Flink vs Spark by Slim Baltagi 151016065205 Lva1 App6891

7/23/2019 Flink vs Spark by Slim Baltagi 151016065205 Lva1 App6891

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Flink vs. Spark

Slim Baltagi @SlimBaltagiDirector of Big Data Engineering, Fellow

Capital One
https://twitter.com/SlimBaltagihttps://twitter.com/SlimBaltagi


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Agenda

I. otivation for t!is talkII. Apac!e Flink vs. Apac!e Spark"

III. #ow Flink is $sed at Capital One"

I%. &!at are some ke' takeawa's"


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I. otivation for t!is talk

(. arketing fl$ff

). Conf$sing statements

*. B$rning +$estions incorrect or

o$tdated answers

-. #elping ot!ers eval$ating Flink vs.Spark


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(. arketing fl$ff


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(. arketing fl$ff


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). Conf$sing statements

Sparkis alread' an e/cellent piece of software and is

advancing ver' +$ickl'.0o vendor 1 no new pro2ect 1is likel' to catc! $p. C!asing Spark wo$ld 3e a waste of

time, and wo$ld dela' availa3ilit' of real4time anal'tic

and processing services for no good reason.5 So$rce6

ap7ed$ce and Spark, ike Olson. C!ief Strateg'

Officer, Clo$dera. Decem3er, *8t!)8(*!ttp699vision.clo$dera.com9mapred$ce4spark9

:oal6 one engine forall data so$rces, workloadsand

environments. So$rce6 Slide (; of a!aria. C?O, Data3ricks.Fe3r$ar' )8t!, )8(;. !ttp699www.slides!are.net9data3ricks9new4directions4for4apac!e4spark4in4)8(;
http://vision.cloudera.com/mapreduce-spark/http://www.slideshare.net/databricks/new-directions-for-apache-spark-in-2015http://www.slideshare.net/databricks/new-directions-for-apache-spark-in-2015http://www.slideshare.net/databricks/new-directions-for-apache-spark-in-2015http://www.slideshare.net/databricks/new-directions-for-apache-spark-in-2015http://vision.cloudera.com/mapreduce-spark/http://vision.cloudera.com/mapreduce-spark/


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o$tdated answersro2ects t!at depend on smart optimiersrarel' work

well in real life.5 C$rt onas!, onas! 7esearc!.an$ar' (, )8(;!ttp699www.comp$terworld.com9article9)(893ig4data4digest4!ow4man'4!adoop

s4do4we4reall'4need.!tml

Flink is 3asicall' a Spark alternative o$t of :erman',w!ic! I=ve 3een dismissing as $nneeded5. C$rt

onas!, onas! 7esearc!, arc! ;, )8(;. !ttp699www.d3ms).com9)8(;98*98;9cask4and4cdap9

Of co$rse, t!is is all a 3$llis! arg$ment for Spark Gor

Flink, if I=m wrong to dismiss its c!ances as a SparkcompetitorH.5 C$rt onas!, onas! 7esearc!,Septem3er ), )8(;. !ttp699www.d3ms).com9)8(;989)9t!e4potential4significance4of4clo$dera4k$d$9
http://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.htmlhttp://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.htmlhttp://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.htmlhttp://www.dbms2.com/2015/03/05/cask-and-cdap/http://www.dbms2.com/2015/03/05/cask-and-cdap/http://www.dbms2.com/2015/09/28/the-potential-significance-of-cloudera-kudu/http://www.dbms2.com/2015/09/28/the-potential-significance-of-cloudera-kudu/http://www.dbms2.com/2015/09/28/the-potential-significance-of-cloudera-kudu/http://www.dbms2.com/2015/09/28/the-potential-significance-of-cloudera-kudu/http://www.dbms2.com/2015/09/28/the-potential-significance-of-cloudera-kudu/http://www.dbms2.com/2015/03/05/cask-and-cdap/http://www.dbms2.com/2015/03/05/cask-and-cdap/http://www.dbms2.com/2015/03/05/cask-and-cdap/http://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.htmlhttp://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.htmlhttp://www.computerworld.com/article/2871760/big-data-digest-how-many-hadoops-do-we-really-need.html


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o$tdated answers

?!e 3enefit of SparkJs micro43atc! model is t!at 'o$get f$ll fa$lt4tolerance and e/actl'4once processing

for t!e entire comp$tation, meaning it can recover all

state and res$lts even if a node cras!es. Flink and

Storm donJt provide t!isK5 atei >a!aria. C?O,

Data3ricks. a' )8(;!ttp699www.kdn$ggets.com9)8(;98;9interview4matei4a!aria4creator4apac!e4

spark.!tml

I $nderstand Spark Streaming $ses micro43atc!ing.

