{"dataset":{"id":"00000000-0000-0000-0000-000000000010","slug":"postgres-best-practices","name":"PostgreSQL best practices","description":"Source-linked guidance on PostgreSQL fundamentals: EXPLAIN query plans, index types, transaction isolation, table constraints, and routine vacuuming. Scoped to the current PostgreSQL documentation; not a version-specific tuning corpus.","visibility":"public","version":1,"documentCount":5,"publisher":"Koskamo editorial","license":"Original summaries; linked sources retain their own terms.","reviewedAt":"2026-10-04T00:00:00.000Z","readOnly":true,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"},"total":5,"documents":[{"title":"Routine VACUUM and up-to-date planner statistics","sourceUrl":"https://www.postgresql.org/docs/current/routine-vacuuming.html","text":"PostgreSQL's VACUUM command has to process each table on a regular basis for several reasons: to recover or reuse disk space occupied by updated or deleted rows, to update data statistics used by the PostgreSQL query planner, to update the visibility map which speeds up index-only scans, and to protect against loss of very old data due to transaction ID wraparound or multixact ID wraparound. There are two variants: standard VACUUM and VACUUM FULL. VACUUM FULL can reclaim more disk space but runs much more slowly and requires an ACCESS EXCLUSIVE lock on the table it is working on, so it cannot be done in parallel with other use of the table; the standard form of VACUUM can run in parallel with production database operations such as SELECT, INSERT, UPDATE, and DELETE, and administrators should generally strive to use standard VACUUM and avoid VACUUM FULL. For many installations it is sufficient to let vacuuming be performed by the autovacuum daemon, which is enabled in the default configuration, though you might need to adjust the autovacuuming parameters to obtain best results for your situation. The planner relies on statistical information about the contents of tables to generate good plans; these statistics are gathered by the ANALYZE command, which can be invoked by itself or as an optional step in VACUUM, and the autovacuum daemon will automatically issue ANALYZE commands whenever the content of a table has changed sufficiently. It is important to have reasonably accurate statistics, otherwise poor choices of plans might degrade database performance. Scope: PostgreSQL current-release documentation. Reviewed October 4, 2026.","id":"postgres-best-practices:Routine VACUUM and up-to-date planner statistics","version":1,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"},{"title":"Table constraints as the guardian of data integrity","sourceUrl":"https://www.postgresql.org/docs/current/ddl-constraints.html","text":"SQL lets you define constraints on columns and tables, and constraints give you as much control over the data in your tables as you wish: if a user attempts to store data in a column that would violate a constraint, an error is raised, and this applies even if the value came from the default value definition. A check constraint is the most generic constraint type; it requires that the value in a certain column satisfy a Boolean expression, for instance CHECK (price > 0) to require positive product prices. A not-null constraint simply specifies that a column must not assume the null value. A primary key constraint indicates that a column, or group of columns, can be used as a unique identifier for rows in the table, which requires that the values be both unique and not null. A foreign key constraint specifies that the values in a column or group of columns must match the values appearing in some row of another table, maintaining the referential integrity between two related tables. Exclusion constraints ensure that if any two rows are compared on the specified columns or expressions using the specified operators, at least one of these operator comparisons will return false or null, and adding an exclusion constraint automatically creates an index of the type specified in the constraint declaration. Enforcing these rules in the database itself, rather than only in application code, is Koskamo editorial guidance. Scope: PostgreSQL current-release documentation. Reviewed October 4, 2026.","id":"postgres-best-practices:Table constraints as the guardian of data integrity","version":1,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"},{"title":"Transaction isolation levels under concurrency","sourceUrl":"https://www.postgresql.org/docs/current/transaction-iso.html","text":"The SQL standard defines four levels of transaction isolation; the most strict is Serializable, and the other three are defined in terms of phenomena resulting from interaction between concurrent transactions which must not occur at each level: dirty read (a transaction reads data written by a concurrent uncommitted transaction), nonrepeatable read (a transaction re-reads data it has previously read and finds it modified by another transaction that