Document Data vs Key–Value Data

Document Data vs Key–Value Data — Technical Comparison

A concise comparison to help you choose the right NoSQL model for your workload.

Overview

Both document databases and key–value stores are types of NoSQL data stores. They differ primarily in data model complexity, query capabilities, and typical use cases. Below you'll find a practical comparison across structure, querying, performance, scaling, and recommended scenarios.

1. Data Structure and Format

Document Data

Stored as self-describing documents (JSON, BSON, or XML). Supports nested objects and arrays.

{
  "id": "123",
  "name": "Hany",
  "address": {
    "city": "Riyadh",
    "zip": "12345"
  }
}

Key–Value Data

The simplest model: a unique key mapped to an opaque value (string, blob, or JSON). The store doesn't interpret the value.

Key: user:123
Value: {"name":"Hany","city":"Riyadh"}

2. Query Capabilities

Document databases provide rich queries (filters, projections, aggregations, and the ability to query nested fields). Example (Cosmos DB Core SQL-like):

SELECT c.name
FROM c
WHERE c.address.city = "Riyadh"

Key–value stores typically only support get/put/delete by key. Any filtering or search must be implemented by the application or via an additional indexing/search layer.

3. Schema Flexibility

Document: Schema-free but structured — each document can differ. Good for schema evolution without migrations.

Key–Value: No schema awareness — values are opaque to the store.

4. Performance Characteristics

Key–value stores are the fastest for direct lookups (O(1) access) and are ideal for caching, session stores, and similar use cases. Document databases are highly performant for complex reads and queries but may be slightly slower than pure key–value stores for simple key lookups.

5. Use Cases

• Document DB: user profiles, product catalogs, content management, logs, telemetry where querying by fields matters.
• Key–Value: caching (Redis), session stores, feature flags, tokens, and any workload that needs extremely fast lookups by key.

6. Global Scaling & Distribution

Both models can scale globally. Document databases (e.g., Azure Cosmos DB, MongoDB) are designed to scale horizontally while preserving rich query capabilities and often include automatic indexing and multi-region replication. Key–value stores scale easily due to simple data model and partitioning strategies, making them ideal for very high throughput, low-latency use cases.

Feature	Document Data	Key–Value Data
Structure	JSON documents (nested)	Key + opaque value (blob)
Querying	Rich, field-level queries	Lookup by key only
Schema	Flexible, self-describing	No schema awareness
Performance	High (complex reads)	Extremely high (fastest for key lookup)
Best for	User profiles, catalogs, logs	Caching, sessions, tokens
DBMSs	MongoDB, Couchbase, Amazon DocumentDB, Cosmos DB (API for MongoDB)	Redis, Amazon DynamoDB (key–value usage), Riak KV
Azure DBMSs	Azure Cosmos DB (Core/SQL, Mongo, Cassandra APIs), Azure Cache for Redis JSON features	Azure Cache for Redis, Azure Table Storage (legacy key–value)
Cost	Often higher per GB due to indexing and richer queries; good for flexible workloads	Often cheaper per GB for simple key access; optimal for heavy-read/write caches