Performance prediction for concurrent database workloads

Jennie Duggan*, Ugur Cetintemel, Olga Papaemmanouil, Eli Upfal

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

114 Scopus citations


Current trends in data management systems, such as cloud and multi-tenant databases, are leading to data processing environments that concurrently execute heterogeneous query workloads. At the same time, these systems need to satisfy diverse performance expectations. In these newly-emerging settings, avoiding potential Quality-of-Service (QoS) violations heavily relies on performance predictability, i.e., the ability to estimate the impact of concurrent query execution on the performance of individual queries in a continuously evolving workload. This paper presents a modeling approach to estimate the impact of concurrency on query performance for analytical workloads. Our solution relies on the analysis of query behavior in isolation, pairwise query interactions and sampling techniques to predict resource contention under various query mixes and concurrency levels. We introduce a simple yet powerful metric that accurately captures the joint effects of disk and memory contention on query performance in a single value. We also discuss predicting the execution behavior of a time-varying query workload through query-interaction timelines, i.e., a fine-grained estimation of the time segments during which discrete mixes will be executed concurrently. Our experimental evaluation on top of PostgreSQL/TPC-H demonstrates that our models can provide query latency predictions within approximately 20% of the actual values in the average case.

Original languageEnglish (US)
Title of host publicationProceedings of SIGMOD 2011 and PODS 2011
PublisherAssociation for Computing Machinery
Number of pages12
ISBN (Print)9781450306614
StatePublished - 2011
Event2011 ACM SIGMOD and 30th PODS 2011 Conference - Athens, Greece
Duration: Jun 12 2011Jun 16 2011

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


Other2011 ACM SIGMOD and 30th PODS 2011 Conference


  • concurrency
  • query performance prediction

ASJC Scopus subject areas

  • Software
  • Information Systems


Dive into the research topics of 'Performance prediction for concurrent database workloads'. Together they form a unique fingerprint.

Cite this