What is Cost-Based Optimizer (CBO)

The cost-based optimizer (CBO) in a database is a critical component responsible for devising the most efficient method, known as the execution plan, to execute SQL queries. The CBO employs a variety of strategies and factors to derive the optimal execution plan, ensuring the database performs at its best during query processing.

Principles of Efficient Execution Plans

To create efficient execution plans, the CBO relies heavily on the most up-to-date statistical information. This includes data about the tables, indexes, and other database structures used in the query. By evaluating different possibilities, the CBO calculates the 'cost'—a measure of efficiency—for each plan, ultimately selecting the one with the lowest cost.

Role of Indexes and Data Volume

In the cost-based optimization process, information such as indexes and data volume play a significant role. The optimizer uses indexes to speed up data retrieval, and the data volume provides context for how much data the execution plan needs to process. A deep understanding of these elements can help the CBO make more effective decisions when constructing an execution plan.

Statistical Information in CBO

The CBO needs a variety of statistical information to function effectively. Examples of such statistics include column data types, the volume of data, the number of records, the indexes and constraints present on the table, and the cardinality, which indicates the level of variance in the data.

Column Data Type, Data Volume, and Record Counts

The column data type, data volume, and record counts are important statistical information that contributes to the construction of an efficient execution plan. For instance, knowing the data type of a column can help the optimizer choose the appropriate operation, while understanding the data volume and record counts can help it estimate the cost of reading and processing the data.

Indexes, Constraints, and Cardinality

Indexes and constraints are crucial elements that influence the access paths the optimizer might choose for a query. For example, an optimizer can use an index to speed up data access, while constraints can influence the optimizer's choice of join order. Additionally, the cardinality, or the data's distinctness, also plays a critical role in determining the cost of different operations.

Collection of Statistical Information

Automatic vs. Manual Collection

Statistical information for tables is often collected automatically by the database system. This automated process ensures that the optimizer has the most up-to-date statistics to make informed decisions when formulating the best execution plan for a query.

However, there may be instances where the automatic collection process does not reflect the most recent changes in the data, such as when there is a sudden surge in data volume in a table. In such cases, manual collection of statistics becomes necessary.

Situations for Manual Collection

A significant increase in data volume or changes in data distribution may require you to manually gather statistics. Manually updating statistics ensures the optimizer can effectively devise efficient execution plans based on the latest state of your data.

MySQL and PostgreSQL: Commands for Collecting and Viewing Statistics

In both MySQL and PostgreSQL, you can use specific commands to collect and view statistical information. The commands can vary depending on the database.

Command to Collect Statistical Information

In MySQL, you can manually collect table statistics using the ANALYZE TABLE command. Here's an example:

ANALYZE TABLE your_table_name;

This command updates key distribution for a specified table. As an example output, you may see something like:

+----------------+---------+----------+----------+
| Table          | Op      | Msg_type | Msg_text |
+----------------+---------+----------+----------+
| your_database.your_table_name | analyze | status   | OK       |
+----------------+---------+----------+----------+

In PostgreSQL, you can use the ANALYZE command for the same purpose:

ANALYZE your_table_name;

This command doesn't return an output to the user but updates the statistics of the table in the system catalogs.

Command to View Table Statistical Information

In MySQL, you can view the statistical information of a table using the SHOW TABLE STATUS command:

SHOW TABLE STATUS LIKE 'your_table_name';

This will display a row of information for the specified table, including the number of rows, data length, index length, and more.

In PostgreSQL, you can use the following query to view the statistical information of a table:

SELECT * FROM pg_stat_user_tables WHERE relname = 'your_table_name';

This will display a row of information for the specified table, including sequential scan and index scan counts, number of rows inserted, updated, deleted, and more.

Command to View Index Statistical Information

In MySQL, you can view index statistics with the SHOW INDEX command:

SHOW INDEX FROM your_table_name;

This command returns a list of all indexes for the specified table along with their characteristics.

In PostgreSQL, you can view index statistics with the following command:

SELECT * FROM pg_stat_user_indexes WHERE relname = 'your_table_name';

This will provide you with various details about the indexes of the specified table, including number of index scans, rows read, and rows fetched.

Execution Plans

Execution plans are detailed roadmaps that SQL databases use to retrieve data. They describe the specific steps and order of operations the database will use to execute a SQL query. The cost-based optimizer is responsible for generating these plans.

Use of EXPLAIN SQL Statements

The EXPLAIN SQL statement is a powerful tool to understand how a SQL query will be executed without actually running the query. By prefixing a SQL query with EXPLAIN, the database will return a representation of the execution plan it would use to run the query. This can be very helpful for troubleshooting performance issues, as it allows you to see what steps the database would take without having to execute potentially slow-running queries.

EXPLAIN SELECT * FROM your_table;

The output will show the steps involved in the execution of the query, such as whether a full table scan or an index scan would be used.

Use of EXPLAIN ANALYZE Statements

EXPLAIN ANALYZE is a variant of the EXPLAIN command that executes the query and provides additional execution statistics, such as the execution time and the actual number of rows processed at each step of the query. This command can be very useful for determining the actual cost of a query.

EXPLAIN ANALYZE SELECT * FROM your_table;

The output will include the planned operations along with actual execution time and rows processed.

Interpreting Execution Plans

• Cardinality
  Cardinality refers to the number of unique values in a dataset or column. In an execution plan, cardinality can give an idea of how many rows each operation would retrieve. High cardinality means there's a large number of unique values, which can lead to more efficient index usage.

• Execution Time (Actual Time)
  In an execution plan resulting from an EXPLAIN ANALYZE command, you can see the actual time it took to execute each step of the query. This information can help pinpoint bottlenecks in the execution plan.

• Access Methods
  The execution plan will also detail the methods the database will use to access the data. This could be a full table scan, where the database reads through all the rows in a table, or an index scan, where the database uses an index to quickly locate the needed rows.

• Join Methods and Order
  If a query involves joining multiple tables together, the execution plan will detail the join method (such as hash, sort-merge, or nested loop) and the order in which the tables are joined. The choice of join method and order can have a significant impact on the efficiency of the query.

• Partitioning
  If a table is partitioned, the execution plan may show whether and how the query makes use of this partitioning. Partitioning can often improve the performance of a query by reducing the amount of data that needs to be scanned.