Database Performance Tuning
Database performance tuning identifies and resolves bottlenecks that slow queries and increase resource consumption. It covers query optimization, indexing strategies, configuration adjustment, schema redesign, and monitoring techniques that keep response times within acceptable bounds.
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Intro
Database Performance Tuning
Database performance tuning is the controlled process of reducing database work and delay for a defined workload. You measure first, change one cause, and measure again.
The target is not “a fast database.” The target is a service objective such as a response-time percentile, batch completion time, throughput, or resource limit.
Start with the workload
A database serves many kinds of work at once. One slow report, thousands of small lookups, and a write-heavy import can stress different resources.
Begin with a representative time window. Record the application operation, query pattern, execution count, latency distribution, rows processed, and resource use. Include the parameter values and concurrency that matter.
Total workload impact matters more than one dramatic duration. A query taking two seconds once may cost less than a query taking ten milliseconds a million times.
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Sources
- https://docs.oracle.com/en/database/oracle/oracle-database/26/tdppt/using-oracle-performance-method.html
Supports
- Performance tuning is iterative and targets bottlenecks and high-load SQL
- Tuning includes proactive monitoring and reactive investigation
- https://docs.oracle.com/en/database/oracle/oracle-database/26/tgdba/performance-improvement-methods.html
Supports
- Objective tuning connects symptoms, bottlenecks, proposed remedies, and validation
- Isolating one change preserves evidence about its effect
- Tuning repeats until the objective is met or constrained
- https://docs.oracle.com/en/database/oracle/oracle-database/26/tgsql/query-optimizer-concepts.html
Supports
- A cost-based optimizer estimates candidate plans and selects a plan
- Execution plans contain row-producing and row-processing operations
- Cardinality estimates and statistics affect access, join, and cost choices
- Optimizer cost is an internal comparison measure rather than elapsed time
- https://www.postgresql.org/docs/current/using-explain.html
Supports
- EXPLAIN exposes plan nodes, cost estimates, and estimated rows
- Measured execution exposes actual rows, loops, buffers, and operation details
- Measured execution has overhead and does not include every client-side cost
- Toy data and different scale can produce different plans
- https://www.postgresql.org/docs/current/sql-analyze.html
Supports
- ANALYZE gathers statistics used for plan selection
- Large-table statistics are sampled and approximate
- Statistics should be refreshed after substantial data change when automation does not cover it
- https://dev.mysql.com/doc/refman/8.4/en/optimization-indexes.html
Supports
- Indexes can reduce row lookup work for suitable predicates
- Unnecessary indexes consume space and add insert, update, and delete cost
- https://dev.mysql.com/doc/refman/8.4/en/using-explain.html
Supports
- EXPLAIN shows optimizer plan information, access, and join order
- Table statistics can affect optimizer choices
- MySQL supports plan inspection for multiple statement types
- https://dev.mysql.com/doc/refman/8.4/en/performance-schema-summary-tables.html
Supports
- Performance Schema aggregates statement and wait events over time
- Summary tables cover input/output, locks, memory, transactions, and errors
- Aggregated evidence can be grouped by statement digest and other dimensions
- https://learn.microsoft.com/en-us/sql/relational-databases/performance/monitoring-performance-by-using-the-query-store?view=sql-server-ver17
Supports
- Query Store retains execution plans, runtime statistics, and wait statistics
- Historical data supports finding high-impact queries and plan regressions
- Plan changes can follow statistics, schema, and index changes