CouchDB vs MongoDB

Document databases let you store JSON-like data without predefined schemas, but how they handle replication and conflicts makes all the difference. CouchDB uses multi-master replication with automatic conflict detection, while MongoDB relies on primary-secondary replication with manual failover. This choice affects how you build distributed applications, not just where data lives.

CouchDB arrived in 2005 as an Apache project focused on eventual consistency and offline-first architectures. The database assumes network partitions will happen and embraces conflict resolution as a core feature. MongoDB launched in 2009 to serve high-traffic web applications that needed flexible schemas and fast queries, prioritizing single-master consistency over partition tolerance.

Today's distributed applications face different architectural decisions. CouchDB syncs data bidirectionally between nodes, letting any instance accept writes independently. MongoDB replicates from primary to secondary nodes, routing all writes through a single leader. Your database choice determines how you handle network failures, mobile sync, and geographic distribution.

What is CouchDB?

CouchDB stores documents as JSON with built-in versioning through a revision system. Every document has a _rev field that tracks its history. When you update a document, CouchDB requires the current revision, preventing accidental overwrites. The database returns conflicts explicitly rather than silently discarding data.

The replication protocol works over HTTP, making it straightforward to sync databases between servers, mobile devices, or browsers. Any CouchDB instance can replicate to any other. You configure continuous replication for real-time sync or trigger one-time replication for batch updates. The protocol handles network interruptions gracefully by resuming where it left off.

Multi-master replication means every node accepts writes independently. When two nodes modify the same document concurrently, CouchDB detects the conflict and flags it for resolution. Your application decides which version wins or merges changes programmatically. The database never loses data silently.

Views in CouchDB use MapReduce functions written in JavaScript. You define how to emit keys and values from documents, and CouchDB builds indexes incrementally. Queries run against these materialized views rather than scanning collections. The view engine updates indexes lazily when you query them.

Basic CRUD operations use HTTP directly:

// Create a document
const response = await fetch('http://localhost:5984/mydb', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    type: 'product',
    name: 'Widget',
    price: 29.99
  })
});

// Update requires current revision
await fetch(`http://localhost:5984/mydb/${docId}`, {
  method: 'PUT',
  body: JSON.stringify({
    _rev: currentRev,
    type: 'product',
    name: 'Widget',
    price: 34.99
  })
});

The HTTP API makes CouchDB accessible from any language or platform. The _rev field enforces optimistic locking. If another process updated the document first, your update fails with a conflict error. You fetch the latest revision and retry.

What is MongoDB?

MongoDB organizes documents into collections that live within databases. Documents use BSON format, an extended JSON that supports additional types like dates and binary data. Collections don't enforce schemas, but you can define validation rules that check documents on insert or update.

Replication uses a primary-secondary model with one writable primary and multiple read-only secondaries. The primary handles all write operations and logs them to an oplog. Secondaries tail the oplog and apply operations asynchronously. When the primary fails, remaining nodes elect a new primary through a voting process.

Write operations go to the primary by default. You configure write concern to specify how many nodes must acknowledge a write before returning success. Read operations can target the primary for consistency or secondaries for lower latency. Read preference lets you balance freshness against load distribution.

Queries use a rich expression language with operators for filtering, sorting, and aggregating. The aggregation framework processes documents through pipelines of transformation stages. MongoDB's query planner chooses execution strategies based on available indexes and collection statistics.

Working with documents feels imperative:

// Insert a document
const result = await db.collection('products').insertOne({
  type: 'product',
  name: 'Widget',
  price: 29.99,
  createdAt: new Date()
});

// Update by ID
await db.collection('products').updateOne(
  { _id: result.insertedId },
  { $set: { price: 34.99 } }
);

The driver handles connection pooling and protocol details. Updates use atomic operators like $set, $inc, and $push. No revision tracking exists unless you implement it manually. Concurrent updates to the same document happen in sequence at the primary.

