Editing PostgreSQL (section)

== Benchmarks and performance ==
{{Update|date=April 2024|reason=Performance information based on soft- and hardware of 9 years ago is basically useless|part=section|updated=2015-06-01}}
Many informal performance studies of PostgreSQL have been done.<ref name="Berkus" /> Performance improvements aimed at improving scalability began heavily with version 8.1. Simple benchmarks between version 8.0 and version 8.4 showed that the latter was more than ten times faster on read-only workloads and at least 7.5 times faster on both read and write workloads.<ref name="Vilmos" />

The first industry-standard and peer-validated benchmark was completed in June 2007, using the Sun Java System Application Server (proprietary version of [[GlassFish]]) 9.0 Platform Edition, [[UltraSPARC T1]]-based [[Sun Fire]] server and PostgreSQL 8.2.<ref name="SPECJ" /> This result of 778.14 SPECjAppServer2004 JOPS@Standard compares favourably with the 874 JOPS@Standard with Oracle 10 on an [[Itanium]]-based [[HP-UX]] system.<ref name="Berkus" />

In August 2007, Sun submitted an improved benchmark score of 813.73 SPECjAppServer2004 JOPS@Standard. With the [[system under test]] at a reduced price, the price/performance improved from $84.98/JOPS to $70.57/JOPS.<ref name="SPECjAppServer2004" />

The default configuration of PostgreSQL uses only a small amount of dedicated memory for performance-critical purposes such as caching database blocks and sorting. This limitation is primarily because older operating systems required kernel changes to allow allocating large blocks of [[shared memory]].<ref name="Kernel Resources" /> PostgreSQL.org provides advice on basic recommended performance practice in a [[wiki]].<ref name="pg9hiperf" />

In April 2012, Robert Haas of [[EnterpriseDB]] demonstrated PostgreSQL 9.2's linear CPU scalability using a server with 64 cores.<ref name="Haas" />

Matloob Khushi performed benchmarking between PostgreSQL 9.0 and MySQL 5.6.15 for their ability to process genomic data. In his performance analysis he found that PostgreSQL extracts overlapping genomic regions eight times faster than MySQL using two datasets of 80,000 each forming random human DNA regions. Insertion and data uploads in PostgreSQL were also better, although general searching ability of both databases was almost equivalent.<ref>{{cite journal |first=Matloob |last=Khushi |pmid=25560631 |title=Benchmarking database performance for genomic data |journal=J Cell Biochem |date=June 2015 |volume=116 |issue=6 |doi=10.1002/jcb.25049 |pages=877–83|arxiv=2008.06835 |s2cid=27458866 }}</ref>