research-article

PerfXplain: debugging MapReduce job performance

Authors:

Nodira Khoussainova,

Magdalena Balazinska,

Dan SuciuAuthors Info & Claims

Proceedings of the VLDB Endowment, Volume 5, Issue 7

Pages 598 - 609

https://doi.org/10.14778/2180912.2180913

Published: 01 March 2012 Publication History

Abstract

While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naïve explanation-generation methods.

References

[1]

Amazon EC2. http://aws.amazon.com/ec2/.

[2]

Ganglia Monitoring System. http://www.ganglia.sourceforge.net.

[3]

MySQL Query Analyzer. http://www.mysql.com/products/enterprise/query.html.

[4]

PostgreSQL Tuning Wizard. http://pgfoundry.org/projects/pgtune.

[5]

Tuning Your PostgreSQL Server. http://wiki.postgresql.org/wiki/Tuning_Your_PostgreSQL_Server.

[6]

S. Agrawal, S. Chaudhuri, L. Kollar, A. Marathe, V. Narasayya, and M. Syamala. Database Tuning Advisor for Microsoft SQL Server demo. In Proc. of the SIGMOD Conf., pages 930--932, 2005.

Digital Library

[7]

S. Agrawal, S. Chaudhuri, L. Kollár, A. P. Marathe, V. R. Narasayya, and M. Syamala. Database Tuning Advisor for Microsoft SQL Server 2005. In Proc. of the 30th VLDB Conf., pages 1110--1121, 2004.

[8]

S. Agrawal, S. Chaudhuri, and V. R. Narasayya. Automated Selection of Materialized Views and Indexes in SQL Databases. In Proc. of the 26th VLDB Conf., pages 496--505, 2000.

Digital Library

[9]

ASTERIX: A Highly Scalable Parallel Platform for Semi-structured Data Management and Analysis. http://asterix.ics.uci.edu/.

[10]

S. Babu. Towards Automatic Optimization of MapReduce Programs. In Proc. of the 1st ACM symposium on Cloud computing (SOCC), pages 137--142, 2010.

Digital Library

[11]

D. Becker and P. A. Barsch. Strike it Rich: Application Tuning Helps Companies Save Money through Query Optimization. Teradata magazine online. http://www.teradata.com/tdmo/v07n04/FactsAndFun/Services/StrikeItRich.aspx.

[12]

N. Borisov, S. Uttamchandani, R. Routray, and A. Singh. Why Did My Query Slow Down? In Proc. of the Fourth CIDR Conf., 2009.

[13]

R. Chaiken, B. Jenkins, P.-A. Larson, B. Ramsey, D. Shakib, S. Weaver, and J. Zhou. SCOPE: Easy and Efficient Parallel Processing of Massive Data Sets. In Proc. of the 34th VLDB Conf., pages 1265--1276, 2008.

Digital Library

[14]

C. Chambers, A. Raniwala, F. Perry, S. Adams, R. R. Henry, R. Bradshaw, and N. Weizenbaum. FlumeJava: Easy, Efficient Data-parallel Pipelines. In PLDI'10: Proceedings of the 2010 ACM SIGPLAN conference on Programming language design and implementation, pages 363--375, 2010.

Digital Library

[15]

S. Chaudhuri and V. Narasayya. AutoAdmin What-if Index Analysis Utility. SIGMOD Rec., 27:367--378, June 1998.

Digital Library

[16]

S. Chaudhuri and V. R. Narasayya. An Efficient Cost-Driven Index Selection Tool for Microsoft SQL Server. In Proc. of the 23rd VLDB Conf., pages 146--155, 1997.

Digital Library

[17]

S. Chaudhuri and V. R. Narasayya. Self-Tuning Database Systems: A Decade of Progress. In Proc. of the 33rd VLDB Conf., pages 3--14, 2007.

Digital Library

[18]

B. Dageville, D. Das, K. Dias, K. Yagoub, M. Zaït, and M. Ziauddin. Automatic SQL Tuning in Oracle 10g. In Proc. of the 30th VLDB Conf., pages 1098--1109, 2004.

Digital Library

[19]

J. Dean and S. Ghemawat. MapReduce: Simplified Data Processing on Large Clusters. In Proc. of the 6th OSDI Symp., pages 137--149, 2004.

Digital Library

[20]

J. Dittrich, J.-A. Quiané-Ruiz, A. Jindal, Y. Kargin, V. Setty, and J. Schad. Hadoop++: Making a Yellow Elephant Run Like a Cheetah (Without It Even Noticing). Proc. VLDB Endow., 3:515--529, September 2010.

Digital Library

[21]

S. Duan, S. Babu, and K. Munagala. Fa: A System for Automating Failure Diagnosis. Proc. of the 25th ICDE Conf., pages 1012--1023, 2009.

