The accuracy of database statistics directly affects the optimization effect of SQL query plan. Statistics are metadata about the distribution of tables and index data, including the number of rows, the number of different values, data distribution and index selectivity, etc., for the optimizer to estimate the cost of execution paths and select the optimal plan. Inaccurate statistical information will lead to problems such as misselecting the full table scan, unused indexes, improper parallel planning, and unreasonable association order. Maintenance methods include: 1. Regular update of statistics; 2. Pay attention to high-frequency query columns; 3. Avoid over-update; 4. Use sampling to reduce overhead; 5. Pay attention to partition table configuration. Determining whether it is a statistical information problem can be achieved by checking the changes in the execution plan, index usage, result set estimation deviation and recent data changes. Mastering statistical information maintenance strategies can significantly improve query performance.

The impact of database statistics on SQL query plans is critical, especially when the optimizer decides how to execute queries. Many people will find that even if they write a seemingly reasonable SQL statement, the execution speed is ridiculously slow, and the problem is often due to inaccurate or missing statistics.

What is database statistics?

Simply put, the statistics of a database are metadata about the data distribution of tables and indexes. for example:

• How many rows are there in the table
• How many different values does a column have
• How is the data distributed (whether it is uniform, whether there is tilt)
• How selective is the index

This information is automatically collected by the database (also triggered manually) for use by the query optimizer. The optimizer estimates the cost of different execution paths based on these statistics, thereby selecting an "optimal" execution plan.

What problems will cause if the statistics are inaccurate?

When statistics are inaccurate, the optimizer may make incorrect judgments, resulting in a decrease in execution efficiency. Common phenomena include:

• Full table scans that should not be used have been selected
• Queries that should have been indexed have become nested loops and a large number of random IOs
• Parallel execution plan is not enabled, or is enabled but is slower
• The association order is unreasonable, the intermediate result set is too large

For example: Suppose an order table has millions of records, and only 10% of the data in a certain status field meets the query conditions. If the statistics show that the value distribution of this field is very even, the optimizer may not go through the index, but choose a full table scan. But if the actual data is unevenly distributed, it will affect performance.

How to maintain statistics?

Different database systems have slightly different aspects in this regard, but the basic ideas are consistent. Here are some general suggestions:

• Regular updates to statistics : especially large tables that change frequently. Some systems support automatic updates, but thresholds need to be properly configured.
• Pay attention to the objects involved in high-frequency queries : those columns that often appear in WHERE, JOIN, and GROUP BY, it is best to keep the statistics up to date.
• Avoid over-update : Frequent updates of statistics can consume resources, especially large tables. The strategy can be adjusted according to the frequency of data changes.
• Using sampling instead of full table scanning : Most databases allow setting the sampling rate. Proper sampling can reduce overhead while ensuring accuracy.
• Pay attention to the situation of partition table : The statistical information of partition table can be global or can be counted separately for each partition. Pay attention to the configuration method.

For example, in PostgreSQL, you can use the ANALYZE command to update statistics; in MySQL, you can use ANALYZE TABLE ; in Oracle, you can use the DBMS_STATS package to manage statistics.

How to determine whether it is a statistical information problem?

If you find that a query suddenly slows down and SQL itself has not changed, you can check it from the following aspects:

• Is there any significant change in the execution plan?
• Have you gone through a different index or been completely useless?
• Does the estimated number of rows in the intermediate result set have a large deviation?
• Have there been a lot of data changes recently? Are there any statistics updated?

Many databases provide the function of viewing execution plans, such as EXPLAIN or EXPLAIN ANALYZE . By comparing the estimated number of rows with the actual number of rows, you can roughly judge whether the statistical information is accurate.

Basically that's it. Mastering the maintenance rhythm of statistical information can often save a lot of complex tuning work.

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