postgresql partitioning performance

PostgreSQL CPU Full (100%) performance analysis and optimizationTransferred from: https://help.aliyun.com/knowledge_detail/43562.htmlIn database operations, a DBA often encounters a more urgent problem, that is, the sudden CPU full (CPU utilization reaches 100%), causing the business to stall. When the CPU is full, it is often necessary to start from the backend database, go back to the specific SQL, and fi

This is a test that was done a long time ago, recently in the collation of the PostgreSQL data related to the test, so also took it out to share with you.First explain the so-called PostgreSQL space performance, mainly based on the postgis of spatial data import performance, detailed PostGIS knowledge please Baidu, the

for a standalone Pgsql server, such as 4G of memory, can be set to 3.5G (437500)Maintence_work_mem: The memory defined here is only used when create INDEX, vacuum, etc., so the frequency is not high, but often these instructions consume more resources, so the instructions should be quickly executed as soon as possible: to MAINTENCE_WORK_MEM large memory, such as 512M ( 524288)max_connections: Typically, the purpose of max_connections is to prevent max_connections * work_mem from exceeding the a

the maximum cache that PostgreSQL can use, and this number should be large enough for a standalone Pgsql server, such as 4G of memory, which can be set to 3.5G (437500)MAINTENCE_WORK_MEM: The memory defined here is only used when create INDEX, vacuum, etc., so the frequency used is not high, but often these instructions consume more resources, so the instructions should be quickly executed as soon as possible: to Maintence_ Work_mem large memory, suc

Tags: Plpgsql associate an ace off index function preFirst, the problem descriptionWhen the number of records for a table in PostgreSQL recently increased from million to 1 million (design capability is 100 million), the query performance of a multi-table associated view built on top of the table was dramatically slower (from about 10ms to 100s). After analyzing the query plan, it is found that the bottlene

PostgreSQL supports hstore to store data such as Key->value, which is similar to the array or JSON type. Efficient indexing is essential to the efficient use of this type of data. Let's take a look at the performance issues of two different types of indexes for the same retrieval request today.If we have such an original table, there is a Btree index based on the Str1 field.t_girl=# \d Status_check;