Does t!is increase latenc'" &!ile Spark does $se a

micro43atc! e/ec$tion model, t!is does not !avem$c! impact on applicationsK5 !ttp699spark.apac!e.org9fa+.!tml
http://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.htmlhttp://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.htmlhttp://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.htmlhttp://spark.apache.org/faq.htmlhttp://spark.apache.org/faq.htmlhttp://spark.apache.org/faq.htmlhttp://spark.apache.org/faq.htmlhttp://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.htmlhttp://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.htmlhttp://www.kdnuggets.com/2015/05/interview-matei-zaharia-creator-apache-spark.html


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-. #elp ot!ers eval$ating Flink vs. SparkBesides t!e marketing fl$ff, t!e conf$sing statements,

t!e incorrect or o$tdated answers to 3$rning

+$estions, t!e little information on t!e s$32ect of Flinkvs. Spark is availa3le piecemealL

&!ile eval$ating different stream processing tools at

Capital One, we 3$ilt a framework listing categories

and over (88 criteria to assess t!ese streamprocessing tools.

In t!e ne/t section, I=ll 3e s!aring t!is framework and

$se it to compare Spark and Flink on a few ke'

criteria.

&e !ope t!is will 3e 3eneficial to 'o$ as well w!en

selecting Flink and9or Spark for stream processing.


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Agenda

I. otivation for t!is talkII. Apac!e Flink vs. Apac!e Spark"




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II. Apac!e Flink vs. Apac!e Spark"

(. &!at is Apac!e Flink"

). &!at is Apac!e Spark"

*. Framework to eval$ate Flink and Spark

-. Flink vs. Spark on a few ke' criteria

;. F$t$re work


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(.&!at is Apac!e Flink"

S+$irrel6 Animal. In !armon' wit! ot!er animals in t!e

#adoop ecos'stem G>ooH6 elep!ant, pig, p't!on,

camel,...S+$irrel6 reflects t!e meaning of t!e word


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(.&!at is Apac!e Flink"

Apac!e Flink is an open so$rce platform for

distri3$ted stream and 3atc! data processing.5!ttps699flink.apac!e.org9

See also t!e definition in &ikipedia6!ttps

699en.wikipedia.org9wiki9Apac!eMFlink
https://flink.apache.org/https://en.wikipedia.org/wiki/Apache_Flinkhttps://en.wikipedia.org/wiki/Apache_Flinkhttps://en.wikipedia.org/wiki/Apache_Flinkhttps://en.wikipedia.org/wiki/Apache_Flinkhttps://en.wikipedia.org/wiki/Apache_Flinkhttps://flink.apache.org/https://flink.apache.org/


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Apac!e SparkR is a fast and general engine for

large4scale data processing.5 !ttp699spark.apac!e.org9See also definition in &ikipedia6 !ttps

699en.wikipedia.org9wiki9Apac!eMSpark

Nogo was picked to reflect Nig!tning4fast cl$stercomp$ting
http://spark.apache.org/http://spark.apache.org/https://en.wikipedia.org/wiki/Apache_Sparkhttps://en.wikipedia.org/wiki/Apache_Sparkhttps://en.wikipedia.org/wiki/Apache_Sparkhttps://en.wikipedia.org/wiki/Apache_Sparkhttps://en.wikipedia.org/wiki/Apache_Sparkhttp://spark.apache.org/http://spark.apache.org/


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*. Framework to eval$ate Flink and Spark

(. Backgro$nd). Fit4for4p$rpose Categories

*. Organiational4fit Categories

-. iscellaneo$s9Ot!er Categories


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(. Backgro$nd

(.( Definition

(.) Origin

(.* at$rit'

).- %ersion

(.; :overnance model

(. Nicense model


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). Fit4for4p$rpose Categories

).( Sec$rit'

