How to Tune Queries in Amazon Redshift

How do I tune slow queries in Amazon Redshift?

Query tuning in Amazon Redshift means inspecting, rewriting, and configuring SQL so statements finish faster and use fewer cluster resources.

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Description

Example H2

Example H3

What is query tuning in Amazon Redshift?

Query tuning is the process of examining SQL execution plans, table design, and cluster configuration to minimise runtime and cost. You identify bottlenecks, rewrite statements, and adjust WLM queues to match workload needs.

How do I find slow queries?

Query monitoring tables—SVL_QLOG, STL_EXPLAIN, and SVL_QUERY_SUMMARY—list duration, rows scanned, and steps. Filter by highest query_cpu_time to isolate expensive statements.

How do I read EXPLAIN output?

Run EXPLAIN on your SQL.Look for large DS_BCAST_INNER or DS_DIST_ALLNONE steps signalling data redistribution. Reduce them with DISTKEYs, JOIN changes, or predicate pushdown.

Which SQL patterns slow Redshift?

Unrestricted SELECT *, cross-region joins, LIKE '%text%' filters, row-by-row correlated subqueries, and missing predicates cause large scans and network shuffles.

How can I speed up joins?

Co-locate join columns with identical DISTKEYs or DISTSTYLE ALL on small dimension tables.Add SORTKEYs on frequently filtered columns to prune blocks quickly.

When should I use result caching?

Keep result caching on for dashboards with identical parameters. Disable per session with SET enable_result_cache_for_session TO off when testing or ingesting near-real-time data.

How do I balance WLM queues?

Create separate queues for ETL, ad-hoc, and reporting queries.Allocate enough slots to ETL but cap concurrency for heavy joins to prevent cluster saturation.

Best practices recap

Use EXPLAIN and ANALYZE regularly, re-write predicates, align DIST/SORT keys with joins and filters, VACUUM & ANALYZE after large loads, and monitor SVL views.

Why How to Tune Queries in Amazon Redshift is important

How to Tune Queries in Amazon Redshift Example Usage


-- Original slow query (10 min)
SELECT COUNT(*)
FROM   Orders o
JOIN   OrderItems oi ON oi.order_id = o.id
WHERE  o.order_date BETWEEN '2024-01-01' AND '2024-03-31';

-- Tuned version (20 s)
/* Assumptions: Orders DISTKEY(order_date), SORTKEY(order_date) */
EXPLAIN ANALYZE
SELECT COUNT(*)
FROM   Orders o
JOIN   OrderItems oi ON oi.order_id = o.id
WHERE  o.order_date BETWEEN '2024-01-01' AND '2024-03-31'
  AND  oi.quantity > 0;

How to Tune Queries in Amazon Redshift Syntax


-- Inspect a plan without running the query
EXPLAIN [ANALYZE] SELECT
    c.name, SUM(oi.quantity * p.price) AS customer_spend
FROM   Customers c
JOIN   Orders o        ON o.customer_id = c.id
JOIN   OrderItems oi   ON oi.order_id   = o.id
JOIN   Products p      ON p.id          = oi.product_id
WHERE  o.order_date >= '2024-01-01'
GROUP  BY c.name
ORDER  BY customer_spend DESC;

-- Session-level cache control
SET enable_result_cache_for_session TO {on|off};

-- Update statistics after bulk load
ANALYZE Customers;
VACUUM FULL Orders;

Common Mistakes

Ignoring DISTKEY alignment: Joining large tables without matching DISTKEYs forces costly data redistribution. Fix by assigning the same DISTKEY to frequently joined columns.
Running VACUUM rarely: Skipping VACUUM after heavy deletes causes unsorted, bloated tables and slower scans. Schedule VACUUM FULL during low-traffic windows.