{
    "api": "YAML JSON TOON Database",
    "version": "1.0.0",
    "format": "json",
    "dataset": {
        "id": 10,
        "slug": "database-comparison",
        "title": "Database Systems Comparison",
        "description": "Comparison of database types: Relational, Document, Key-Value, Column-Family, Graph, and Vector databases with examples and best-fit use cases.",
        "summary": "The main families of database systems and what each is best for: relational, document, key-value, column-family, graph and vector, extended with time-series, search, distributed SQL, in-memory and multi-model systems. Each row gives the data model and example products.",
        "use_when": [
            "You are picking a category of database for a new system.",
            "You need quick examples of each category."
        ],
        "caveats": [
            "Categories overlap and products borrow from each other; PostgreSQL stores JSON and supports vectors, for example.",
            "Examples are illustrative, not rankings or recommendations."
        ],
        "related": [
            "nosql-databases-overview",
            "acid-vs-base",
            "sql-query-types",
            "cap-theorem"
        ],
        "category": "Database Types",
        "category_slug": "database-types",
        "tags": "database,sql,nosql,comparison",
        "view_count": 0,
        "created_at": 1777673262,
        "updated_at": 1777673262,
        "entry_count": 11,
        "fields": [
            {
                "section": "databases",
                "field": "type",
                "type": "string",
                "example": "Relational"
            },
            {
                "section": "databases",
                "field": "examples",
                "type": "string",
                "example": "PostgreSQL, MySQL, SQLite"
            },
            {
                "section": "databases",
                "field": "model",
                "type": "string",
                "example": "Tables with rows and columns"
            },
            {
                "section": "databases",
                "field": "best_for",
                "type": "string",
                "example": "Structured data, ACID transactions, complex que…"
            }
        ]
    },
    "data": {
        "databases": [
            {
                "type": "Relational",
                "examples": "PostgreSQL, MySQL, SQLite",
                "model": "Tables with rows and columns",
                "best_for": "Structured data, ACID transactions, complex queries"
            },
            {
                "type": "Document",
                "examples": "MongoDB, CouchDB",
                "model": "JSON-like documents",
                "best_for": "Flexible schemas, rapid development, hierarchical data"
            },
            {
                "type": "Key-Value",
                "examples": "Redis, DynamoDB",
                "model": "Key-value pairs",
                "best_for": "Caching, session storage, high throughput"
            },
            {
                "type": "Column-Family",
                "examples": "Cassandra, HBase",
                "model": "Columns grouped in families",
                "best_for": "Time-series, write-heavy workloads, big data"
            },
            {
                "type": "Graph",
                "examples": "Neo4j, ArangoDB",
                "model": "Nodes and edges",
                "best_for": "Relationship-heavy data, social networks, recommendations"
            },
            {
                "type": "Vector",
                "examples": "Pinecone, Weaviate, pgvector",
                "model": "High-dimensional vectors",
                "best_for": "Semantic search, similarity matching, RAG"
            },
            {
                "type": "Time-Series",
                "examples": "InfluxDB, TimescaleDB, Prometheus",
                "model": "Timestamped measurements optimized for append and range queries",
                "best_for": "Metrics, IoT sensor data, monitoring and financial ticks"
            },
            {
                "type": "Search Engine",
                "examples": "Elasticsearch, OpenSearch, Apache Solr",
                "model": "Inverted indexes over documents with relevance scoring",
                "best_for": "Full-text search, log analytics, faceted navigation"
            },
            {
                "type": "Distributed SQL (NewSQL)",
                "examples": "CockroachDB, TiDB, Google Spanner",
                "model": "Relational tables with SQL and distributed transactions",
                "best_for": "Horizontally scaled applications that need SQL and strong consistency"
            },
            {
                "type": "In-Memory",
                "examples": "Redis, Memcached, SAP HANA",
                "model": "Data held primarily in RAM",
                "best_for": "Caching, session stores, leaderboards, low-latency analytics"
            },
            {
                "type": "Multi-Model",
                "examples": "ArangoDB, Azure Cosmos DB, SurrealDB",
                "model": "Several data models behind one engine",
                "best_for": "Applications mixing document, graph and key-value access"
            }
        ]
    }
}