).) rovisioning onitoring Capa3ilities).* Natenc' rocessing Arc!itect$re

).- State anagement

).; rocessing Deliver' Ass$rance

). Data3ase Integrations, 0ative vs. ?!ird

part' connector

). #ig! Availa3ilit' 7esilienc'

). Ease of Development

). Scala3ilit'

).(8 ni+$e Capa3ilities9Qe' Differentiators


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).( Sec$rit'

).(.( A$t!entication, A$t!oriation

).(.) Data at rest encr'ption Gdata persisted in t!e

frameworkH

).(.* Data in motion encr'ption Gprod$cer

4Tframework 4T cons$merH

).(.- Data in motion encr'ption Ginter4node

comm$nicationH


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).) rovisioning onitoring Capa3ilities

2.2.1 Robustness of Administration

2.2.2 Ease of maintenance Does tec!nolo"#

provide con$"uration% deplo#ment% scalin"%

monitorin"% performance tunin" and auditin"

capabilities&2.2.' onitorin" Alertin"

2.2.* +o""in"

2.2., Audit

2.2.- ransparent p"rade 0ersion up"rade

it! minimum dontime


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). Fit4for4p$rpose Categories).* Natenc' rocessing Arc!itect$re

2.'.1 Supports tuple at a time% microbatc!%

transactional updates and batc! processin"

2.'.2 3omputational model

2.'.' Abilit# to reprocess !istorical data from

source

2.'.* Abilit# to reprocess !istorical data from

native en"ine

2.'., 3all e4ternal source (A56/database calls)

2.'.- 6nte"ration it! 7atc! (static) source2.'.8 Data #pes (ima"es% sound etc.)

2.'.9 Supports comple4 event processin" and

pattern detection vs. continuous operator

model (lo latenc#% :o control)


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).* Natenc' rocessing Arc!itect$re

2.'.;


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).- State anagement

).-.( Statef$l vs. Stateless

).-.) Is statef$l data ersisted locall' vs.

e/ternal data3ase vs. Ep!emeral).-.* 0ative rolling, t$m3ling and !opping

window s$pport

).-.- 0ative s$pport for integrated data store


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).; rocessing Deliver' Ass$rance

).;.( :$arantee GAt least onceH).;.) :$arantee GAt most onceH

).;.* :$arantee GE/actl' onceH

).;.- :lo3al Event order g$aranteed).;.; >uarantee predictable and

repeatable outcomes( deterministic or

not)


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). Data3ase Integrations, 0ative vs. ?!ird

part' connector

)..( 0oSPN data3ase integration

)..) File Format GAvro, ar+$et and ot!er

format s$pportH

)..* 7DBS integration

)..- In4memor' data3ase integration9 Cac!ing

integration


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). #ig! Availa3ilit' 7esilienc')..( Can t!e s'stem avoid slowdown d$e to straggler node

)..) Fa$lt4?olerance Gdoes t!e tool !andlenode9operator9messaging fail$res wit!o$t catastrop!icall' failingH

)..* State recover' from in4memor'

)..- State recover' from relia3le storage

)..; Over!ead of fa$lt tolerance mec!anism GDoes failure!andlin" introduce additional latenc# or ne"ativel#

impact t!rou"!put&)

).. $lti4site s$pport Gm$lti4regionH

).. Flow control6 3ackpress$re tolerance from slow operators or

cons$mers

).. Fast parallel recover' vs. replication or serial recover' on

one node at a time


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). Ease of Development)..( SPN Interface

)..) 7eal4?ime de3$gging option)..* B$ilt4in stream oriented a3straction Gstreams, windows, operators , iterators

4 e/pressive AIs t!at ena3le programmers to +$ickl' develop streaming data

applicationsH

)..- Separation of application logic from fa$lt tolerance

)..; ?esting tools and framework

2.9.- 3!an"e mana"ement multiple model deplo#ment ( E.".

separate cluster or can one create multiple independent redundant

streams internall#)

2.9.8 D#namic model sappin" (Support d#namic updatin" of

operators/topolo"#/DA> it!out restart or service interruption)

2.9.9 Re?uired @noled"e of s#stem internals to develop anapplication

).. ?ime to market for applications

2.9.1= Supports plu"in of e4ternal libraries

2.9.11 A56


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). Scala3ilit'

