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redis-query-engine

redis-query-engine

github.com/redisData & databases171435 installsUpdated 7w ago
Repository

About redis-query-engine

This skill guides developers in indexing and querying Hash and JSON documents with Redis Query Engine. It covers field selection, query tuning, index lifecycle management, and alias-based updates to limit downtime.

Key features

  1. 01Guidance for FT.CREATE and FT.ALTER
  2. 02Covers FT.SEARCH and FT.AGGREGATE
  3. 03Explains field types and query uses
  4. 04Shows index updates with aliases
  5. 05Covers query tuning and performance pitfalls

Use cases

  1. 01Design indexes for Hash or JSON documents
  2. 02Optimize search and aggregation queries
  3. 03Plan production index schema updates
  4. 04Control index scope and query results

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/redis/agent-skills --skill redis-query-engine

After installing, type/redis-query-enginein Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

.cursor-plugin/
plugin.json
references/
dialect.md
field-types.md
index-creation.md
index-management.md
query-optimization.md
skip-initial-scan.md

Details

From the project README

Redis Query Engine

Guidance for using the Redis Query Engine (RQE) to index and search Hash or JSON documents. Covers schema design with FT.CREATE, field-type choices, query syntax, index lifecycle management, and the most common performance pitfalls.

When to apply

  • Creating, modifying, or reviewing an RQE index (FT.CREATE, FT.ALTER).
  • Writing or optimizing FT.SEARCH / FT.AGGREGATE queries.
  • Deciding between TEXT, TAG, NUMERIC, GEO, GEOSHAPE, or VECTOR for a field.
  • Rolling out a new index schema without downtime.
  • Spinning up an index that should only cover newly written keys.

1. Use DIALECT 2 (the modern default)

DIALECT 2 is the baseline. Other dialects (1, 3, 4) are deprecated as of Redis 8. Most modern client libraries already default to it — but specify it explicitly in raw commands for portability.

FT.SEARCH idx:products "@name:laptop" DIALECT 2

DIALECT 2 is required for vector search queries. It also handles special characters and NULLs predictably.

See references/dialect.md.

2. Pick the right field type

The field type decides both what you can query and how fast that query is. Use the narrowest type that supports your access pattern.

Field typeUse whenNotes
TEXTFull-text search neededTokenized + stemmed; not for exact match
TAGExact match / filteringAdd SORTABLE UNF for fastest tag queries
NUMERICRange queries, sortingPrices, counts, timestamps
GEOLat/long point queriesSingle points (stores, users)
GEOSHAPEPolygon / area queriesDelivery zones, regions
VECTORSimilarity searchHNSW or FLAT; see redis-vector-search

The classic mistake is using TEXT for a category or status field because "it's a string." TAG is 10× faster for those.

See references/field-types.md.

3. Index only what you query — and always set a prefix

FT.CREATE without a PREFIX indexes every matching key in the database; with a wide schema it can blow up index size and write latency.

FT.CREATE idx:products ON HASH PREFIX 1 product:
    SCHEMA
        name TEXT WEIGHT 2.0
        category TAG SORTABLE
        price NUMERIC SORTABLE
        location GEO

Rules of thumb:

  • Start with the minimum schema. Add fields as new query patterns emerge.
  • Always set PREFIX (or filter via FILTER expression).
  • Use FT.INFO idx:<name> to monitor index size after adding fields.
  • Use SORTABLE only on fields you actually sort by; it has a memory cost.

See references/index-creation.md.

4. Zero-downtime index updates — use aliases

For schema changes in production, keep application queries pointed at an alias and swap the underlying index.

FT.CREATE idx:products_v2 ON HASH PREFIX 1 product: SCHEMA ...
FT.ALIASUPDATE products idx:products_v2

# App queries are stable:
FT.SEARCH products "@category:{electronics}"

Useful management commands: FT.INFO, FT.DROPINDEX, FT._LIST, FT.ALIASADD/UPDATE/DEL.

See references/index-management.md.

5. SKIPINITIALSCAN — only when historical data is irrelevant

By default FT.CREATE walks all existing keys that match the prefix and indexes them. Use SKIPINITIALSCAN only when:

  • You're standing up the index for a new feature and existing data shouldn't be queryable.
  • Existing data is too large to scan synchronously.
  • You're indexing event streams where only future events matter.

For most schema migrations, the default (scan everything) is what you want.

See references/skip-initial-scan.md.

6. Write specific queries, not *

Narrow the result set with filters before paging or aggregating.

# Good — specific filter, limited fields returned
FT.SEARCH idx:products "@category:{electronics} @price:[100 500]"
    LIMIT 0 20
    RETURN 3 name price category
# Bad — full scan plus unbounded LIMIT
FT.SEARCH idx:products "*" LIMIT 0 10000

Other levers:

  • SORTBY requires SORTABLE on the sort field. Without it, sort is slow.
  • LIMIT early; the engine still processes everything above the limit if you don't.
  • RETURN specific fields — don't fetch the whole document if you only need a few.
  • Profile with FT.PROFILE idx:<name> SEARCH QUERY "<query>" when a query is slow.

See references/query-optimization.md.

References

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