tpc-h analytics' scenarios and performances on hadoop data clouds

TPC-H ANALTYCS’ SCENARIOSTPC H ANALTYCS SCENARIOS

AND PERFORMANCES ON

HADOOP DATA CLOUDS

RIM [email protected] –UNIV OF TUNISLATICE –UNIV. OF TUNISTUNISIA

4th International. Conference on Networked Digital TechnologiesNDT’2012, Dubai, UAE.24th, April 2012

OUTLINELaTICE

Cloud A l tiAnalytics

1. Business Intelligence2. Motivation

data managment issues, NoSQL, cloudsg , ,

OLAP in the cloud

3. Implementation of OLAP in the cloud3. Implementation of OLAP in the cloudTPC-H Benchmark

Analytics Scenariosy

Performance Measurements

4. Related Work4. Related Work5. Conclusion6 W k

NDT’2012. Dubai. UAE 224th, Apr. 2012

6. Future Work

BUSINESS INTELLIGENCELATICE

Cloud A l ti

BIMotivationTPC‐H & COLAPPerformanceConclusionAnalytics Future work

Business intelligence aims to support better business g ppdecision-making. Common functions of business intelligence technologies areCommon functions of business intelligence technologies are

On-Line Analytical Processing,

data mining process mining data mining, process mining, Business performance managementT t i i d di ti l ti Text mining and predictive analytics, …

Market shareGartner Research Reports BI Market Revenue Hit $12.2 Billion in 2011


MOTIVATIONLATICE

Cloud A l ti

BIMotivation|—Issues|— NoSQL|—CloudAnalytics ||—COLAP

Decision Support Systems• I t D t & l kl d• Incessant Data & complex workload• Complex DB schema

Scalability Issues• Ideally, Linear Speed up & Linear Scale up• DBMS do not scale linearlyy• OLAP Technologies do not scale

Hardware Hardware • I/O Bottleneck I/O-bound data storage systems• Gilder law: Thrice bandwidth every 3 years

M L T d 18 h • Moore Law: Twice computing and storage capacities every 18 months. Obsolete by 2017

• Vertical scaling cost >> Horizontal scaling cost


NOSQLLATICE

Cloud A l ti

BIMotivation|—Issues|— NoSQL|—CloudAnalytics

Bi Ch ll l t d t l it

||—COLAP

Big Challenges related to velocity How fast huge volumes of data can be processed?

N SQL l tiNoSQL solutionsAdopted by Google, Facebook, Amazon, …

Dynamic horizontal scale upDynamic horizontal scale-up

Nodes are added without bringing the cluster down

Shared-nothing architectureShared nothing architecture

Independent computing& storage nodes interconnected via a high speed network

Distributed programming framework: MapReduce (Google)


CLOUD COMPUTINGLATICE

Cloud A l ti


Cloud computing is a style of computing where scalable and elastic IT-bl d biliti id d " i " t t l t

||—COLAP

enabled capabilities are provided as a service to external customers using Internet technologies.

Broad network accessBroad network access

Resource pooling (virtualization)

Self-provisioningScalableAnalyticsp g

Rapid elasticity

Measured service

Analytics

Market shareForrester Research expects the global cloud computing market to reach $241 billion in 2020. In particular, SaaS market growing to $92.8 billion by 2016.

Gartner group expects the cloud computing market will reach $US150.1 billion, with a compound annual rate of 26 5% in 2013


with a compound annual rate of 26.5%, in 2013.

OLAP IN THE CLOUDLATICE

Cloud A l ti


OLAP constraints

||—COLAP

Big data analytics’ obstaclesCurrent systems & technologies do not scale

Cloud computing

Key benefits of Cloud ComputingPerformance

NoSQLBig data analytics

Much faster data analysis,Dynamic and up-to-date hardware infrastructure,y p ,

More Economical Organizations no longer need to expend capital upfront for Organizations no longer need to expend capital upfront for hardware and software purchasesServices are provided on a pay-per-use basis,


p p y p ,

TPC-HDECISION SUPPORT SYSTEM BENCHMARK

LATICE

Cloud A l ti

BIMotivationTPC‐H COLAPPerformanceDECISION-SUPPORT SYSTEM BENCHMARKAnalytics

DATA

Conclusion

• Complex DB schema• Scale factor 1, 10, …, 100,000 correspond respectively

to 1GB 10 GB 100 TBto 1GB, 10 GB, …, 100 TB• 8 data files {lineitem, customer…, region}.tbl• broad industry-wide relevancey

