firestore-query-indexes

name	firestore-query-indexes
description	Design Firestore query shapes and the composite indexes they require — filters, ordering, pagination, collection groups, denormalized reads. Use when the user plans Firestore reads, hits a missing-index error, or asks for firestore.indexes.json.

Firestore Query & Index Design

A query plan is an index proof: name what the product must read, prove each query is allowed by rules, and extract the composite indexes the code requires. Model reads first — slow Firestore apps usually download too many documents, not scan too many.

Steps

1. Inventory query intents. For each screen, report, listener, or search: collection path (or collection group), filters, orderBy fields, limit, cursor strategy, auth identity, and expected result size. Complete when every read the product performs is a row in the inventory.

2. Choose read shapes. Options per intent: direct document read, top-level collection query, subcollection query (removes a where for single-parent reads), collection-group query (cross-parent subcollection reads), or a denormalized summary document. Complete when each intent has a shape and a one-line justification.

3. Prove rules compatibility. Rules are not filters: a list query must be constrained (where on owner/membership fields) so it can only return documents its identity may read. Verify with firestore_simulate_rules per identity, or a firestore_test_rules case per query. Complete when every list query has a matching rule + constraint pair.

4. Write the query code in modular SDK shape inside a function body — query(collection(db, ...), where(...), orderBy(...)) — so the extractor can see it. Complete when each inventory row has code.

5. Extract indexes. Run firestore_extract_indexes over the query code after every change. It returns firestore.indexes.json-shaped config plus warnings. Zero extracted shapes means the source didn't expose a pattern (missing file, admin-chain syntax, no composite query) — report that and fix the source; never hand-write index JSON the extractor didn't produce. Review overshootSuspected warnings; a targeted @firestore-mutex annotation trims shapes enumerated from mutually exclusive branches. Complete when extraction succeeds with reviewed warnings.

6. Deploy and confirm. Write the reviewed config to firestore.indexes.json, then apply it with npx firebase-tools deploy --only firestore:indexes. Confirm each build is ready in Firebase before exercising the query. Complete when every composite query has a ready index.

7. Verify against data. Run representative queries with firestore_query_where (seed via firestore_batch_write if needed) and confirm result shape and size match the inventory. Complete when each intent returns what its screen expects.

Reference — query rules

• Single-field queries index automatically; combined filters/order need composite indexes.
• Arrays take array-contains / array-contains-any; use a real array, not a map-as-tag shape, when membership is the access pattern.
• Paginate with cursors, never growing limits or offsets.
• Document reads are shallow — subcollections do not ride along.
• Denormalization is a spectrum: duplicate slow-changing display data when it removes repeated lookups or impossible joins; plan fan-out writes to keep copies consistent.