)..( S$pports m$lti4t!read across m$ltiple

processors9cores

)..) Distri3$ted across m$ltiple mac!ines9servers

)..* artition Algorit!m)..- D'namic elasticit' 4 Scaling wit! minim$m impact9

performance penalt'

)..; #oriontal scaling wit! linear

performance9t!ro$g!p$t

).. %ertical scaling G:H

).. Scaling wit!o$t downtime

O f C


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*.( at$rit' Comm$nit' S$pport

*.) S$pport Nang$ages for Development

*.* Clo$d orta3ilit'

*.- Compati3ilit' wit! 0ative #adoop

Arc!itect$re

*.; Adoption of Comm$nit' vs. Enterprise

Edition

*. Integration wit! essage Brokers

* O i ti l fit C t i


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*.( at$rit' Comm$nit' S$pport

*.(.( Open So$rce S$pport *.(.) at$rit' G'earsH

*.(.* Sta3le

*.(.- Centralied doc$mentation wit! versioning

s$pport

*.(.; Documentation of pro"rammin" A56 it!

"ood code e4amples

'.1.- 3entralied visible roadmap

'.1.8 3ommunit# acceptance vs. 0endor

driven

*.(. Contri3$tors



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*.) S$pport Nang$ages for Development

*.).( Nang$age tec!nolog' was 3$ilt on

*.).) Nang$age s$pported to access

tec!nolog'



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*.* Clo$d orta3ilit'

*.*.( Ease of migration 3etween clo$d vendors

*.*.) Ease of migration 3etween on premise to clo$d

*.*.* Ease of migration from on premise to complete

clo$d services

*.*.- Clo$d compati3ilit' GA&S, :oogle, A$reH



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*.- Compati3ilit' wit! 0ative #adoop

Arc!itect$re

*.-.( Implement on top of #adoop A70 vs.

Standalone

*.-.) esos

*.-.* Coordination wit! Apac!e >ookeeper



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*.; Adoption of Comm$nit' vs. Enterprise

Edition

*.;.( Open So$rce

*.;.) Enterprise S$pport

- i ll 9Ot! C t i


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-. iscellaneo$s9Ot!er Categories

-.( Best S$ited for

-.) Qe' $se case scenarios

-.* Companies $sing tec!nolog'

- Fli k S k f k it i


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-. Flink vs. Spark on a few ke' criteria

(. Streaming Engine

). Iterative rocessing*. emor' anagement

-. Optimiation

;. Config$ration. ?$ning

. erformance

- ( St i E i


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-.(. Streaming Engine

an' time4critical applications need to process large

streams of live dataand provide res$lts in real4time.For e/ample6Financial Fra$d detectionFinancial Stock monitoringAnomal' detection

?raffic management applicationsatient monitoringOnline recommenders

Some claim t!at ;U of streaming $se cases can

3e !andled wit! micro43atc!esL" 7eall'LLL

- ( St i E i


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Spark=s micro43atc!ing isn=t good eno$g!L?ed D$nning, C!ief Applications Arc!itect at ap7,

talk at t!e Ba' Area Apac!e Flink eet$p on A$g$st), )8(;

!ttp699www.meet$p.com9Ba'4Area4Apac!e4Flink4eet$p9events9))-(;)-

9

?ed descri3ed several $se cases w!ere 3atc! and micro

3atc! processing is not appropriate and descri3ed w!'.#e also descri3ed w!at a tr$e streaming sol$tion needs

to provide for solving t!ese pro3lems.?!ese $se cases were taken fromreal ind$strial

sit$ations, 3$t t!e descriptions drove down to tec!nical

details as well.

- ( Streaming Engine
http://www.meetup.com/Bay-Area-Apache-Flink-Meetup/events/224189524/http://www.meetup.com/Bay-Area-Apache-Flink-Meetup/events/224189524/http://www.meetup.com/Bay-Area-Apache-Flink-Meetup/events/224189524/http://www.meetup.com/Bay-Area-Apache-Flink-Meetup/events/224189524/


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I wo$ld consider stream data anal'sis to 3e a ma2or

$ni+$e selling propositionfor Flink. D$e to its

pipelined arc!itect$re, Flink is a perfect matc! for 3ig

data stream processing in t!e Apac!e stack. %olker

arkl

7ef.6 On Apac!e Flink. Interview wit! %olker arkl, $ne )-t!)8(;

!ttp699www.od3ms.org93log9)8(;989on4apac!e4flink4interview4wit!4volker4markl9

Apac!e Flink $ses streams for all workloads6

streaming, SPN, micro43atc! and 3atc!. Batc! is 2$st

treated as a finite set of streamed data. ?!is makes

Flinkt!e most sop!isticated distri3$ted open so$rceBig Data processing engine Gnott!e most mat$reone

'etLH.

