postgresql vs mariadb performance

First, the use of explain: PostgreSQL generates a query plan for each query because the performance impact of selecting the correct query path is critical. PostgreSQL itself already contains a planner for finding the optimal plan, and we can view the query plan generated by the planner for each query by using the explain command.The query plan generated in

PostgreSQL Performance Optimization considerations caused by connection to IPVs that consumes cpu resources in particular. bitsCN.com Because it is in the development stage, the postgres parameter is not configured, and the default configuration during installation is used,In the past, nothing was abnormal, but my cpu usage suddenly increased a few days ago.Check the process and find that the CPU usage of o

Tips for improving PostgreSQL Performance In a (poor) PostgreSQL query, only a small change is required (ANY (ARRAY [...]). to ANY (VALUES (...))) the query time can be reduced from 20 s to 0.2 s. From the simplest use of explain analyze, to the use of EXPLAIN s community, there will be a hundred times of learning time investment to return. Use ipvs to monitor sl

statements to reduce the cost of committing a transaction;Use cluster when extracting multiple records from an index;Use limit when extracting part of a record from a query result;Use pre-compiled queries (Prepared query);Use analyze to maintain accurate optimization statistics;Regular use of VACUUM or pg_autovacuumDelete indexes first when making large amounts of data changes (then rebuilding indexes)2, program ExperienceCheck the program, whether the use of connection pool, if not used, as so

PostgreSQL Performance Monitoring ToolsHttps://github.com/CloudServer/postgresql-perf-toolsThis package includes three useful scripts aimed to help to pinpoint performance issues on systems with PostgreSQL as data Base backend.All Scritps is written in Python. Requirements:

PostgreSQL supports hstore to store data such as Key->value, which is in fact similar to 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 had such an original table. There is a Btree index based on the Str1 field.t_girl=# \d Status_check;

PostgreSQL provides some functions to help improve performance. There are several main aspects. 1. Use the explain command to view the execution plan. In the previous blog PostgreSQL provides some functions to help improve performance. There are several main aspects. 1. Run the EXPLAIN command to view the execution pla

1. Use EXPLAIN:PostgreSQL generates a query plan for each query, because selecting the correct query path has a critical impact on performance. PostgreSQL itself contains a scheduler for optimal planning. We can use the EXPLAIN command to view the scheduler's query plan generated for each query.The query plan generated by PostgreSQL is a planning tree consisting

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

In a (bad) PostgreSQL query, just a little to change (any (array[...)) to any (VALUES (...)) will be able to reduce the query time from 20s to 0.2s. From the simplest learning to use EXPLAIN analyze, to learn to use the Postgres community a lot of learning time input will have a hundredfold time to return. Using Postgres to monitor slow Postgres queries Earlier this week, a primary key query for the small table (10gb,1500) on our graphics editor had

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

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;