Digital Library

[22]

S. Duan, V. Thummala, and S. Babu. Tuning Database Configuration Parameters with iTuned. PVLDB, 2(1):1246--1257, 2009.

Digital Library

[23]

My Excite. http://www.excite.com/.

[24]

A. Ganapathi, Y. Chen, A. Fox, R. H. Katz, and D. A. Patterson. Statistics-driven Workload Modeling for the Cloud. In SMDB, pages 87--92, 2010.

[25]

A. Ganapathi, K. Datta, A. Fox, and D. Patterson. A Case for Machine Learning to Optimize Multicore Performance. In HotPar, 2009.

Digital Library

[26]

A. Ganapathi, H. Kuno, U. Dayal, J. Wiener, A. Fox, M. Jordan, and D. Patterson. Predicting Multiple Performance Metrics for Queries: Better Decisions Enabled by Machine Learning. In Proc. of the 25th ICDE Conf., pages 592--603, 2009.

Digital Library

[27]

Greenplum Database. http://www.greenplum.com/.

[28]

Hadoop. http://hadoop.apache.org/.

[29]

H. Herodotou and S. Babu. Xplus: a SQL-Tuning-Aware Query Optimizer. Proc. VLDB Endow., 3:1149--1160, 2010.

Digital Library

[30]

H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, and S. Babu. Starfish: A Self-tuning System for Big Data Analytics. In Proc. of the Fifth CIDR Conf., 2011.

[31]

Hive. http://hadoop.apache.org/hive/.

[32]

IBM. DB2 Performance Tuning using the DB2 Configuration Advisor. http://www.ibm.com/developerworks/data/library/techarticle/dm-0605shastry/index.html.

[33]

M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly. Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks. In Proc. of the European Conference on Computer Systems (EuroSys), pages 59--72, 2007.

Digital Library

[34]

E. Jahani, M. Cafarella, and C. Re. Automatic Optimization for MapReduce Programs. PVLDB, 4(6):385--396, 2011.

Digital Library

[35]

D. Jiang, B. C. Ooi, L. Shi, and S. Wu. The Performance of MapReduce: An In-depth Study. PVLDB, 3:472--483, 2010.

Digital Library

[36]

MATLAB. version 7.10.0 (R2010a). The MathWorks Inc., 2010.

[37]

K. Morton, M. Balazinska, and D. Grossman. ParaTimer: A Progress Indicator for MapReduce DAGs. In Proc. of the SIGMOD Conf., pages 507--518, 2010.

Digital Library

[38]

K. Morton, A. Friesen, M. Balazinska, and D. Grossman. Estimating the Progress of MapReduce Pipelines. In Proc. of the 26th ICDE Conf., pages 681--684, 2010.

[39]

Netezza, inc. http://www.netezza.com/.

[40]

C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins. Pig Latin: a Not-So-Foreign Language for Data Processing. In Proc. of the SIGMOD Conf., pages 1099--1110, 2008.

Digital Library

[41]

R. Pike, S. Dorward, R. Griesemer, and S. Quinlan. Interpreting the Data: Parallel Analysis with Sawzall. Scientific Programming, 13(4), 2005.

Digital Library

[42]

J. R. Quinlan. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.

Digital Library

[43]

R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2011.

[44]

M. Robnik-Sikonja and I. Kononenko. An Adaptation of Relief for Attribute Estimation in Regression. In D. H. Fisher, editor, Fourteenth International Conference on Machine Learning, pages 296--304. Morgan Kaufmann, 1997.

Digital Library

[45]

J. Schad, J. Dittrich, and J.-A. Quiané-Ruiz. Runtime Measurements in the Cloud: Observing, Analyzing, and Reducing Variance. PVLDB, 3:460--471, 2010.

Digital Library

[46]

StratoSphere: Above the Clouds. http://www.stratosphere.eu/.

[47]

Teradata, Inc. http://www.teradata.com/.

[48]

V. Thummala and S. Babu. iTuned: a Tool for Configuring and Visualizing Database Parameters. In Proc. of the SIGMOD Conf., pages 1231--1234, 2010.

Digital Library

[49]

T. White. Hadoop: The Definitive Guide. MapReduce for the Cloud. O'Reilly Media, 2009.

Digital Library

[50]

I. H. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Second edition, 2005.

Digital Library

[51]

W. Xu, L. Huang, A. Fox, D. Patterson, and M. Jordan. Online System Problem Detection by Mining Patterns of Console Logs. In ICDM '09: Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, pages 588--597, 2009.

Digital Library

[52]

Y. Yu, M. Isard, D. Fetterly, M. Budiu, U. Erlingsson, P. K. Gunda, and J. Currey. DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language. In Proc. of the 8th OSDI Symp., pages 1--14, 2008.