CouchDB vs MongoDB: quick comparison

Replication models create different guarantees

The replication difference became clear when I built a mobile app with offline support. CouchDB's bidirectional sync worked naturally for my use case:

// CouchDB - continuous bidirectional sync
const localDB = new PouchDB('tasks');
const remoteDB = new PouchDB('http://example.com:5984/tasks');

const sync = localDB.sync(remoteDB, {
  live: true,
  retry: true
});

sync.on('change', (info) => {
  console.log('Synced:', info.docs);
});

// Both databases accept writes independently
await localDB.put({
  _id: 'task-1',
  title: 'Buy groceries',
  completed: false
});

The mobile app wrote to its local PouchDB database. When network returned, sync pushed changes to CouchDB and pulled remote changes. Both sides accepted writes simultaneously. If users edited the same task offline, CouchDB detected the conflict and let my app resolve it.

MongoDB's primary-secondary model complicated offline scenarios:

// MongoDB - writes require connection to primary
try {
  await db.collection('tasks').insertOne({
    title: 'Buy groceries',
    completed: false
  });
} catch (error) {
  // Primary unreachable - write fails
  // Must queue writes locally and retry
}

// Need custom sync logic
const pendingWrites = await localCache.getPending();
for (const write of pendingWrites) {
  try {
    await db.collection('tasks').insertOne(write);
    await localCache.markSynced(write.id);
  } catch (error) {
    // Handle conflicts manually
  }
}

Without a primary connection, writes failed immediately. I built a local queue for offline writes and sync logic to replay them later. Conflict detection required tracking document versions manually. MongoDB worked great when connected but needed significant custom code for offline support.

Conflict resolution exposes different tradeoffs

That offline sync revealed how each database handles conflicts. CouchDB makes conflicts explicit and requires resolution:

// CouchDB - fetch document with conflicts
const doc = await db.get('task-1', { conflicts: true });

if (doc._conflicts) {
  console.log('Conflicts detected:', doc._conflicts);

  // Fetch conflicting revisions
  const conflicts = await Promise.all(
    doc._conflicts.map(rev => db.get('task-1', { rev }))
  );

  // Resolve by choosing winner or merging
  const resolved = {
    _id: 'task-1',
    _rev: doc._rev,
    title: doc.title,
    completed: conflicts.some(c => c.completed)
  };

  // Delete losing revisions
  await Promise.all(
    doc._conflicts.map(rev => 
      db.remove('task-1', rev)
    )
  );

  await db.put(resolved);
}

The conflicts: true option returns conflicting revision IDs. I fetched each version, implemented merge logic, and deleted losers. CouchDB preserved all versions until I explicitly resolved them. My app controlled conflict resolution strategy based on document semantics.

MongoDB's single-master model eliminates conflicts at the cost of availability:

// MongoDB - last write wins at primary
// No built-in conflict detection

// Custom conflict handling requires application logic
const existingDoc = await db.collection('tasks').findOne({ _id: taskId });

if (existingDoc && existingDoc.updatedAt > localDoc.updatedAt) {
  // Remote is newer - merge or discard local changes
  const merged = mergeDocuments(existingDoc, localDoc);
  await db.collection('tasks').replaceOne(
    { _id: taskId },
    merged
  );
} else {
  // Local is newer or document doesn't exist
  await db.collection('tasks').replaceOne(
    { _id: taskId },
    localDoc,
    { upsert: true }
  );
}

I tracked updatedAt timestamps manually to detect conflicts. The database provided no help determining which write came first across distributed clients. Last write wins meant data could silently disappear if I didn't implement careful conflict detection.

HTTP versus binary protocols affect integration

Those conflict patterns reflected deeper protocol differences. CouchDB's HTTP API made integration straightforward:

curl -X POST http://localhost:5984/products \
  -H "Content-Type: application/json" \
  -d '{"name":"Widget","price":29.99}'

{"ok":true,"id":"abc123","rev":"1-xyz789"}

curl http://localhost:5984/products/abc123

{
  "_id": "abc123",
  "_rev": "1-xyz789",
  "name": "Widget",
  "price": 29.99
}

Any HTTP client worked without special drivers. I debugged with curl commands. The REST interface followed web conventions. Built-in authentication used HTTP Basic or cookie sessions. Proxies, load balancers, and caches worked naturally with HTTP semantics.

