evaluation of cloudera impala 1.1

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Evaluation of Cloudera impala 1.1

Aug 7, 2013CELLANT Corp. R&D Strategy Division

Yukinori SUDA@sudabon

Copyright © CELLANT Corp. All Rights Reserved. h t t p : / / w w w . c e l l a n t . j p /

v  Sentry support:l  Fine-‐‑‒grained authorizationl  Role-‐‑‒based authorization

v  Support for viewsv  Performance improvements

l  Parquet columnar performancel  More efficient metadata refresh for larger installations

v  Additional SQLl  SQL-‐‑‒89 joins (in addition to existing SQL-‐‑‒92)l  LOAD functionl  REFRESH command for JDBC/ODBC

v  Improved Hbase support:l  Binary typesl  Caching configuration

v  Fixed many bugs

Cloudera Impala 1.1 was released !!

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v Hive ⇒ Impalal On Impala shell, can read data in “VIEW” that was created via Hive command ?

v Impala ⇒ Hivel On Hive shell, can read data in “VIEW” that was created via Impala command ?

v ResultTwo “VIEW”s have compatibility

Check compatibility of “VIEW”

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Check performance (Hive on Cluster1)

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0 50 100 150 200 250

No Comp.

Gzip

Snappy

Gzip

Snappy

TextFile

SequenceFile

RCFile

222.039

244.67

239.182

228.801

230.327

Avg. Job Latency [sec]

This result will be invalid as performance evaluation cause some data may be read remotely. See the slide of “Check performance (Hive on Cluster2)”.


Check performance (Impala on Cluster1)

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0 50 100 150 200 250

No Comp.

Gzip

Snappy

Gzip

Snappy

Snappy

Text

File

Sequence

File

RCFile

Parquet

File

23.518

32.155

28.617

20.774

12.654

13.146


This result will be invalid as performance evaluation cause some data may be read remotely. See the slide of “Check performance (Impala on Cluster2)”.


Check performance (Hive on Cluster2)

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0 50 100 150 200 250 300

No Comp.

Gzip

Snappy

Gzip

Snappy

TextFile

SequenceFile

RCFile

272.176

249.531

245.009

230.034

216.802



Check performance (Impala on Cluster2)

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0 50 100 150 200 250 300

No Comp.

Gzip

Snappy

Gzip

Snappy

Snappy

Text

File

Sequence

File

RCFile

Parquet

File

32.528

28.73

21.173

24.794

14.308

19.814



v IMPALA-‐‑‒357l Insert into Parquet exceed mem-‐‑‒limit

v Probleml Even if set mem_̲limit setting, when create ParquetFile table with partitions, consumed memory isnʼ’t limited.

l At last, Impalad crashes due to memory shortage

v ResultCREATE command failed due to memory limit

Check fixed bug

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v Thanks to dev. team, Impala is also going from “Good to Great”

v Both “VIEW” and “Parquet” are already readyv Performance

v RCFile+Snappy is the fastest on both Cluster1 and Cluster2

v If use larger size table, Parquet+Snappy may be the fastest

v Hope for future extensionl Support Structure Typesl Support UDF/UDTF, etc

Summary

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Appendix. Benchmark Details


Our System Environment(Cluster1)

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v  Install using Cloudera Manager Free Edition 4.6.0

Master Slave

14 Servers

All servers are connected with 1Gbps Ethernet through an L2 switch

ActiveNameNode

DataNodeTaskTrackerImpalad

Stand-‐‑‒byNameNode

JobTrackerstatestored

3 Servers










DataNode

DataNode

DataNode

DataNode


Our System Environment(Cluster2)

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v  Install using Cloudera Manager Free Edition 4.6.0

Master Slave

10 Servers

All servers are connected with 1Gbps Ethernet through an L2 switch

ActiveNameNode


Stand-‐‑‒byNameNode

JobTrackerstatestored

3 Servers










DataNode

DataNode

DataNode

DataNode

Decommissioned


v CPUl Intel Core 2 Duo 2.13 GHz with Hyper Threading

v Memoryl 8GB : Namenodes onlyl 4GB : Others

v Diskl 7,200 rpm SATA mechanical Hard Disk Drive * 1

v OSl Cent OS 6.3

Our Server Specification

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v  Use CDH4.3.0 + Impala 1.1v  Use hivebench in open-‐‑‒sourced benchmark tool “HiBench”

l  https://github.com/hibenchv  Modified datasets to 1/10 scale

l  Default configuration generates table with 1 billion rowsv  Modified query sentence

l  Deleted “INSERT INTO TABLE …” to evaluate read-‐‑‒only performancev  Combines a few storage format with a few compression method

l  TextFile, SequenceFile, RCFile, ParquestFilel  No compression, Gzip, Snappy

v  Comparison with job query latencyv  Average job latency over 5 measurementsv  Benchmark on both Cluster1 and Cluster2

Benchmark

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•  Uservisits table–  100 million rows–  16,895 MB as TextFile–  Table Definitions

•  sourceIP string•  destURL string•  visitDate string•  adRevenue double•  userAgent string•  countryCode string•  languageCode string•  searchWord string•  duration int

•  Rankings table–  12 million rows–  744 MB as TextFile–  Table Definitions

•  pageURL string•  pageRank int•  avgDuration int

Modified Datasets

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SELECT 　sourceIP, 　sum(adRevenue) as totalRevenue, 　avg(pageRank) FROM 　rankings_̲t RJOIN [BROADCAST] ( 　SELECT 　　sourceIP, 　　destURL, 　　adRevenue 　FROM 　　uservisits_̲t UV 　WHERE 　　(datediff(UV.visitDate, '1999-‐‑‒01-‐‑‒01')>=0 　　AND 　　datediff(UV.visitDate, '2000-‐‑‒01-‐‑‒01')<=0) 　) NUV

ON 　(R.pageURL = NUV.destURL)group by sourceIPorder by totalRevenue DESClimit 1;

Modified Query

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evaluation of cloudera impala 1.1

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opensourced benchmark

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