Digital Library

Cited By

Currim SSnodgrass RSuh Y(2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3636425
Li YLee B(2022)Phronesis: Efficient Performance Modeling for High-dimensional Configuration TuningACM Transactions on Architecture and Code Optimization10.1145/354686819:4(1-26)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1145/3546868
Narasayya VChaudhuri SIves ZBonifati AEl Abbadi A(2022)Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and OpportunitiesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522566(2465-2473)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3522566
Show More Cited By

Index Terms

PerfXplain: debugging MapReduce job performance
1. Information systems
  1. Data management systems
    1. Database management system engines

Index terms have been assigned to the content through auto-classification.

Recommendations

Scheduling of deteriorating jobs with release dates to minimize the maximum lateness

In this paper, we consider the problem of scheduling n deteriorating jobs with release dates on a single (batching) machine. Each job's processing time is a simple linear function of its starting time. The objective is to minimize the maximum lateness. ...
Primary-secondary bicriteria scheduling on identical machines to minimize the total completion time of all jobs and the maximum T-time of all machines

In this paper, we study a new primary-secondary bicriteria scheduling problem on identical machines. The primary objective is to minimize the total completion time of all jobs and the secondary objective is to minimize the maximum T-time of all machines,...
Preemptive Hadoop Jobs Scheduling under a Deadline
SKG '12: Proceedings of the 2012 Eighth International Conference on Semantics, Knowledge and Grids

MapReduce has become the dominant programming model in a cloud-based data processing environment, such as Hadoop. First In First Out (FIFO) is the default job scheduling policy of Hadoop, but it cannot guarantee that the job will be completed by a ...

Comments

Information & Contributors

Information

Published In

cover image Proceedings of the VLDB Endowment

Proceedings of the VLDB Endowment Volume 5, Issue 7

March 2012

94 pages

ISSN:2150-8097

Issue’s Table of Contents

Publisher

VLDB Endowment

Publication History

Published: 01 March 2012

Published in PVLDB Volume 5, Issue 7

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

31
Total Citations
View Citations
299
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to

Other Metrics

View Author Metrics

Citations

Cited By

Currim SSnodgrass RSuh Y(2024)Identifying the Root Causes of DBMS SuboptimalityACM Transactions on Database Systems10.1145/363642549:1(1-40)Online publication date: 28-Feb-2024
https://dl.acm.org/doi/10.1145/3636425
Li YLee B(2022)Phronesis: Efficient Performance Modeling for High-dimensional Configuration TuningACM Transactions on Architecture and Code Optimization10.1145/354686819:4(1-26)Online publication date: 16-Sep-2022
https://dl.acm.org/doi/10.1145/3546868
Narasayya VChaudhuri SIves ZBonifati AEl Abbadi A(2022)Multi-Tenant Cloud Data Services: State-of-the-Art, Challenges and OpportunitiesProceedings of the 2022 International Conference on Management of Data10.1145/3514221.3522566(2465-2473)Online publication date: 10-Jun-2022
https://dl.acm.org/doi/10.1145/3514221.3522566
Glasbergen BAbebe MDaudjee KLevi A(2020)SentinelProceedings of the VLDB Endowment10.14778/3407790.340785613:12(2720-2733)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407856
Ma MYin ZZhang SWang SZheng CJiang XHu HLuo CLi YQiu NLi FChen CPei D(2020)Diagnosing root causes of intermittent slow queries in cloud databasesProceedings of the VLDB Endowment10.14778/3389133.338913613:8(1176-1189)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.14778/3389133.3389136
Savva FAnagnostopoulos CTriantafillou PKolomvatsos K(2020)Large-scale Data Exploration Using Explanatory Regression FunctionsACM Transactions on Knowledge Discovery from Data10.1145/341044814:6(1-33)Online publication date: 28-Sep-2020
https://dl.acm.org/doi/10.1145/3410448
Abuzaid FKraft PSuri SGan EXu EShenoy AAnanthanarayan ASheu JMeijer EWu XNaughton JBailis PZaharia M(2020)DIFF: a relational interface for large-scale data explanationThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00633-630:1(45-70)Online publication date: 30-Sep-2020
https://dl.acm.org/doi/10.1007/s00778-020-00633-6
Wang XMeliou A(2019)Explain3DProceedings of the VLDB Endowment10.14778/3317315.331732012:7(779-792)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.14778/3317315.3317320
Teoh JGulzar MXu GKim M(2019)PerfDebugProceedings of the ACM Symposium on Cloud Computing10.1145/3357223.3362727(465-476)Online publication date: 20-Nov-2019
https://dl.acm.org/doi/10.1145/3357223.3362727
Kalmegh PBabu SRoy SBoncz PManegold SAilamaki ADeshpande AKraska T(2019)iQCARProceedings of the 2019 International Conference on Management of Data10.1145/3299869.3319904(918-935)Online publication date: 25-Jun-2019
https://dl.acm.org/doi/10.1145/3299869.3319904
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Issue’s Table of Contents