pgvector and Qdrant Parity Matrix

This file is generated from docs/user_guide/parity_matrix.data. Run scripts/generate-parity-matrix.sh after changing the source data.

Status values:

  • stable: covered by the first production SQL compatibility contract.
  • experimental: SQL-visible or implemented, but outside the production promise.
  • planned: explicitly not part of the first stable surface yet.
  • intentionally different: pgContext deliberately uses PostgreSQL-native semantics.
Capability Reference Status pgContext release contract Owning reference
Dense vector SQL type, casts, operators, aggregates pgvector stable Dense vector, including vector(n) typmod metadata and assignment enforcement, is the stable first-release vector surface. docs/user_guide/api_reference.md
Exact vector search over arrays and registered tables pgvector,Qdrant stable Exact search is the correctness baseline for SQL search, recall checks, ANN, and hybrid retrieval. docs/user_guide/vector_search.md
Collections over PostgreSQL source tables Qdrant stable Collections reference authoritative PostgreSQL source tables and pgContext catalog metadata. docs/user_guide/collections.md
Point upsert and delete mappings Qdrant stable Point APIs map source keys to stable pgContext point IDs without owning source rows. docs/user_guide/collections.md
Filter JSON over ordinary columns and JSONB paths Qdrant stable Registered fields render through typed SQL predicates and SPI parameters. docs/user_guide/filters.md
Scroll, count, and facet Qdrant stable Stable APIs operate over active table-backed point mappings with shared filter semantics. docs/user_guide/api_reference.md
Dense plus full-text hybrid query Qdrant stable pgcontext.query supports dense vector plus PostgreSQL full-text retrieval with reciprocal rank fusion. docs/user_guide/hybrid_retrieval.md
Telemetry and operational status functions Qdrant stable Diagnostics expose typed statuses and local counters without vectors, payloads, filters, or literal query text. docs/user_guide/operations.md
Model versions and embedding migrations Qdrant stable Model-version metadata and migration progress records are stable catalog APIs. docs/user_guide/collections.md
HNSW access method pgvector,Qdrant experimental pgcontext_hnsw serves dense and halfvec/sparsevec L2, inner-product, cosine, and L1 kNN plus bitvec Hamming/Jaccard through metric-bound persisted PostgreSQL pages. Non-dense opclass names and bindings are stable; the access method’s on-disk compatibility remains experimental. Traversal exposes hnsw_last_scan_work, has no silent exact fallback, and every non-dense pair passes exact-oracle and bounded-candidate gates. docs/user_guide/indexes.md
Filtered ANN serving Qdrant experimental Filtered table search binds a validated HNSW index and, in one statement snapshot, materializes registered column/JSONB candidates once. Selective masks cross over to exact scoring; broader masks drive one page-backed HNSW traversal using the stored metric. Masked-out nodes remain connectors and sparse masks can expand through ACORN-like second-hop neighbors. Final source joins preserve logical PointId, MVCC/deletion state, ACL/RLS, predicate, and exact-distance rechecks. Selective, broad, empty, stale-stat, deleted, typed JSONB, tenant-isolation, and cancellation cases are covered. docs/user_guide/vector_search.md
SQL halfvec pgvector experimental halfvec text I/O, dimensions, typmods, exact distance helpers, distance operators, explicit rounding numeric-array casts, aggregates, and btree ordering remain experimental; stable HNSW opclasses cover L2, inner product, cosine, and L1 over dense graph storage. docs/user_guide/pgvector_migration.md
SQL sparsevec pgvector,Qdrant experimental sparsevec text I/O, dimensions, typmods, structured construction, casts, exact metrics, aggregates, and collection metadata remain experimental; stable HNSW opclasses cover L2, inner product, cosine, and L1, and named sparse search can bind them for bounded candidates with exact rerank. docs/user_guide/vector_search.md
SQL bit vectors pgvector experimental bitvec type helpers, casts, exact Hamming/Jaccard operators, aggregates, and btree ordering remain experimental; stable explicit HNSW opclasses serve Hamming and Jaccard. Default pgcontext_hnsw attempts fail with SQLSTATE 42704 so callers must choose the bit metric. docs/user_guide/pgvector_migration.md
SQL quantization APIs pgvector,Qdrant experimental Binary, scalar/SQ8-style, and product encodings serve from revision-bound mapped HNSW artifacts with exact source rerank; PostgreSQL index-AM pages still store full-precision graph vectors. docs/user_guide/indexes.md
Named dense vector registration and search Qdrant stable Named dense vector registration, dimensions, metrics, and search-by-name selection are part of the first stable table-backed search surface. docs/user_guide/collections.md
Per-vector dense index and quantization metadata Qdrant experimental The experimental collection_vectors and configure_vector functions expose validated metadata containers; HNSW/quantization option semantics and full planner use remain planned. docs/user_guide/collections.md
Named sparse vectors per collection Qdrant experimental Sparse registration, exact fallback, validated metric-matched HNSW attachment, filtered candidate masks, bounded work diagnostics, authoritative exact rerank, and exact dense+sparse RRF query fusion are SQL-visible. docs/user_guide/collections.md
Multi-vector and late-interaction query Qdrant experimental Exact late-interaction MaxSim rerank over explicit arrays, exact table-backed vector[] search, typed ANN-planner diagnostics, and HNSW token candidate generation with O(1) declared token-dimension validation, source-table exact MaxSim rerank, deleted-point checks, token-table prerequisite failures, planner preflight, and hydrated budget checks are SQL-visible; full memory/latency release gates remain open. docs/user_guide/vector_search.md
Recommendation search Qdrant stable Positive/negative point-ID and raw-vector recommendation search uses exact rerank with ACL/RLS and deleted-point checks. docs/user_guide/vector_search.md
Discovery or explore search Qdrant stable Exact diversity-oriented discover/explore search ranks active rows farthest from visible context examples. docs/user_guide/vector_search.md
Query constructors and composite execution Qdrant stable Validated SQL constructors produce bounded executable plans for named/filtered dense, named sparse, full-text, quantized mapped HNSW, owned late-interaction, recommend, discover, lookup, prefetch, weighting, thresholds, formulas, and final score ordering; every retrieval candidate is authoritatively rechecked before fusion. docs/user_guide/vector_search.md
Grouped search Qdrant stable Grouped exact search caps results per registered payload field with deterministic ordering and PostgreSQL ACL/RLS checks. docs/user_guide/collections.md
Payload mutation helpers Qdrant stable Registered payload fields can be set, deleted, or cleared through documented source-table mutation policy. docs/user_guide/collections.md
Bulk point backfill APIs Qdrant stable Bulk point upsert, delete, and source-table backfill APIs report bounded per-batch progress diagnostics. docs/user_guide/collections.md
IVFFlat pgvector intentionally different pgContext does not support IVFFlat in the first production surface; use exact search or HNSW paths, or keep pgvector IVFFlat indexes alongside pgContext during migration. docs/user_guide/indexes.md
PostgreSQL-native ACL, RLS, transactions, and backups Postgres improvement intentionally different pgContext relies on PostgreSQL source tables, privileges, RLS, transactions, backup, and WAL instead of replacing them. docs/user_guide/security.md
Rebuildable acceleration artifacts Postgres improvement intentionally different Indexes and segment files are cache artifacts; PostgreSQL tables remain authoritative. docs/user_guide/storage.md