22 l ld b

WORKLOAD

• 22 real world business questions• High degree of complexity

• Star queries (complex joins)Star queries (complex joins)• Grouping• Nested queries


TPC-H BENCHMARKLATICE

Cloud A l ti

BIMotivationTPC‐H |— E/R schemaCOLAPAnalytics Performance


HADOOP/PIG LATINLATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translationPerformance

APACHE HADOOP

Analytics Conclusion

• Framework for running applications on large clusters of dit h d

APACHE HADOOP

commodity hardware. • Implements computational framework MapReduce• HDFS: (hadoop distributed file system) stores data on the ( p y )

compute nodes • Replication & job resoumissions for failures’ handling

APACHE PIG LATIN

• high-level language for expressing data analysis programs (filter, projection, join, group, sort, union, …)


PIG LATIN BENCHMARK5 TRANSLATION HINTS

LATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translation|—nominal 5 TRANSLATION HINTSAnalytics

1. Load Data for Immediate

|scenario

Processing• Better memory managementy g

2. Minimum Relation Scan• Conjunction/disjunction of Conjunction/disjunction of

predicates applied once

3 Unary operations prior to 3. Unary operations prior to binary operations• U ti ( j ti • Unary operations (projection,

restriction, ) reduce data volume


PIG LATIN BENCHMARK5 TRANSLATION HINTS CTND

LATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translation|—nominal 5 TRANSLATION HINTS -CTNDAnalytics

4. Intra-operation parallelism

|scenario

p p• partitioned join

5 Join Algorithm5. Join Algorithm• Algorithm

• h h j i • hash join, • merge join,

• S i j i d i• Star-queries: joins ordering


NOMINAL ANALYTICAL SCENARIOLATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translation|—nominal Analytics |

scenario

Pig Latin Script (Business Question)

Response


NOMINAL ANALYTICAL SCENARIOLATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translation|—nominal Analytics

High Cost

|scenario

• Measured Service, pay as you consume cloud ressources (bandwitdh, CPU, RAM)

Performance Issues• The same query (with same or different parameters) is

executed several times with no optimization

Di ti it f S iDiscontinuity of Service• Network failure/congestion


COMPLEX!!LATICE

Cloud A l ti

TPC‐H COLAP|— hadoop/pig|— translation|—nominal Analytics

How to reduce service cost?

|scenario

How to reduce service cost? How to improve performances?How to prevent discontinuity of service?

Cost• Materialized

views?• Aggregated data • Exploit

i i

CostManagement

Aggregated data replication

• OLAP or not?

W kl d S d

organizationhardware resources?

• Cloud Right size?Workload Studyfor Tuning

Cloud Right size?


TPC-H WORKLOAD NUMERICAL STUDYTYPE A

LATICE

Cloud A l ti

COLAP|— …|—nominal

scenario|—towardsTYPE AAnalytics

*Order Priority Checking*

|better scenario

135dimdim

alwaysmeasureorderscountpriorityorderdateorder ××

OLAP!+export to olap server


+export to olap server+MV

TPC-H WORKLOAD NUMERICAL STUDYTYPE B

LATICE

Cloud A l ti


scenario|—towardsTYPE BAnalytics |

better scenario

*Large Volume Orders*

dim × measureqtylinesumorder

130083000,500,1

dim

=>

×

×

∑ SFforqtylinehaveordersofppmalmostSF

measureqtylinesumorder

1,3008.3 =>∑ SFforqtylinehaveordersofppmalmost

Not OLAP!


+MV

TPC-H WORKLOAD NUMERICAL STUDYTYPE C

LATICE

Cloud A l ti


scenario|—towardsTYPE CAnalytics

*Minimum Cost Supplier*

|better scenario

000,000,000,2

costsupplymindimpartdimsupplier2 ×

××

SF

measureOLAP!MV storage cost


,,,MV storage costbest supplier in each region for each part!