MongoDB's binary protocol required language-specific drivers:

// MongoDB - requires official driver
const { MongoClient } = require('mongodb');

const client = new MongoClient('mongodb://localhost:27017', {
  maxPoolSize: 50,
  minPoolSize: 10
});

await client.connect();
const db = client.db('mydb');

// Can't use curl or standard HTTP tools

The binary protocol improved performance but complicated debugging. I needed MongoDB-specific tools like mongosh or Compass. Connection pooling, authentication, and compression happened inside the driver. Building custom tooling required understanding BSON encoding and the wire protocol.

Views versus indexes serve different query patterns

The protocol difference extended to how queries work. CouchDB's MapReduce views required defining transformations upfront:

// CouchDB - define a view for querying
await db.put({
  _id: '_design/products',
  views: {
    by_category: {
      map: function(doc) {
        if (doc.type === 'product') {
          emit(doc.category, {
            name: doc.name,
            price: doc.price
          });
        }
      }.toString()
    }
  }
});

// Query the view
const result = await db.query('products/by_category', {
  key: 'electronics',
  include_docs: false
});

The map function emitted keys and values from documents. CouchDB built an index incrementally. Queries ran against the materialized view, not raw documents. Complex queries required designing views in advance or combining multiple view queries in application code.

MongoDB's ad-hoc queries worked against any field:

// MongoDB - query without predefined views
const products = await db.collection('products').find({
  category: 'electronics',
  price: { $lt: 100 }
}).sort({ price: 1 }).toArray();

// Aggregation for complex operations
const summary = await db.collection('products').aggregate([
  { $match: { category: 'electronics' } },
  { $group: {
    _id: '$manufacturer',
    avgPrice: { $avg: '$price' },
    count: { $sum: 1 }
  }},
  { $sort: { avgPrice: -1 } }
]).toArray();

I queried any combination of fields without preparation. Indexes improved performance but weren't required for functionality. The aggregation pipeline composed complex transformations. MongoDB's flexibility accelerated development but required careful index design for production performance.

Geographic distribution strategies diverge

Those query patterns revealed different scaling approaches. CouchDB's multi-master design made geographic distribution natural:

// CouchDB - replicate between regions
// US datacenter
const usDB = new PouchDB('http://us.example.com:5984/products');

// EU datacenter  
const euDB = new PouchDB('http://eu.example.com:5984/products');

// Bidirectional replication
usDB.replicate.to(euDB, { live: true });
euDB.replicate.to(usDB, { live: true });

// Both accept writes locally
await usDB.put({ _id: 'product-1', name: 'Widget', region: 'US' });
await euDB.put({ _id: 'product-2', name: 'Gadget', region: 'EU' });

Each datacenter ran an independent CouchDB instance. Users wrote to their nearest server with local latency. Changes replicated asynchronously across regions. Conflicts arose when users in different regions modified the same document, but CouchDB detected and flagged them.

MongoDB's primary-secondary model concentrated writes in one region:

// MongoDB - configure replica set across regions
// Primary in US
// Secondaries in EU and Asia

// All writes go to US primary
await db.collection('products').insertOne({
  name: 'Widget',
  region: 'US'
});
// Write latency: ~5ms local

// EU clients also write to US primary  
await db.collection('products').insertOne({
  name: 'Gadget',
  region: 'EU'
});
// Write latency: ~100ms cross-region

Writes from EU clients crossed the Atlantic to reach the US primary. Read-only secondaries in EU served local queries but couldn't accept writes. Failover promoted an EU secondary to primary if US failed, but this required election time. Geographic distribution improved read latency but writes remained centralized.

Document versioning serves different purposes

That distribution model connected to how each database tracks changes. CouchDB's revision system enables replication and conflict detection:

// CouchDB - revisions track document history
const doc = await db.get('product-1');
console.log(doc._rev); // "3-abc123def456"

// Revision tree tracks all branches
const revs = await db.get('product-1', {
  revs: true,
  revs_info: true
});

console.log(revs._revs_info);
// [
//   { rev: '3-abc', status: 'available' },
//   { rev: '2-def', status: 'available' },
//   { rev: '1-xyz', status: 'available' }
// ]

Every update incremented the revision number. The revision tree tracked branching when conflicts occurred. CouchDB used revisions to determine which changes needed replication. Old revisions remained accessible for conflict resolution or auditing.

