Database Types reference data
Different database systems and their characteristics.
Topic guide
What Database Types covers
Database reference data compares the families of data stores and the guarantees behind them: relational systems and the SQL that queries them, ACID transactions versus BASE-style eventual consistency, what the CAP theorem does and does not say about distributed systems, and the NoSQL, graph, time-series and NewSQL systems that trade some properties for others. The consistent theme is trade-offs: no system gives every property at once, so the entries emphasize what each design optimizes for rather than which is best.
Key ideas
- ACID
- Atomicity, consistency, isolation and durability: the guarantees of a classic transaction. A transaction either fully happens or not at all, and committed data survives a crash.
- BASE
- Basically available, soft state, eventually consistent: a design stance that favors availability and scale over immediate consistency.
- CAP theorem
- When a network partition occurs, a distributed system must choose between consistency and availability. It does not describe behavior when there is no partition.
- Isolation level
- How much concurrent transactions can see of one another's work, from read uncommitted to serializable, each preventing a different set of anomalies.
- Normalization
- Structuring relational tables to reduce redundancy; denormalization deliberately reintroduces it for read speed.
- Index
- A data structure that speeds up lookups at the cost of storage and slower writes.
- Sharding
- Splitting data across nodes by key so storage and load scale horizontally.
Common pitfalls
- Reading CAP as 'pick two of three' in normal operation. Partitions are rare; the practical trade-off is often latency versus consistency (PACELC).
- Choosing NoSQL for scale a relational database would handle comfortably, then rebuilding joins and transactions in application code.
- Assuming 'eventually consistent' means 'wrong'. It means replicas converge; applications must tolerate the window.
- Comparing products by feature checklists instead of by their consistency model, failure behavior and operational cost.
Using it from an agent
- When asked to recommend a database, ask for the access patterns and consistency needs first; the best choice follows from them.
- Distinguish the data model (relational, document, graph) from the guarantees (ACID or eventual) when explaining a system.
- Treat benchmark and market-share claims as dated; point to current sources rather than quoting figures.
Start with: Database Systems Comparison · ACID vs BASE Properties
Datasets in Database Types
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ACID vs BASE Properties
Comparison of ACID (traditional RDBMS) and BASE (NoSQL) consistency models.
14 entries -
CAP Theorem
The CAP theorem: a distributed data store can provide only two of three guarantees simultaneously.
7 entries -
Database Isolation and Concurrency Anomalies
Concurrency anomalies, example interleavings, and mitigation choices with PostgreSQL-specific caveats.
8 entries -
Database Systems Comparison
Comparison of database types: Relational, Document, Key-Value, Column-Family, Graph, and Vector databases with examples and best-fit use cases.
11 entries -
Graph Databases Comparison
Property graphs (Neo4j, Amazon Neptune) vs RDF triplestores (Apache Jena). Query languages (Cypher, Gremlin, SPARQL). Use cases: social networks, recommendation engines, knowledge graphs.
8 entries -
NewSQL & Distributed SQL Databases
Modern distributed SQL databases combining ACID with horizontal scaling: CockroachDB, TiDB, YugabyteDB, Google Spanner, Amazon Aurora, Vitess.
7 entries -
NoSQL Databases Overview
Document (MongoDB, Couchbase), Key-Value (Redis, DynamoDB), Column-Family (Cassandra, HBase), Graph (Neo4j, JanusGraph). CAP theorem, scaling, use cases.
11 entries -
PostgreSQL Index Design Patterns
Index choices for equality, ordered retrieval, selective predicates, and specialized PostgreSQL workloads.
10 entries -
SQL Join Types
Reference for all SQL JOIN types and their use cases.
15 entries -
SQL Query Types Reference
Categories of SQL statements for data definition, manipulation, and control.
5 entries -
SQL Window Functions with Examples
PostgreSQL window functions and explicit frame patterns for ranks, running totals, and neighboring rows.
12 entries -
Time-Series Databases Comparison
Comparison of time-series databases: InfluxDB, TimescaleDB, Prometheus, VictoriaMetrics, QuestDB, and ClickHouse. Storage engines, query languages, compression, and use cases.
6 entries
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