committed since the initial read), phantom read (a transaction re-executes a query and finds the set of rows satisfying its search condition has changed due to another recently-committed transaction), and serialization anomaly (the result of successfully committing a group of transactions is inconsistent with all possible orderings of running those transactions one at a time). In PostgreSQL you can request any of the four standard isolation levels with SET TRANSACTION, but internally only three distinct isolation levels are implemented: PostgreSQL's Read Uncommitted mode behaves like Read Committed. Read Committed is the default isolation level in PostgreSQL; a SELECT query sees only data committed before the query began and never sees uncommitted data or changes committed by concurrent transactions while the query runs. The Serializable level provides the strictest transaction isolation, emulating serial transaction execution for all committed transactions as if they had been executed one after another, and applications using this level must be prepared to retry transactions due to serialization failures. Scope: PostgreSQL current-release documentation. Reviewed October 4, 2026.","id":"postgres-best-practices:Transaction isolation levels under concurrency","version":1,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"},{"title":"Index types and when the planner uses them","sourceUrl":"https://www.postgresql.org/docs/current/indexes-types.html","text":"PostgreSQL provides several index types: B-tree, Hash, GiST, SP-GiST, GIN, BRIN, and the extension bloom. Each index type uses a different algorithm best suited to different types of indexable clauses; by default CREATE INDEX creates B-tree indexes, which fit the most common situations, and other types are selected by writing the keyword USING followed by the index type name. B-trees can handle equality and range queries on data that can be sorted into some ordering, and the planner will consider using a B-tree index whenever an indexed column is involved in a comparison using <, <=, =, >=, or >; constructs equivalent to combinations of these operators, such as BETWEEN and IN, can also be implemented with a B-tree index search, as can IS NULL and IS NOT NULL conditions on an index column. The optimizer can also use a B-tree index for the pattern matching operators LIKE and ~ when the pattern is a constant anchored to the beginning of the string, for example col LIKE 'foo%' but not col LIKE '%bar', and B-tree indexes can be used to retrieve data in sorted order. Hash indexes store a 32-bit hash code derived from the value of the indexed column and can only handle simple equality comparisons using =. GIN indexes are inverted indexes appropriate for data values that contain multiple component values, such as arrays; GiST and SP-GiST are infrastructures within which many different indexing strategies can be implemented, and both can optimize nearest-neighbor searches for operator classes that support distance ordering. Scope: PostgreSQL current-release documentation. Reviewed October 4, 2026.","id":"postgres-best-practices:Index types and when the planner uses them","version":1,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"},{"title":"Reading query plans with EXPLAIN","sourceUrl":"https://www.postgresql.org/docs/current/using-explain.html","text":"PostgreSQL devises a query plan for each query it receives, and the EXPLAIN command shows what query plan the planner creates for any query. Plan costs are measured in arbitrary units determined by the planner's cost parameters; traditional practice measures costs in units of disk page fetches, with seq_page_cost conventionally set to 1.0 and the other cost parameters set relative to that. Each plan node reports an estimated startup cost, the time spent before the output phase can begin, and an estimated total cost, stated on the assumption that the node runs to completion, plus estimated row counts and the average width of output rows in bytes; the cost of an upper-level node includes the cost of all its child nodes. EXPLAIN ANALYZE actually runs the query, so any side-effects will happen as usual even though whatever results the query might output are discarded in favor of printing the EXPLAIN data; to analyze a data-modifying query without changing your tables, run it inside BEGIN and roll the command back afterwards. The measurement overhead added by EXPLAIN ANALYZE can be significant, especially on machines with slow gettimeofday() operating-system calls, and the pg_test_timing tool measures that overhead on your system. EXPLAIN results should not be extrapolated to situations much different from the one you are actually testing; results on a toy-sized table cannot be assumed to apply to large tables. Scope: PostgreSQL current-release documentation. Reviewed October 4, 2026.","id":"postgres-best-practices:Reading query plans with EXPLAIN","version":1,"createdAt":"2026-10-04T00:00:00.000Z","updatedAt":"2026-10-04T00:00:00.000Z"}]}