MongoDB generates _id values but doesn't track versions:

// MongoDB - no revision tracking
const doc = await db.collection('products').findOne({ _id: productId });
console.log(doc); // { _id: ObjectId('...'), name: 'Widget' }

// To track versions, implement manually
await db.collection('products').updateOne(
  { _id: productId },
  {
    $set: { name: 'Updated Widget' },
    $inc: { version: 1 }
  }
);

Documents had no built-in version field. Implementing change tracking required application code. The change streams feature captured recent operations but didn't preserve full history. Auditing required maintaining separate collections or external event logs.

Transactions scope differently

Version tracking affected transactional capabilities. CouchDB provides atomicity per document only:

// CouchDB - atomic single document updates
await db.put({
  _id: 'order-1',
  _rev: currentRev,
  items: ['product-1', 'product-2'],
  total: 59.98,
  status: 'pending'
});

// Multi-document atomicity requires application coordination
const order = await db.get('order-1');
const inventory = await db.get('inventory-1');

// No guarantee these execute atomically
await db.put({ ...order, status: 'confirmed' });
await db.put({ 
  ...inventory, 
  quantity: inventory.quantity - order.items.length 
});

Single document updates succeeded or failed atomically. Updating multiple documents had no transactional guarantees. Inventory might decrement even if the order update failed. I designed around this by embedding related data in single documents or implementing two-phase commit patterns.

MongoDB supports multi-document transactions within replica sets:

// MongoDB - atomic multi-document updates
const session = client.startSession();
session.startTransaction();

try {
  await db.collection('orders').updateOne(
    { _id: orderId },
    { $set: { status: 'confirmed' } },
    { session }
  );

  await db.collection('inventory').updateOne(
    { _id: inventoryId },
    { $inc: { quantity: -itemCount } },
    { session }
  );

  await session.commitTransaction();
} catch (error) {
  await session.abortTransaction();
}

The transaction ensured both updates committed together or neither did. This capability simplified modeling financial operations, inventory management, and other scenarios requiring atomicity across documents. The feature came with performance overhead but solved real architectural problems.

Administration complexity varies significantly

Transaction support reflected overall operational differences. CouchDB required minimal administration:

# CouchDB - start server
couchdb

# Configure through Fauxton web interface
# http://localhost:5984/_utils

# Backup is file copy
tar -czf backup.tar.gz /var/lib/couchdb

I started CouchDB, configured it through the web UI, and it ran. Replication required no cluster coordination. Backups copied database files directly. Compaction reclaimed space from old revisions on a schedule. The operational model fit small teams without dedicated database administrators.

MongoDB required ongoing operational attention:

# MongoDB - configure replica set
mongosh --eval "rs.initiate()"

# Add secondaries
mongosh --eval "rs.add('mongodb-2:27017')"
mongosh --eval "rs.add('mongodb-3:27017')"

# Monitor replication lag
mongosh --eval "rs.printReplicationInfo()"

# Backup with mongodump
mongodump --uri="mongodb://localhost:27017/mydb"

I initialized replica sets, configured member priority, monitored replication lag, and planned failover scenarios. Sharding added complexity with config servers and mongos routers. Index management, query optimization, and performance tuning required expertise. Atlas managed service reduced this burden but cost more than running CouchDB.

Final thoughts

CouchDB and MongoDB both store flexible, JSON-like data, but they take very different paths in how they sync and manage it.

CouchDB is built for a world where connections can drop. Its multi-master setup lets every node accept writes and sync later, making it great for offline or distributed systems. The simple HTTP API and built-in sync make it easy to connect devices and recover from conflicts without losing data. If your app needs to work offline, across regions, or with unreliable networks, CouchDB is the better fit.

MongoDB, on the other hand, focuses on speed, consistency, and powerful queries. Its primary-secondary model keeps data predictable and stable, while features like transactions and aggregation simplify complex workloads. For always-connected web apps, analytics, or systems that value consistency over availability, MongoDB is a strong choice.

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