- ) Iterative rocessing
http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/http://www.odbms.org/blog/2015/06/on-apache-flink-interview-with-volker-markl/


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-.). Iterative rocessing

&!' Iterations" an' ac!ine Nearning and :rap!

processing algorit!ms need iterationsL For e/ample6

ac!ine Nearning Algorit!msCl$stering GQ4eans, Canop', KH:radient descent GNogistic 7egression, atri/

FactoriationH

:rap! rocessing Algorit!msage47ank, Nine47ankat! algorit!ms on grap!s Gs!ortest pat!s,

centralities, KH:rap! comm$nities 9 dense s$34componentsInference GBelief propagationH



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FlinkJs AI offers two dedicated iteration operations6

Iterateand Delta Iterate.

Flink e/ec$tes programs wit! iterationsas c'clicdata flows6 a data flow program Gand all its operatorsH

is sc!ed$led2$st once.In eac! iteration, t!e step f$nction cons$mest!e

entire inp$t Gt!e res$lt of t!e previo$s iteration, or t!einitial data setH, and comp$test!e ne/t version of t!e

partial sol$tion



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Delta iterations r$n onl' onparts of t!e datat!at is

c!anging and can significantl' speed $p man'

mac!ine learning andgrap! algorit!ms 3eca$se t!ework in eac! iteration decreases as t!e n$m3er of

iterations goes on.

Doc$mentation on iterations wit! Apac!e Flink!ttp699ci.apac!e.org9pro2ects9flink9flink4docs4master9apis9iterations.!tml

http://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.html


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StepStep

Step Step Step

3lient

for (int i = 0; i < m axIterations; i+ + ) {// Execute M apReduce job

0on4native iterations in #adoop and Sparkare

implemented as reg$lar for4loops o$tside t!e s'stem.



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Alt!o$g!Spark cac!es data across iterations, it still

needs to sc!ed$leand e/ec$tea new set of tasks foreac! iteration.

Spinning Fast Iterative Data Flows 4 Ewen et al. )8() 6

!ttp699vld3.org9pvld39vol;9p()Mstep!anewenMvld3)8().pdf ?!e

Apac!e Flink model for incremental iterative dataflow

processing. Academic paper.7ecap of t!e paper, $ne (, )8(;!ttp

6993log.acol'er.org9)8(;989(9spinning4fast4iterative4dataflows9

Doc$mentationon iterationswit! Apac!e Flink!ttp699ci.apac!e.org9pro2ects9flink9flink4docs4master9apis9iterations.!tml

- * emor' anagement
http://vldb.org/pvldb/vol5/p1268_stephanewen_vldb2012.pdfhttp://blog.acolyer.org/2015/06/18/spinning-fast-iterative-dataflows/http://blog.acolyer.org/2015/06/18/spinning-fast-iterative-dataflows/http://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://ci.apache.org/projects/flink/flink-docs-master/apis/iterations.htmlhttp://blog.acolyer.org/2015/06/18/spinning-fast-iterative-dataflows/http://blog.acolyer.org/2015/06/18/spinning-fast-iterative-dataflows/http://vldb.org/pvldb/vol5/p1268_stephanewen_vldb2012.pdf


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-.*. emor' anagement

P$estion6 Spark vs. Flink low memor' availa3le"

P$estion answered on stackoverflow.com!ttp699stackoverflow.com9+$estions9*(*;)9spark4vs4flink4low4memor'4

availa3le

?!e same +$estion still $nanswered on t!e Apac!e

Spark ailing NistLL !ttp699apac!e4flink4$ser4mailing4list4arc!ive.)**8;8.n-.na33le.com9spark4

vs4flink4low4memor'4availa3le4td)*-.!tml

- * emor' anagement
http://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-availablehttp://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-availablehttp://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-availablehttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://apache-flink-user-mailing-list-archive.2336050.n4.nabble.com/spark-vs-flink-low-memory-available-td2364.htmlhttp://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-availablehttp://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-availablehttp://stackoverflow.com/questions/31935299/spark-vs-flink-low-memory-available


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Feat$res6CVV st'le memor' management inside t!e %

ser data stored in serialied 3'te arra's in %emor' is allocated, de4allocated, and $sed strictl'

$sing an internal3$ffer pool implementation.

