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What is the Importance of Database Statistics in Query Optimization?


The dynamics of database management prioritize efficient data retrieval as a key performance index. Herein lies the significance of database statistics, which provide a snapshot of database content like row counts in a table or value distributions within a column. The database engine employs these statistics to construct the most proficient strategy to carry out queries, a process known as Query Optimization.

The Process of Query Optimization and the Impact of Database Statistics

Query optimization is a procedure used by the database management system (DBMS) to identify the most resource-efficient way to execute a SQL query by considering a range of possible query plans. Upon receiving a SQL query, the DBMS does not initiate execution immediately; it first analyzes the query to generate an execution plan. This plan details the best method to retrieve the necessary data.

Database statistics are vital for this decision-making process. For instance, the DBMS might need to choose between performing a table scan or using an index to retrieve the data requested by the query. The choice will largely depend on database statistics. If the statistics indicate that the table is vast, and the query column’s index is highly selective, then the DBMS will likely choose to use the index.

Generating Database Statistics

Database statistics are primarily generated via a technique known as sampling, where a fraction of the data is inspected to calculate statistics. The DBMS uses these statistics to infer details about the entire dataset. The update frequency for these statistics can vary depending on the DBMS and the data’s volatility. In certain scenarios, updates may be scheduled periodically, whereas, in others, they might be updated in real time as data changes.

For example, in PostgreSQL, you can compute statistics with the ANALYZE command:

ANALYZE table_name;

Application of Database Statistics

Database statistics enable the DBMS’s query optimizer to make well-informed decisions. Consider this example: You have a large table called “Orders” containing a million rows and a smaller table “Customers” with a hundred rows. You need to execute the following query:

SELECT * FROM Orders o
INNER JOIN Customers c ON c.CustomerID = o.CustomerID
WHERE c.CustomerName = 'John Doe'

The DBMS, in this case, has to decide whether to scan the “Orders” table and subsequently look for matching “Customers” or vice versa. Without statistics, it’s challenging for the DBMS to make an optimal decision. However, equipped with statistics, the DBMS is aware of the number of rows in each table, so it can choose to scan the smaller “Customers” table first, thus reducing the volume of data processed.


Database statistics are pivotal in the process of query optimization. They equip the DBMS’s query optimizer with invaluable metadata about the database’s content, enabling it to make knowledgeable decisions about the best way to carry out queries. Keeping these statistics updated is a critical factor in maintaining a DBMS’s query performance.