# DB Specialized Fit > Specialized-engine fitness (M20) — checks whether a vector, time-series/OLAP, graph, or search store is configured correctly for its job. M20a vector (dimensions match the embedding model, distance metric matches how the model was trained, HNSW/IVFFlat params present), M20b time-series/OLAP (hypertable/partition fit, continuous aggregates, compression), M20c graph (edge modeling, supernode risk, index-backed lookups), M20d search (analyzer/mapping, FTS config). Feeds the Design & Integrity AND Performance & Scale scores per sub-module. - Skill: `hainrixz/db-specialized-fit` (Agent Skill) - Install (CLI): `npx skillmds@latest add hainrixz/db-specialized-fit` - Raw SKILL.md: https://api.skillmd.com/api/skills/hainrixz/db-specialized-fit/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: Hainrixz (https://skillmd.com/u/hainrixz) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/hainrixz/db-specialized-fit --- # db-specialized-fit (M20) — vector · time-series · graph · search M20 covers purpose-built engines whose correctness depends on parameters a generic relational audit ignores. Sub-modules carry a letter (`M20a`..`M20d`); the scorer maps them to the parent M20 in the paradigm profile (Vector / Time-series / Graph categories in `references/scoring-model.md`). ## M20a — Vector (pgvector, Qdrant, Pinecone, Weaviate) *Feeds:* design (Métrica & dimensión, Modelo-version, Metadata/filtro) + performance (Índice & params, Búsqueda filtrada, Recall-vs-latencia). - **Dimension match** — column/collection dim equals the embedding model's output dim (e.g. `vector(1536)` for text-embedding-3-small). A mismatch is a hard bug (design, sev 5). - **Distance metric match** — the index metric (cosine / L2 / inner-product) matches how the model was trained; a mismatch silently wrecks recall. **Sev-5 only when the model is declared in-repo**; else `directional` and do not cap (per scoring-model honesty rule). - **Index present & tuned** — HNSW (`m`, `ef_construction`, `ef_search`) or IVFFlat (`lists`/`probes`) declared, not a brute-force seq scan on a large table (performance). - **Model version captured** — embeddings are tied to a model version so a re-embed is possible (design). - **Filtered search** — metadata used for pre/post-filtering is itself indexed (performance). ## M20b — Time-series / OLAP (TimescaleDB, InfluxDB, ClickHouse) *Feeds:* design (Hypertable-fit, Retención, Precisión-ts & tz) + performance (Chunk/retención, Continuous-agg, Compresión, Query). - **Hypertable / partition fit** — large append-only time data is a hypertable / partitioned by time, with a sane chunk interval (not one giant chunk, not millions of tiny ones). - **Continuous aggregates / rollups** declared for dashboard queries instead of scanning raw rows. - **Compression / TTL retention** policy present for cold chunks; raw retention bounded. - **Timestamp precision & timezone** — `timestamptz`/UTC, not naive local time (shares the M4 rule). ## M20c — Graph (Neo4j) *Feeds:* design (Modelado-aristas, Nodos/traversal) + performance (Índice-lookup, Traversal, Supernodo). - **Edge modeling** — relationships carry the right direction/type; relationship properties exist where traversals need them; no relationship modeled as a node-pair table that defeats the graph engine. - **Index-backed lookups** — entry-point node properties used to start traversals have indexes/constraints (`CREATE CONSTRAINT ... IS UNIQUE`), so traversals don't begin with a full label scan. - **Supernode risk** — a node with a very high relationship degree that will dominate traversal cost (performance). **Sev escalation requires live degree evidence**; else `directional`. ## M20d — Search (Elasticsearch, OpenSearch) *Feeds:* design (mapping/analyzer correctness) + performance (query/shard layout). - **Mapping & analyzer** — fields have explicit mappings (not relying on dynamic mapping for analyzed text); analyzer matches language; keyword vs text chosen deliberately. - **No mapping explosion / unbounded dynamic fields**; shard count sane for index size. ## Tier-0 static checks Parse declared schema/config: pgvector `vector(N)` columns + `CREATE INDEX ... USING hnsw/ivfflat`; Qdrant/Pinecone collection config (`size`, `distance`); Timescale `create_hypertable`/`add_*_policy`; ClickHouse `ENGINE = MergeTree ... ORDER BY`; Neo4j constraint/index DDL; ES `mappings`/`settings` JSON. Cross-check declared embedding-model dim/metric against any model id referenced in repo code. ## Tier-1 verification query - pgvector: `SELECT indexdef FROM pg_indexes WHERE indexdef ILIKE '%hnsw%' OR indexdef ILIKE '%ivfflat%';` and `\d ` for the `vector(N)` dim. - Timescale: `SELECT * FROM timescaledb_information.hypertables;` and `...continuous_aggregates;`. - Neo4j: `SHOW INDEXES;` `SHOW CONSTRAINTS;` and per-node degree for supernode (`MATCH (n) RETURN ...`). - ES: `GET //_mapping`, `GET //_settings`. When the engine isn't reachable → `needs_api`. ## Findings Emit per `schema/finding.schema.json`. Example ids: - `M20a.embeddings.dim_mismatch` — `vector(768)` but model emits 1536 (fail, severity 5, axis `design`, confidence `established` when the model id is in-repo). - `M20a.embeddings.metric_mismatch` — index uses L2 but model trained for cosine (fail, severity 5 only if model declared, else warn/directional, axis `both`). - `M20a.embeddings.no_ann_index` — seq scan vector search (warn, severity 4, axis `performance`). - `M20b.metrics.no_hypertable` — large time table not a hypertable (warn, severity 4, axis `both`). - `M20c.users.unindexed_traversal_root` — traversal start property unindexed (warn, severity 3, perf). Each finding: `evidence.observed` quotes the real DDL/collection config verbatim (secrets redacted); `verification.reproduce` is a runnable command above using `$DATABASE_URL`; `verification.method` is `schema_introspect`, `index_check`, `ddl_parse`, or `explain_plan`; `expected_impact` carries `{axis, confidence, magnitude, rationale}` — banded, never a naked %. ## Honesty - Never assert a recall number or latency for an index configuration — magnitude is banded only. - Metric/dimension mismatch caps only when the embedding model is declared in-repo (durable fact). Inferred mismatches are `directional` and never cap. Supernode/hot-partition sev-5 needs live degree evidence; otherwise `directional` or `needs_api`, never a silent pass.