117 lines
5.3 KiB
Markdown
117 lines
5.3 KiB
Markdown
# 🥬 MongoDB
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## Overview
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MongoDB is the most widespread document-oriented NoSQL database. It stores data as BSON (binary JSON) documents with a flexible schema. Suitable for applications with rapid development where the schema frequently migrates or is diverse.
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## Data model
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- **Database** → Collection → Document (JSON/BSON)
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- **Document** — fields with key-value, nested objects, arrays
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- **Flexible schema** — each document can have different fields (but not recommended)
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- **ObjectID** — default primary key (12-byte: timestamp + machine + PID + counter)
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## Architecture
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```
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mongod (individual node)
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├── WiredTiger storage engine (default since 3.2)
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│ ├── B-Tree indexes (B-Tree, not LSM)
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│ ├── MVCC (snapshot isolation)
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│ ├── Compression (zlib, snappy, zstd)
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│ └── Cache (WiredTiger internal cache)
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├── Replication (replica set)
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│ ├── Primary (all writes)
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│ └── Secondary (replication, optional reads)
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└── Sharding (cluster)
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├── mongos (router)
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├── Config servers (metadata)
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└── Shards (replica sets)
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```
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### Replica set
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- Primary node = all writes, secondary = replication (oplog)
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- Automatic failover (election among secondaries)
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- Up to 50 nodes in a replica set, max 7 voting nodes
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- Read preference: primary (default), primaryPreferred, secondary, secondaryPreferred, nearest
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### Sharding
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- Shard key = decisive for distribution
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- **Range sharding** — close data on the same shard (good for range queries, risk of hot spots)
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- **Hashed sharding** — even distribution (good for write throughput, bad for range queries)
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- **Zoned sharding** — data placed according to zones (geo-distribution, compliance)
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## Index types
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| Type | Description |
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|------|-------------|
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| **Single field** | Standard B-Tree index |
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| **Compound** | Multiple fields in index (order matters) |
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| **Multikey** | Index on array field — each value separately |
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| **Text** | Full-text search |
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| **Geospatial (2d, 2dsphere)** | Geo queries (near, within, intersect) |
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| **Hashed** | For hashed sharding |
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| **TTL** | Automatic document deletion after expiration |
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| **Wildcard** | Index on unknown/irregular fields |
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## Aggregation pipeline
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MongoDB pipeline framework for data transformations:
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```javascript
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db.orders.aggregate([
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{ $match: { status: "shipped" } },
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{ $group: { _id: "$customer_id", total: { $sum: "$amount" } } },
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{ $sort: { total: -1 } },
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{ $limit: 10 }
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])
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```
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## Recommendations — where MongoDB is better
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| Area | MongoDB | Competition | Why MongoDB |
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|------|---------|-------------|-------------|
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| **Flexible schema** | Schema-less, changes without migration | PostgreSQL (ALTER TABLE + migration) | Rapid development, MVP, frequent model changes |
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| **JSON / documents** | Native BSON, nested objects | PostgreSQL (jsonb, but lacks $ operators) | Simpler object mapping from code |
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| **Horizontal scaling** | Native sharding (mongos + config) | MySQL (Vitess external) | Built-in, simple to set up |
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| **Geo-distribution** | Zoned sharding, replica set per region | Cassandra (AP model, different philosophy) | CP from CAP, consistency + distribution |
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| **Aggregation** | Aggregation pipeline, $lookup (LEFT JOIN) | PostgreSQL (SQL JOINs, more powerful) | Useful for denormalized data |
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| **Development speed** | ORM-like (Mongoose), natural JSON | SQL (schema first, migrations) | Fastest time-to-market |
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### When to use MongoDB
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- **Rapid development / MVP** — schema evolves frequently, no migrations
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- **Catalog data** — products with varying attributes (e-commerce, marketplace)
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- **Content management** — diverse content (blog, CMS, headless CMS)
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- **Real-time analytics** — aggregations, dashboards, event data
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- **IoT / sensor data** — diverse message structures
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- **Mobile applications** — JSON documents naturally map to API responses
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### When to use something else
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- **Financial transactions** → PostgreSQL (ACID, referential integrity)
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- **Complex reports / JOINs** → PostgreSQL or ClickHouse
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- **Relationship data (friends, follows)** → Neo4j (graph DB)
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- **High-throughput writes** → Cassandra (AP model, no master bottleneck)
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- **Small data, single server** → SQLite (simpler, no daemon)
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## MongoDB licensing
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MongoDB changed its license in 2018 from GNU AGPL v3 to **SSPL** (Server Side Public License):
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| Variant | License | Price | Conditions |
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|---------|---------|-------|------------|
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| **MongoDB Community** | SSPL | Free | SSPL: if you offer MongoDB as a managed service, you must release the entire stack (incl. orchestration, monitoring) as open source. Internal use without restrictions |
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| **MongoDB Enterprise Advanced** | Commercial | ~$10,000/server/year (Atlas: pay-per-use) | Enterprise features (LDAP, Kerberos, auditing, encryption), 24/7 support |
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| **MongoDB Atlas** | Managed | Pay-per-use (~$0.10-5.00/hour depending on instance) | Fully managed, multi-cloud, auto-scaling, backup, monitoring |
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**Impact**: SSPL is similar to Redis model — self-hosted internal use without restrictions, cloud providers (AWS, Azure) cannot offer MongoDB as a managed service without commercial agreement. Alternative: **FerretDB** (open source proxy compatible with MongoDB wire protocol).
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## Sources
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References, books, and standards: [sources/databases/sources.en.md](sources/databases/sources.en.md)
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*Last revision: 2026-06-03*
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