Advantages6

(. Flink will not t!row an OO e/ception on 'o$.). 7ed$ctionof :ar3age Collection G:CH

*. %er' efficient disk spilling and network transfers

-. 0o 0eed for r$ntime t$ning

;. ore relia3le and sta3le performance

- * emor' anagement


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pub!ic c!ass " # {pub!ic $trin%& ord;pub!ic intcount;empt

#pa"e

ool of emor' ages

Sorting,

!as!ing,

cac!ing

S!$ffles9

3roadcasts

ser code

o32ects

anagednman

aged

Flink contains its own memor' management stack.

?o do t!at, Flink contains its own t'pe e/traction

and serialiationcomponents.

% #eap

0

etwork

B$ffers

- * emor' anagement


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eeking into Apac!e FlinkJs Engine 7oom 4 3' Fa3ian

#Wske, arc! (*, )8(; !ttp

699flink.apac!e.org9news9)8(;98*9(*9peeking4into4Apac!e4Flinks4Engine47oom.!tml$ggling wit! Bits and B'tes 4 3' Fa3ian #Wske, a'

((,)8(;!ttps699flink.apac!e.org9news9)8(;98;9((9$ggling4wit!4Bits4and4B'tes.!tml

emor' anagement GBatc! AIH 3' Step!an Ewen4

a' (, )8(;!ttps699cwiki.apac!e.org9confl$ence9pages9viewpage.action"pageIdX

;*-(;);

Flink added anOff4#eap optionfor its memor'

management component in Flink 8.(86!ttps699iss$es.apac!e.org92ira93rowse9FNI0Q4(*)8

- * emor' anagement
http://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.htmlhttp://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.htmlhttps://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.htmlhttps://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525https://issues.apache.org/jira/browse/FLINK-1320https://issues.apache.org/jira/browse/FLINK-1320https://issues.apache.org/jira/browse/FLINK-1320https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525https://cwiki.apache.org/confluence/pages/viewpage.action?pageId=53741525https://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.htmlhttps://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.htmlhttps://flink.apache.org/news/2015/05/11/Juggling-with-Bits-and-Bytes.htmlhttp://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.htmlhttp://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.htmlhttp://flink.apache.org/news/2015/03/13/peeking-into-Apache-Flinks-Engine-Room.html


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Compared to Flink, Spark is still 3e!ind in c$stom

memor' management3$t is catc!ing $p wit! itspro2ect ?$ngstenfor emor' anagement and Binar'

rocessing:manage memor' e/plicitl' and eliminate

t!e over!ead of % o32ect model and gar3age

collection. April ), )8(-!ttps699data3ricks.com93log9)8(;98-9)9pro2ect4t$ngsten43ringing4spark4closer4to43are4metal.!tml

It seems t!at Spark is adopting somet!ing similar to

Flinkand t!e initial ?$ngsten anno$ncement read

almost like Flink doc$mentationLL

- - Optimiation
https://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.htmlhttps://databricks.com/blog/2015/04/28/project-tungsten-bringing-spark-closer-to-bare-metal.html


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-.- Optimiation

Apac!e Flink comes wit! an optimiert!at is

independent of t!e act$al programming interface.

It c!ooses a fitting e/ec$tion strateg' depending ont!e inp$ts and operations.

E/ample6 t!e oin operator will c!oose 3etween

partitioning and 3roadcasting t!e data, as well as

3etween r$nning a sort4merge42oin or a !'3rid !as!2oin algorit!m.

?!is !elps 'o$ foc$s on 'o$r application logic

rat!er t!an parallel e/ec$tion. P$ick introd$ction to t!e Optimier6 section of t!e

paper6


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52

-.- Optimiation

7$n locall' on a data

sample

on t!e laptop7$n a mont! later

after t!e data evolved

#as! vs. Sort

artition vs. Broadcast

Cac!ing

7e$sing partition9sortE/ec$tion

lan A

E/ec$tion

lan B

7$n on large files

on t!e cl$ster

E/ec$tion

lan C

&!at is A$tomatic Optimiation" ?!e s'stemJs 3$ilt4in

optimier takes care of finding t!e 3est wa' to

e/ec$te t!e program in an' environment.