TPC-H WORKLOAD NUMERICALSTUDY

LATICE

Cloud A l ti


scenario|—towardsSTUDYAnalytics

Type Features TPC-H Business Questions

|better scenario

(OLAP Cube)

A • Medium dimensionality Q1, Q3, Q4, Q5, Q6, Q7, • Result is TPC-H Scale Factor

independentQ8, Q12, Q13, Q14, Q16, Q19, Q2213 business questions13 business questions

B • High dimensionality• few results, lots of empty

Q15, Q182 business questionsfew results, lots of empty

cellsq

C • High dimensionality Q2, Q9, Q10, Q11, Q17, g y• Result % of Scale Factor

, , , , ,Q20, Q217 business questions


CLOUDCOST MANAGEMENT

LATICE

Cloud A l ti


scenario|—towardsCOST MANAGEMENTAnalytics

Measured Service

|better scenario

Measured Service pay as you goCPU + Memory + BandwidthCPU + Memory + Bandwidth

“When users understand the relationship between cost and consumption everybody wins” –Ron Millerconsumption, everybody wins –Ron MillerEmerging need to understand, manage and

ti l t l t th l dproactively control costs across the cloudResource Utilization MonitoringRight size w.r.t. both performances & cost (client and provider)

Green cloud through energy saving


BETTER SCENARIOLATICE

Cloud A l ti

COLAP|— …|—better scenarioPerformanceRelated workAnalytics Conclusion

Pig Latin Script (Generalized Business

Question)Question)

Pre-aggregated DataInteraction

OLAP Client Import Data into an on-siteinto an on-site

OLAP server


PERFORMANCE MEASUREMENTSLATICE

Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusion Analytics Future work

K

• French GRID platform: a large H • TPC H S • Apache

G5K

p gscale nation wide infrastructure for Grid research.

• Bordeaux Site TPC

-H • TPC-H Benchmark

• SF=1• 1 1GB source /H

DFS • Apache

Hadoop 0.20• N=3, 5 or 8 • one Hadoop• Bordeaux Site

• Borderel: 24GB RAM, 4 AMD CPUs, 2.27 GHz,

/

T 1.1GB source files

• 4.5GB single big file Pi

g/

one HadoopMaster

• (2, 4 or 7) Workers

and 4cores/CPU.• Borderline: :

32GB RAM, 4 Intel Xeon CPUs,

g• SF=10

• 11GB source files

• Apache Pig 0.8.1

e eo C Us, 2.6 GHz, and 2 cores/CPU.

• Ethernet10Gbps

• 45GB single big file



Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusion Analytics

Original TPC H 1GB

Original TPC H 11GB

Big FileBig File 4 5GB

Big File 45GB

Original 1.1 11GB

Future work

TPC-H 1GB TPC-H 11GBg

4.5GB 45GBvs 11GB

Except business questions which do not perform join operations: No improvementwhen cluster size doubles



Cloud A l ti


Original TPC H 1GB

Original TPC H 11GB


Big File 45GB

Original 1.1 11GB

Future work


4.5GB 45GBvs 11GB

Improvement when cluster size doublesMore data so it’s nice to have more storage & computing nodes



Cloud A l ti


Original TPC H 1GB

Original TPC H 11GB


Big File 45GB

Original 1.1 11GB

Future work


4.5GB 45GBvs 11GB

Elapsed times for SF=10 (11GB) are

At maximum 5 times elapsed times obtained for SF=1 (1.1GB)

In average twice elapsed times obtained for SF=1 (1.1GB)



Cloud A l ti


Original TPC H 1GB

Original TPC H 11GB


Big File 45GB

Original 1.1 11GB

Future work

Joining partitionned files is complex!


4.5GB 45GBvs 11GB

Combine all files into one fileSF=1 4.5GBSF 1 4.5GBSF=10 45GB

Evaluation of Pig/MR without joinsDenormalization

j i tsaves join costincreases required storage space (≈ ×4 for TPC-H)



Cloud A l ti


Original TPC H 1GB

Original TPC H 11GB


Big File 45GB

Original 1.1 11GB

Future work


4.5GB 45GBvs 11GB

Compared to (SF=1, 1.1GB), improvements range from 10% to 80%,p ( , ), p g ,performance degradation with more than 4 workers (N=5): this is due to MR framework (before reduce phase, data is grouped and sorted which has a costwhen involving more storage and computing nodes)


when involving more storage and computing nodes)


