Database Query Processing: Steps, Optimization, and Cost Models

Database Query Processing: Steps and Cost Estimation

Query processing in a Database Management System (DBMS) involves several steps to transform a high-level query (such as an SQL query) into an efficient execution plan. Understanding these stages is crucial for database performance tuning.

Key Stages of Query Processing

The process typically follows these four major steps:

1. Parsing and Translation
2. Query Optimization
3. Evaluation/Execution Plan Generation
4. Query Execution

1. Parsing and Translation

Objective: Convert the SQL query into an internal representation, usually a parse tree or Abstract Syntax Tree (AST).

• The SQL query is checked for syntactic correctness.
• The SQL query is parsed into a parse tree.

Cost Estimation Techniques for Query Optimization

Cost estimation in query processing is a critical component of query optimization. It involves predicting the resources required to execute different query execution plans, helping the DBMS choose the most efficient one.

Resource Metrics Used in Cost Estimation

The cost model typically evaluates the consumption of the following resources:

1. I/O Cost: The number of disk accesses required to read data pages from storage. This is typically the most significant cost factor.
2. CPU Cost: The computational effort needed for operations such as scanning, sorting, and joining data.
3. Memory Cost: The amount of memory required for operations, especially for large intermediate results during joins or sorts.

Statistics and Metadata

Accurate cost estimation relies heavily on up-to-date statistical information about the data:

• Table Statistics: Information like the number of rows, size of the table, and distribution of values in columns (histograms).
• Index Statistics: Details about the availability and efficiency of indexes, including the number of levels in the index tree and the selectivity of indexed columns.

Cost Model Components

These components use statistics to predict the outcome and resource usage of operations:

Selectivity Estimation

Predicts the fraction of rows that will satisfy a query predicate. For example, if a table has 1,000,000 rows and the predicate age > 20 is expected to match 10% of the rows, the selectivity is 0.1.

Cardinality Estimation

Estimates the number of rows in the intermediate results after applying query operations. This uses selectivity and table statistics to predict the size of data after each operation.