Redis query engine

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.

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 type	Use when	Notes
TEXT	Full-text search needed	Tokenized + stemmed; not for exact match
TAG	Exact match / filtering	Add SORTABLE UNF for fastest tag queries
NUMERIC	Range queries, sorting	Prices, counts, timestamps
GEO	Lat/long point queries	Single points (stores, users)
GEOSHAPE	Polygon / area queries	Delivery zones, regions
VECTOR	Similarity search	HNSW 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

• Redis: Query Engine — Indexing
• Redis: Query syntax
• Redis: Query dialects
• Redis: Administration (aliases, dropindex)
• FT.CREATE