Requirements (assumed): store model versions, metrics (time series / aggregates), tags, artifact references (URIs), support queries for latest version, search by tag/metric thresholds, audit history, transactions for promote/deprecate, and schema evolution.
PostgreSQL — schema options:
- Relational (normalized): tables: models, versions, metrics, tags, artifacts, users. versions FK→models; metrics rows per run/version. Good for integrity and complex joins.
- JSONB hybrid: versions table with JSONB metadata column for flexible fields (hyperparams, extra tags) + indexed JSONB paths for common fields.
Typical queries & indexes:
- Query latest version: index on (model_id, created_at DESC) or partial unique where is_current.
- Search by tag: tags table with (model_id, key, value) indexed; or GIN index on JSONB tags.
- Metric threshold/aggregate: index on (version_id, metric_name, metric_value) or use materialized views for summaries.
- Use transactions for promote/deprecate to ensure single current version (SELECT ... FOR UPDATE).
MongoDB — schema options:
- Document-per-version: collection "versions" containing model_id, version_meta, metrics (array or subdocs), tags, artifact refs.
- Embedded metrics vs separate collection: embed recent metrics, push older to time-series collection.
Typical queries & indexes:
- Latest version: compound index {model_id:1, created_at:-1}.
- Tag/metadata search: create indexes on frequently queried fields or use wildcard/compound indexes; GIN-like capabilities via multikey indexes on arrays.
- Metrics queries: if metrics embedded, create indexes on metrics.metric_name and metrics.value (multikey) or use time-series collections.
Tradeoffs:
- Consistency & transactions: Postgres has strong ACID, multi-row transactions, ideal for guarantees (single current version). MongoDB offers multi-document transactions since v4.0 but with higher complexity and potential throughput cost.
- Analytics: Postgres excels at complex joins, aggregates, and SQL analytics (materialized views). MongoDB's aggregation pipeline is powerful for nested docs and fast single-collection reads; for heavy analytics, couple Mongo with a columnar store/ETL.
- Schema evolution: MongoDB is naturally flexible for evolving metadata fields. Postgres JSONB provides flexibility while preserving relational constraints for core fields.
- Performance/scaling: Postgres is efficient for transactional workloads and relational queries; horizontal scaling is harder. MongoDB scales horizontally more easily for high write volumes and schema variability but may complicate queries needing joins.
- Operational: Backups, migrations, and tooling mature in Postgres; MongoDB simplifies evolving fields but requires careful index management to avoid hot shards.
Recommendation (ML engineer): If you need strong transactional guarantees (promote/deprecate, audit), relational core with Postgres + JSONB for flexible metadata. If you expect high write volume, many sparse evolving fields, or want simpler single-document reads for model serving, MongoDB with careful indexing and periodic ETL for analytics is suitable.
