database-technology-selection
Select DB technology for a use case. Evaluates DB classes (relational / document / wide-column / key-value / graph / time-series / search / vector / cache / analytical / ledger / newSQL) against consistency / scale / query patterns / schema flex / ops / cost / ecosystem.
What this skill does
# Database Technology Selection You select the right DB class + specific product for a use case. Distinct from `technology-evaluation-matrix` (generic): this is DB-specific with DB-specific criteria. ## Core rules - **Multiple DBs is normal** — polyglot persistence for different needs - **Start with query patterns** — reads + writes drive choice - **Consistency model explicit** — strong / eventual / per-aggregate - **Scale envelope realistic** — what's the actual projected load - **Operational complexity counts** — who runs it, managed vs self-hosted ## DB class catalog | Class | Best for | Examples | |---|---|---| | **Relational / RDBMS** | Transactions, joins, structured data, SQL ecosystem | Postgres, MySQL, SQL Server, Oracle | | **Document** | Schema flexibility, nested aggregates | MongoDB, Couchbase, DynamoDB (document-mode) | | **Wide-column** | Time-series / logs / massive-write | Cassandra, ScyllaDB, HBase, BigTable | | **Key-value** | Simple lookups, cache, session | Redis, Memcached, DynamoDB (KV-mode), Riak | | **Graph** | Deeply-connected data, traversal queries | Neo4j, JanusGraph, Amazon Neptune | | **Time-series** | Metrics, IoT, monitoring | InfluxDB, TimescaleDB, Prometheus, Clickhouse | | **Search** | Full-text, faceted search, log analysis | Elasticsearch, OpenSearch, Typesense, Meilisearch | | **Vector** | Embeddings, similarity search, RAG | Pinecone, Weaviate, Qdrant, pgvector (Postgres ext) | | **Cache (in-memory)** | Speed layer | Redis, Memcached | | **Analytical / OLAP** | Reporting, aggregations over large data | Snowflake, BigQuery, Redshift, Clickhouse, DuckDB | | **Ledger / immutable** | Audit trail, financial | QLDB, ImmuDB, blockchain | | **NewSQL** | SQL ecosystem + horizontal scale | CockroachDB, YugabyteDB, Spanner, TiDB | ## Selection criteria (DB-specific) | Criterion | What it captures | |---|---| | **Consistency model** | Strong / eventual / per-aggregate / linearizable | | **Query patterns** | Read-heavy / write-heavy / analytical / transactional / search / graph traversal | | **Schema flex** | Fixed / evolving / document / schemaless | | **Scale envelope** | Current + projected data volume + QPS | | **ACID vs BASE** | Transaction needs | | **Latency SLO** | p99 target | | **Ops complexity** | Self-hosted / managed service; team familiarity | | **Cost per GB / per request** | At projected scale | | **Ecosystem / tooling** | Drivers, migrations, backup, monitoring | | **Backup + DR** | RPO / RTO achievable | | **Multi-region** | If needed | ## Decision flow 1. **Query patterns → class**: what are you querying and how? 2. **Consistency → filter within class**: what consistency is acceptable? 3. **Scale → product within class**: single-node vs distributed? 4. **Ops + cost → final**: what can team run + afford? ## Polyglot persistence Typical production uses 3–5 DBs: - Primary OLTP (Postgres) - Cache (Redis) - Search (Elasticsearch) - Analytics (Snowflake) - Optional: time-series, graph, vector per domain need Don't treat it as monolithic choice. ## Trade-offs per class Per recommendation, surface: - **Won on**: criteria that favor this class - **Lost on**: criteria where it's weak - **Alternatives considered**: other classes/products rejected and why - **Reversal conditions**: triggers to revisit ## Report ```markdown # Database Technology Selection: [Use case] ## Scope [Use case + data shape + scale + query patterns + consistency needs] ## DB Class Decision [Class + rationale from query patterns] ## Product Within Class [Specific product + rationale] ## Polyglot Considerations [Other DBs for complementary needs] ## Trade-offs [Won on / lost on / alternatives] ## Reversal Conditions [Triggers] ## Migration / Adoption Plan [If replacing existing DB] ``` ## Failure behavior - Generic "pick a DB" without query patterns → interview - "Microservices + one DB per service" pushed for small team → flag overhead - DB chosen for cool-factor not fit → challenge - mmdc failure → see mixin
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