Cloud A l ti


Original TPC H 1GB

Original TPC H 11GB


Big File 45GB

Original 1.1 11GB

Future work


4.5GB 45GBvs 11GB

Compared to (SF=10, 11GB), after affording more nodes we obtain similar resultsp ( , ), gCompared to (SF=1, 4.5GB), elapsed times are less than 10× for same cluster sizePerformance degradation for queries which do not perform joins

Q1 executes over 7GB lineitem file (SF=10,11GB), now it executes over 45GB file


Q1 executes over 7GB lineitem file (SF 10,11GB), now it executes over 45GB file


Cloud A l ti



Big File 45GB

OLAP OLAP 4.5GB OLAP 45GB

Future work

g4.5GB 45GB

Aggregated data TPC-H business questions type A (SF independent & small resultset)TPC-H business questions type A (SF independent & small resultset)TPC-H business questions type B (very very small resultset)15 business questions from 22

Tradeoff between space & computation TPC-H business questions type C Add derived fieldsAdd derived fields

Q2: check (true) minimum supplycost by supplier for a part in PartSuppQ17: average_line_quantity field for each partQ20: sum_lines_quantities_per_year for each supplierQ21: number of waiting orders for each supplier

7 business questions from 22


7 business questions from 22


Cloud A l ti



Big File 45GB


Future work

g4.5GB 45GB

Average degradation is 5%done once + time to retreive data



Cloud A l ti



Big File 45GB


Future work

g4.5GB 45GB

Average degradation is 60%done once + time to retreive data


RELATED WORKLATICE

Cloud A l ti


Implementation of relational operations using MR framework

Future work

Implementation of relational operations using MR frameworkKim et al. –MRBench, 2008

Nominal analytics scenarioNominal analytics scenario

TPC-H benchmarking for SF=1,3

Translation from SQL to Pig LatinTranslation from SQL to Pig LatinIu et al. –Hadoop to SQL, 2010

Lee et al. –Ysmart, 2011 ,

Other pig latin use cases

Shatzle et al, RDF data, 2011

Loebman etal. Astrophysical data, 2009


CONCLUSIONLATICE

Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusionAnalytics

TPC H in depth numerical study

Future work

TPC-H in-depth numerical studyOLAP in the cloud

ScenariosImplementation p

Pig / Hadoop Distributed File SystemPerformancesPerformances

Various cluster sizes Various data volumesVarious schemas (with and without joins)


( j )

FUTURE WORKAQP

LATICE

Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusion AQPAnalytics

Approximate Query Processing in clouds

Future work

pp y gMost Distributed File Systems implement replication for high availabilityfor high availabilityMDS erasure codes outperform replication from two perspectives (i) storage cost and (ii) minimal perspectives (i) storage cost and (ii) minimal operation cost of redundant data management

N H d l New Hadoop release Facebook

Generalized framework for approximate data analytics in the cloud coping with nodes’ failure


FUTURE WORKPIG LATIN++

LATICE

Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusion PIG LATIN++Analytics

Most of TPC-H business questions scripts are composed of

Future work

jobs, which execute sequentially,some branches of the DAG are unnecessarily y

blocked! namely Q1 Q3 Q4 Q9 Q10 Q11 Q12 Q13 namely Q1, Q3, Q4, Q9, Q10, Q11, Q12, Q13, Q14, Q16, Q17 and Q18

Pig Latin EnhancementsPig Latin EnhancementsInvestigate intra-operation Parallelism for better

f f Sperformances of Pig Scripts Investigate better job definitions strategies, in order


to increase inter-job parallelism

FUTURE WORKPIG LATIN++⟩⟩ Q16 EXPLE

LATICE

Cloud A l ti

TPC‐HCOLAPPerformanceRelated workConclusion PIG LATIN++⟩⟩ Q16 EXPLEAnalytics

Q16’s DAG Q16’s pig

Future work

J_0005

script

J_0004

J_0003

J_0002

J 0001


J_0001

TPC-H ANALTYCS SCENARIOS AND PERFORMANCES ON HADOOP DATACLOUDS

THANK YOU FOR YOUR ATTENTION

Q & AQ & A

24th, Apr. 2012, p

4th International. Conference on Networked Digital Technologies

NDT’12.Dubai. UAE

tpc-h analytics' scenarios and performances on hadoop data clouds

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