Core optimization techniques:
1. Indexing: Indexes are crucial for speeding up data retrieval by allowing the database to find rows without scanning the entire table.
Create indexes on the right columns: Add indexes to columns used in WHERE, JOIN, and ORDER BY clauses.
Use the right index type:
B-Tree: The default type, best for general purpose equality and range queries.
GIN: Ideal for indexing composite values like arrays, JSONB, and full-text search.
BRIN: Effective for very large, sequentially ordered tables, such as time-series data.
Be strategic with composite indexes: The order of columns matters for multi-column indexes. Place columns with equality conditions first, followed by range conditions.
Index-only scans: For read-heavy queries, a covering index that includes all required columns can be used to retrieve data directly from the index without accessing the table.
2. Query rewriting and design: Sometimes, a small change to your SQL can lead to a more efficient execution plan.
Avoid SELECT *: Only select the columns you need. This reduces disk I/O, memory usage, and network traffic.
Optimize WHERE clauses: Avoid wrapping an indexed column in a function, as this can prevent the use of an index.
Instead of LIKE '%searchterm', use full-text search or pg_trgm for faster partial text matching.
Refine join strategies: Ensure columns used in JOIN conditions are indexed. Prefer INNER JOINs over OUTER JOINs when possible.
Limit your result set: Use LIMIT to restrict the number of rows returned, which is especially important for pagination. For large datasets, key-set pagination (e.g., WHERE column > last_seen_value) is more efficient than using a large OFFSET.
Use Materialized Views: For complex, resource-intensive queries that are run frequently, a materialized view can pre-calculate and store the result. You then query the view for much faster results.
3. Database maintenance: Regular maintenance keeps the database's internal statistics accurate and prevents performance degradation.
VACUUM and ANALYZE: VACUUM reclaims storage space occupied by "dead" rows.
ANALYZE updates table and index statistics, which is crucial for the query planner.
The autovacuum daemon handles this automatically but may need tuning for high-write workloads.
Table partitioning: For very large tables, partitioning divides the data into smaller, more manageable pieces. This allows queries to scan only the relevant partitions.
4. Server configuration tuning: Adjusting PostgreSQL configuration parameters can have a significant impact on overall performance.
shared_buffers: Controls the amount of memory dedicated to caching data. A common recommendation is 25-40% of your system's total RAM.
work_mem: Sets the memory used for operations like sorts and hash joins. If this is too low, the database will write temporary data to disk, which is slower. This is set per operation, so be mindful of concurrency.
effective_cache_size: An estimate of the total available memory for caching, which helps the planner decide on using an index scan vs. a sequential scan.
Connection pooling: For high-concurrency applications, use a tool like PgBouncer to manage connections efficiently, reducing the overhead of creating new connections.
1. Indexing: Indexes are crucial for speeding up data retrieval by allowing the database to find rows without scanning the entire table.
Create indexes on the right columns: Add indexes to columns used in WHERE, JOIN, and ORDER BY clauses.
Use the right index type:
B-Tree: The default type, best for general purpose equality and range queries.
GIN: Ideal for indexing composite values like arrays, JSONB, and full-text search.
BRIN: Effective for very large, sequentially ordered tables, such as time-series data.
Be strategic with composite indexes: The order of columns matters for multi-column indexes. Place columns with equality conditions first, followed by range conditions.
Index-only scans: For read-heavy queries, a covering index that includes all required columns can be used to retrieve data directly from the index without accessing the table.
2. Query rewriting and design: Sometimes, a small change to your SQL can lead to a more efficient execution plan.
Avoid SELECT *: Only select the columns you need. This reduces disk I/O, memory usage, and network traffic.
Optimize WHERE clauses: Avoid wrapping an indexed column in a function, as this can prevent the use of an index.
Instead of LIKE '%searchterm', use full-text search or pg_trgm for faster partial text matching.
Refine join strategies: Ensure columns used in JOIN conditions are indexed. Prefer INNER JOINs over OUTER JOINs when possible.
Limit your result set: Use LIMIT to restrict the number of rows returned, which is especially important for pagination. For large datasets, key-set pagination (e.g., WHERE column > last_seen_value) is more efficient than using a large OFFSET.
Use Materialized Views: For complex, resource-intensive queries that are run frequently, a materialized view can pre-calculate and store the result. You then query the view for much faster results.
3. Database maintenance: Regular maintenance keeps the database's internal statistics accurate and prevents performance degradation.
VACUUM and ANALYZE: VACUUM reclaims storage space occupied by "dead" rows.
ANALYZE updates table and index statistics, which is crucial for the query planner.
The autovacuum daemon handles this automatically but may need tuning for high-write workloads.
Table partitioning: For very large tables, partitioning divides the data into smaller, more manageable pieces. This allows queries to scan only the relevant partitions.
4. Server configuration tuning: Adjusting PostgreSQL configuration parameters can have a significant impact on overall performance.
shared_buffers: Controls the amount of memory dedicated to caching data. A common recommendation is 25-40% of your system's total RAM.
work_mem: Sets the memory used for operations like sorts and hash joins. If this is too low, the database will write temporary data to disk, which is slower. This is set per operation, so be mindful of concurrency.
effective_cache_size: An estimate of the total available memory for caching, which helps the planner decide on using an index scan vs. a sequential scan.
Connection pooling: For high-concurrency applications, use a tool like PgBouncer to manage connections efficiently, reducing the overhead of creating new connections.