- - Optimiation


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53

-.- Optimiation

In contrast to Flink=s 3$ilt4in a$tomatic optimiation,

Spark 2o3s !ave to 3e man$all' optimied and

adapted to specific datasets 3eca$se 'o$ need toman$all' control partitioning and cac!ing if 'o$

want to get it rig!t.Spark SPN $ses t!e Catal'st optimier t!at

s$pports 3ot! r$le43ased and cost43asedoptimiation. 7eferences6Spark SPN6 7elational Data rocessing in Spark

!ttp699people.csail.mit.ed$9matei9papers9)8(;9sigmodMsparkMs+l.pdf

Deep Dive into Spark SPN=s Catal'st Optimier!ttps699data3ricks.com93log9)8(;98-9(*9deep4dive4into4spark4s+ls4catal'st4

optimier.!tml

- ; Config$ration
http://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdfhttps://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.htmlhttps://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.htmlhttps://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.htmlhttps://databricks.com/blog/2015/04/13/deep-dive-into-spark-sqls-catalyst-optimizer.htmlhttp://people.csail.mit.edu/matei/papers/2015/sigmod_spark_sql.pdf


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-.;. Config$ration

Flinkre+$ires no memor' t!res!olds to

config$reFlink manages its own memor'Flinkre+$ires no complicated network

config$rations

ipelining engine re+$ires m$c! lessmemor' for data e/c!angeFlinkre+$ires no serialiers to 3e config$redFlink !andles its own t'pe e/traction and

data representation

- ?$ning


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-.. ?$ning

According to ike Olsen, C!ief Strateg' Officer of

Clo$deraInc. Spark is too kno33' 1 it !as too man't$ning parameters, and t!e' need constant

ad2$stment as workloads, data vol$mes, $ser co$nts

c!ange. 7eference6

!ttp699vision.clo$dera.com9one4platform9?$ning Spark Streaming for ?!ro$g!p$t B' :erard

aas from %irdata. Decem3er )), )8(- !ttp

699www.virdata.com9t$ning4spark9Spark ?$ning6

!ttp699spark.apac!e.org9docs9latest9t$ning.!tml

- ?$ning
http://vision.cloudera.com/one-platform/http://www.virdata.com/tuning-spark/http://www.virdata.com/tuning-spark/http://spark.apache.org/docs/latest/tuning.htmlhttp://spark.apache.org/docs/latest/tuning.htmlhttp://www.virdata.com/tuning-spark/http://www.virdata.com/tuning-spark/http://www.virdata.com/tuning-spark/http://vision.cloudera.com/one-platform/http://vision.cloudera.com/one-platform/


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56

-.. ?$ning

7$n locall' on a data

sample

on t!e laptop7$n a mont! later

after t!e data evolved

#as! vs. Sort

artition vs. Broadcast

Cac!ing

7e$sing partition9sortE/ec$tion

lan A

E/ec$tion

lan B

7$n on large files

on t!e cl$ster

E/ec$tion

lan C

&!at is A$tomatic Optimiation" ?!e s'stemJs 3$ilt4in

optimier takes care of finding t!e 3est wa' to

e/ec$te t!e program in an' environment.

erformance


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57

. erformance

&!' Flink provides a 3etter performance"C$stom memor' manager0ativeclosed4loop iteration operators makegrap!

and mac!ine learning applications r$n m$c! faster.7ole of t!e 3$ilt4in a$tomatic optimier. For

e/ample6 more efficient 2oin processing.

ipeliningdata to t!e ne/t operator in Flink is moreefficient t!an in Spark.

See 3enc!marking res$lts against Flink !ere6!ttp699www.slides!are.net9s3altagi9w!'4apac!e4flink4is4t!e4-g4of43ig4da

ta4anal'tics4frameworks9
http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87http://www.slideshare.net/sbaltagi/why-apache-flink-is-the-4g-of-big-data-analytics-frameworks/87


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Agenda


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59

Agenda



III. #ow Flink is $sed at Capital

One"




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&e started o$r 2o$rne' wit! Apac!e Flink at Capital

One w!ile researc!ing and contrasting stream

processing tools in t!e #adoop ecos'stem wit! apartic$lar interest in t!e ones providing real4time

stream processing capa3ilitiesand not 2$st micro4

3atc!ing as in Apac!e Spark.

