How to Query Tuning Oracle in PostgreSQL

How do I speed up slow queries in PostgreSQL?

Query tuning maximizes PostgreSQL performance by analyzing execution plans, adding indexes, rewriting SQL, and adjusting planner settings.

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Description

Example H2

Example H3

What is query tuning in PostgreSQL?

Query tuning is the systematic way of making SQL run faster by inspecting execution plans, creating the right indexes, and rewriting queries so the planner picks the cheapest path.

Why does EXPLAIN ANALYZE matter for tuning?

EXPLAIN ANALYZE shows the actual execution plan and runtime statistics. It reveals whether PostgreSQL uses indexes or sequential scans, how many rows flow between nodes, and where time is spent.

How do I read an EXPLAIN plan quickly?

Start at the most indented node—that is the first operation executed. Compare estimated rows (rows) to actual rows (actual). Large differences suggest outdated statistics or missing indexes.

Which planner settings help diagnose issues?

Turn on SET track_io_timing = on to see I/O wait, and use SET enable_seqscan = off to force index usage temporarily when testing alternatives.

When should I add an index?

Add an index when filters or joins on a column touch a small fraction of the table. Use multicolumn or expression indexes for composite predicates such as WHERE customer_id = ? AND order_date > now()-'30 days'::interval.

What are fast ways to rewrite slow queries?

Replace SELECT * with required columns, push predicates into sub-queries or CTEs, and prefer EXISTS over IN for correlated subqueries. Remove unnecessary DISTINCT and ORDER BY.

Best practice: combine statistics & indexes

After creating an index, run ANALYZE so PostgreSQL updates statistics and starts using the new access path immediately.

Best practice: batch updates

Bulk insert or update using COPY or multi-row INSERT to reduce commit overhead and avoid bloating indexes.

Why How to Query Tuning Oracle in PostgreSQL is important

How to Query Tuning Oracle in PostgreSQL Example Usage


-- Find high-value customers in the last 30 days and view plan
EXPLAIN ANALYZE
SELECT c.id, c.name, SUM(o.total_amount) AS spend
FROM Customers c
JOIN Orders o ON o.customer_id = c.id
WHERE o.order_date > CURRENT_DATE - INTERVAL '30 days'
GROUP BY c.id, c.name
HAVING SUM(o.total_amount) > 5000
ORDER BY spend DESC
LIMIT 10;

How to Query Tuning Oracle in PostgreSQL Syntax


EXPLAIN [ANALYZE] [VERBOSE] [COSTS] [BUFFERS] [TIMING] [SUMMARY] [FORMAT { TEXT | JSON | YAML | XML }]
  SELECT columns
  FROM Orders o
  JOIN Customers c ON c.id = o.customer_id
  WHERE o.order_date > CURRENT_DATE - INTERVAL '30 days';

CREATE INDEX idx_orders_customer_date
  ON Orders (customer_id, order_date DESC);

SET enable_seqscan = off; -- test index-only plan

Common Mistakes

Ignoring ANALYZE after bulk loads. Without fresh statistics PostgreSQL underestimates row counts, leading to sequential scans. Always run ANALYZE or VACUUM ANALYZE after large inserts.
Creating too many overlapping indexes. Each DML operation maintains every index, slowing writes. Drop unused indexes and prefer composite indexes covering multiple predicates.

Frequently Asked Questions (FAQs)

Does EXPLAIN ANALYZE change data?

No. It runs the query and discards results, so data remains unchanged. Use it freely on production if the query itself is safe.

How can I see memory usage per query?

Enable track_activity_query_size and inspect pg_stat_activity or add EXPLAIN (BUFFERS) to view shared/hit buffer counts, which correlate with memory.