaming

• Ad-hoc will produce Identify Value• Ad-hoc will become batch• The value will demand less latency on batch• Batch will become Streaming

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Advanced Streaming

• Requirements for Ideal Batch to Streaming frameworks• Something that can snap both paradigms• Something that can use the tools of Ad-hoc

• SQL• MlLib• R• Scala• Java

• Development through a common IDE• Debugging• Unit Testing• Common deployment model

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Advanced Streaming

• In Spark Streaming• A DStream is a collection of RDD with respect to micro batch

intervals• If we can access RDDs in Spark Streaming

• We can convert to Vectors• KMeans• Principal component analysis

• We can convert to LabeledPoint• NaiveBayes• Random Forest• Linear Support Vector Machines

• We can convert to a DataFrames• SQL• R

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Wrap-up

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Understand common use-cases for streaming and

their architecturesOur original goal

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Common streaming use-cases• Ingestion

– Transformation

• Counting– Lambda, etc.

• Advanced streaming

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Thank you!Mark Grover | @mark_grover

Ted Malaska | @TedMalaska

@hadooparchbook

hadooparchitecturebook.com

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Transformations with context

streaming architecture patterns

Engineering