&!ile learning more a3o$t Apac!e Flink, wediscovered some $ni+$e capa3ilities of Flink w!ic!

differentiate it from ot!er Big Data anal'tics tools not

onl' for 7eal4?ime streaming3$t also for Batc!

processing.&e eval$ated Apac!e Flink 7eal4?ime stream

processing capa3ilities in a OC.

III. #ow Apac!e Flink is $sed at Capital One"


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61

p p

&!ere are we in o$r Flink 2o$rne'"S$ccessf$l installation of Apac!e Flink 8. in o$r

re4rod$ction cl$ster r$nning on CD# ;.- wit!sec$rit' and #ig! Availa3ilit' ena3led.

S$ccessf$l installation of Apac!e Flink 8. in a (8

nodes 7D cl$ster r$nning #D.

S$ccessf$l completion of Flink OCfor real4timestream processing. ?!e OC proved t!at propriet'

s'stem can 3e replaced 3' a com3ination of tools6

Apac!e Qafka, Apac!e Flink,Elasticsearc! and

Qi3ana in addition to advanced real4time streaming

anal'tics.



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p p

&!at are t!e opport$nities for $sing Apac!e

Flinkat Capital One"(. 7eal4?ime streaming anal'tics

). Cascadingon Flink

*. Flink=s ap7ed$ce Compati3ilit' Na'er

-. Flink=s Storm Compati3ilit' Na'er

;. Ot!er Flink li3raries Gac!ine Nearning

and :rap! processingH once t!e' come

o$t of 3eta.



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p p

Cascading on Flink6 First release of Cascading on Flink was anno$nced

recentl' 3' Data Artisans and Conc$rrent. It will 3es$pported in $pcoming Cascading *.(. Capital One is t!e firstcompan' verif'ingt!is release

on real4world Cascading data flows wit! a simple

config$ration switc! andno code re4work neededL

?!is is a good e/ample ofdoing anal'tics on3o$ndeddata sets GCascadingH $singa stream processor GFlinkH

E/pected advantagesof performance 3oost and less

reso$rce cons$mption. F$t$re work is to s$pport


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p p

Flink=s compati3ilit' la'er for Storm6&e can e/ec$te e/isting Storm topologies

$sing Flink as t!e $nderl'ing engine.&e can re$seo$r application code G3oltsand

spo$tsH inside Flink programs.

Flink=s li3raries GFlinkNfor ac!ineNearning and :ell'for Narge scale grap!

processingH can 3e $sed along Flink=s

DataStream AI and DataSet AI for o$r end to

end 3ig data anal'tics needs.

Agenda


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Agenda





III. &!at are some ke' takeawa's"


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' '0eit!er Flink nor Spark will 3e t!e singleanal'tics

framework t!at will solve ever' Big Data pro3lemL

B' design, Spark is not for real4time stream processingw!ile Flinkprovides a tr$e low latenc' streaming

engine and advanced DataStream AI for real4time

streaming anal'tics.Alt!o$g! Spark is a!ead in pop$larit' and adoption,

Flinkis a!ead intec!nolog' innovation and is growingfast.

It is not alwa's t!e most innovative tool t!at gets t!e

largest market s!are, t!e Flink comm$nit' needs to

take into acco$nt t!e market d'namicsLBot! Sparkand Flinkwill !ave t!eir sweet spots

despite t!eir e too s'ndrome5.

?! k L


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?!anksL ?o all of 'o$ for attendingL ?o Capital One for giving me t!e

opport$nit' to meet wit! t!e growing

Apac!e Flink famil'. ?o t!e Apac!e Flink comm$nit' for t!e

great spirit of colla3oration and !elp.)8( will 3e t!e 'ear of Apac!e FlinkLSee 'o$ at FlinkForward )8(L

Flink vs Spark by Slim Baltagi 151016065205 Lva1 App6891

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Transcript of Flink vs Spark by Slim Baltagi 151016065205 Lva1 App6891