Cardinality

Enhanced Definition

In the context of IBM mainframe databases like **DB2 for z/OS** or **IMS**, cardinality refers to the number of rows (or segments/occurrences for IMS) in a table or the number of distinct values in a specific column. It is a crucial statistical metric used by the database optimizer to determine the most efficient access paths for queries.

Key Characteristics

- Statistical Metric: Cardinality is a statistical value collected by database utilities (e.g., RUNSTATS in DB2) and stored in the system catalog or directory.
- Optimizer Input: It serves as a primary input for the DB2 Optimizer (or IMS's access path selection logic) to estimate the cost of different data retrieval strategies, such as using an index versus performing a table scan.
- Dynamic Nature: Cardinality changes as data is inserted, updated, or deleted within tables, necessitating periodic updates to statistics to maintain optimal query performance.
- Column vs. Table: It can refer to the total number of rows in a table (table cardinality) or the number of unique values within a specific column (column cardinality).
- Impact on Joins: High cardinality columns, particularly when used in join predicates, significantly influence the performance of join operations between large tables.

Use Cases

- Query Optimization: The DB2 Optimizer uses column and table cardinality to select the most efficient access path for SQL queries, deciding whether to use an index, perform a table space scan, or employ a specific join method.
- Index Design: Understanding column cardinality is vital for designing effective indexes. Columns with high cardinality (many unique values) are generally good candidates for indexing to improve search and filter performance.
- Performance Tuning: Database Administrators (DBAs) analyze cardinality statistics to identify potential performance bottlenecks in SQL queries or to pinpoint tables that might benefit from new or modified indexes.
- Data Distribution Analysis: DBAs utilize cardinality alongside other statistics (e.g., HIGH2KEY, LOW2KEY, NUMCOLS, histograms) to understand data distribution and skew, which directly impacts query execution plans.

Related Concepts

DB2 Optimizer: Cardinality is fundamental to the DB2 Optimizer, which relies heavily on these statistics to generate optimal access plans for SQL statements. Inaccurate cardinality can lead the optimizer to choose inefficient data access paths.
RUNSTATS Utility: The RUNSTATS utility in DB2 is responsible for collecting and updating cardinality and other statistical information in the DB2 catalog. Regular execution of RUNSTATS is critical for maintaining good query performance.
Indexes: The effectiveness of an index is often directly tied to the cardinality of the indexed column(s). Columns with high cardinality generally yield greater performance benefits from indexing for equality searches.
Access Paths: Cardinality directly influences the chosen access path (e.g., TABLESPACE SCAN, INDEX SCAN, INDEX ACCESS) for SQL queries, impacting I/O and CPU consumption.

Best Practices:

Regular RUNSTATS Execution: Execute RUNSTATS regularly on tables and indexes, especially after significant data changes (e.g., large data loads, mass updates, deletions) to ensure the DB2 Optimizer has up-to-date statistics.
Monitor Statistics: Periodically review cardinality statistics for critical tables and columns to detect data skew or unexpected changes that could negatively impact query performance.
Utilize Appropriate RUNSTATS Options: Use specific RUNSTATS options (e.g., TABLESPACE, INDEX, COLUMN, HISTORY, SAMPLE) to collect the necessary level of detail for the optimizer, including histograms for skewed data.
Index High Cardinality Columns: For columns frequently used in WHERE clauses, JOIN conditions, or ORDER BY clauses, consider creating indexes if they exhibit high cardinality to improve query performance.
Understand Data Skew: Be aware that cardinality alone doesn't always tell the complete story; data skew (uneven distribution of values) can also significantly impact performance. Use RUNSTATS with HISTOGRAM options to capture and inform the optimizer about data skew.