| name | redis-query-engine |
| description | Redis Query Engine (RQE) guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR), DIALECT 2 query syntax, efficient FT.SEARCH and FT.AGGREGATE queries, zero-downtime index updates via aliases, and the SKIPINITIALSCAN option. Use when defining a search index on Hash or JSON documents, picking between TEXT and TAG for filtering, writing FT.SEARCH queries with filters and SORTBY, managing or swapping indexes in production, or troubleshooting slow searches with FT.PROFILE. |
| license | MIT |
| metadata | {"author":"Redis, Inc.","version":"0.1.0"} |
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 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
