Redshift Sort Keys: 2025 Best Practices Explained

Definition

A sort key in Amazon Redshift is one or more columns that physically order the data blocks on disk, accelerating range-restricted scans and merge joins. Correctly chosen sort keys can reduce I/O by orders of magnitude; poorly chosen keys waste space and slow queries.

Why It Matters in 2025

Over the last three years Redshift’s architecture has evolved with RA3 nodes, AQUA off-loading, automatic materialized view refreshes, and the new AUTO distribution/style recommendations. Yet the underlying principle remains: disk I/O is the dominant cost of most analytical workloads. In 2025, an optimized sort key strategy is the cheapest way to stretch performance before scaling clusters or paying for Spectrum off-loads. Well-designed sort keys also lower concurrency penalties in Redshift Serverless, where compute credits are consumed per scanned data block.

Core Concepts Refresher

Compound vs. Interleaved vs. Auto

• Compound (default): left-to-right precedence. Ideal when most queries filter on the leading column.
• Interleaved: equal weight for each column; excels with multi-dimensional filters but is more sensitive to skew.
• Auto: Redshift monitors workload and rewrites sort keys during VACUUM/AUTO ANALYZE. It simplifies ops but is still new—monitor generated keys.

RA3 & Managed Storage

With data automatically tiered between SSD and S3, cold blocks become dirt-cheap but slow. Relevant sort keys keep frequently accessed blocks in the SSD cache, effectively giving you a larger hot tier.

2025 Best Practices

1. Start with Query-Driven Design, not Entity Modeling

Mine your stl_query and sys_query_history tables (or CloudWatch Metrics) to find the WHERE clauses that drive the most block scans. Choose columns that appear most often in equality and range predicates, weighted by query frequency and runtime cost.

2. Embrace Incremental Materialization for Fancy Patterns

If a table must satisfy radically different access patterns (e.g., time-series filters and customer-centric lookups), keep one primary sort key optimized for the broader workload, and create MATERIALIZED VIEWs partitioned by alternative keys. Redshift refreshes them incrementally, so you avoid the interleaved penalty.

3. Prefer DATE/TIMESTAMP Leading Keys for Append-Only Fact Tables

Most event/fact tables are insert-only with time filters. A leading date key keeps new data clustered automatically and minimizes the vacuum cost. Pair with AUTO VACUUM and you rarely need manual vacuums.

4. Use Narrow, Encoded Surrogate Keys

Large VARCHARs or long UUIDs inflate block sizes. Instead, map them to INT8 surrogate keys via dimension tables, then encode with AZ64 to shrink disk usage and widen SSD cache effectiveness.

5. Monitor svv_table_info & svv_interleaved_columns Monthly

Sort keys degrade as data grows. Automate a monthly check; if pct_skew > 20 or unsorted > 10, schedule a VACUUM REINDEX or consider redefining the key.

6. Test AUTO Sort Keys in Non-Prod, then Promote

AUTO can surprise you with a completely different compound key. Validate against nightly ETL and ad-hoc workloads before enabling in production. Keep enable_auto_sortkey OFF until satisfied.

7. Combine with Spectrum Partition Projection

When off-loading cold data to Redshift Spectrum, align external table partitions with the same leading column as your internal sort key. Query planners then prune both local blocks and S3 objects consistently.

8. Remember Serverless Credits

Redshift Serverless bills by data scanned. Superior sort keys translate directly into lower compute seconds—and lower AWS bills.

Practical Example

Assume we have a clickstream table ingesting 10 B events per day. Analysts query primarily on a 30-day window and occasionally on a campaign_id.

CREATE TABLE clickstream
( event_time TIMESTAMP NOT NULL,
user_id BIGINT NOT NULL,
campaign_id INT,
page_url VARCHAR(1024),
referrer_url VARCHAR(1024),
user_agent VARCHAR(256)
)
BACKUP YES
SORTKEY (event_time) -- 2025 recommendation
DISTSTYLE AUTO; -- Let Redshift decide distribution


Why not interleaved? Because 95 % of queries restrict by event_time BETWEEN; compound keeps ingestion and vacuum cheap. For the remaining 5 %, create:

CREATE MATERIALIZED VIEW mv_clickstream_by_campaign
DISTSTYLE AUTO
AUTO REFRESH YES
AS
SELECT *
FROM clickstream
WHERE event_time > current_date - INTERVAL '180 days';


Common Mistakes & How to Fix Them

1. Picking High-Cardinality, Sparse Columns

Why wrong: The planner can’t prune many blocks, and vacuums grow expensive.
Fix: Use surrogate integers or move those columns to a late position in a compound key.

2. Switching to Interleaved Prematurely

Why wrong: Interleaved keys lose their benefits when data volumes exceed 2 TB without frequent VACUUM REINDEX. Teams often underestimate maintenance cost.
Fix: Evaluate with EXPLAIN and monitor interleaved_skew before committing.

3. Neglecting Vacuum After Massive Deletes

Why wrong: Deleted blocks remain unsorted. Query runtime spikes gradually, confusing SREs.
Fix: Schedule VACUUM DELETE ONLY; followed by VACUUM SORT ONLY; during low traffic windows or enable AUTO VACUUM.

Working Code Example

The snippet below mines query history to recommend candidate sort keys—a mini workload analyzer you can run inside Galaxy or any SQL editor.

WITH predicates AS (
SELECT
userid,
query,
regexp_substr(lower(text),'where (.*?) (group|order|limit|$)') AS where_clause
FROM stl_querytext
WHERE starttime > dateadd(day,-7,current_timestamp)
),
columns AS (
SELECT column_name
FROM predicates,
regexp_split_to_table(where_clause,'and|or') AS clause,
regexp_matches(clause,'([a-z_]+)') AS m(column_name)
)
SELECT column_name,
COUNT(*) AS freq
FROM columns
GROUP BY 1
ORDER BY 2 DESC;


This query surfaces the most common predicate columns over the past week—perfect input to your sort-key decisions.

Where Galaxy Fits

Galaxy’s blazing-fast desktop client makes Redshift schema exploration painless. Use the AI copilot to auto-document sort-key choices, refactor DDL, and run the workload analyzer above without leaving your editor. Collections let teams endorse the final DDL so nobody accidentally re-creates tables without the right sort key.

Key Takeaways

• Let real workloads—not entity diagrams—drive sort-key design.
• Favor compound date keys for append-only facts; use materialized views for niche patterns.
• Audit skew and unsorted percentages monthly; automate fixes.
• Test AUTO sort keys carefully; they can be game-changing when stable.
• Every block your sort key skips is money saved, especially on Serverless.