CouchDB vs FaunaDB

Document databases handle distribution in fundamentally different ways. CouchDB uses multi-master replication with eventual consistency, while FaunaDB enforces strict serializability across all nodes. This architectural split affects how you handle conflicts and reason about data correctness, not just where your data lives.

CouchDB launched in 2005 built on the assumption that network partitions are inevitable and conflicts should be detected explicitly. Any node accepts writes independently, and the database surfaces conflicting versions for application-level resolution. FaunaDB arrived in 2016 with strict serializability as the default, using the Calvin protocol to coordinate distributed transactions without traditional two-phase commit overhead.

Modern distributed systems face different tradeoffs. CouchDB prioritizes availability during network partitions, letting applications continue working offline and syncing changes when connectivity returns. FaunaDB prioritizes consistency, coordinating writes globally to prevent conflicts automatically. Your choice determines whether you write conflict resolution code or accept coordination latency.

What is CouchDB?

CouchDB stores JSON documents with automatic revision tracking through _rev fields. Every update requires specifying the current revision, preventing lost updates through optimistic concurrency control. When you attempt to modify a document someone else already changed, the database returns a conflict error instead of silently overwriting data.

The replication protocol runs over HTTP and works bidirectionally between any two CouchDB instances. You configure continuous replication for real-time sync or one-time replication for batch operations. When network connectivity drops, nodes operate independently. When connectivity returns, replication resumes and detects any conflicting edits made on different nodes.

Multi-master architecture means every CouchDB instance accepts writes without coordinating with others. Two users can modify the same document on different continents simultaneously. CouchDB doesn't prevent this scenario. Instead, it preserves both versions and marks the document as conflicted. Your application decides which version wins or implements custom merge logic.

Views provide querying through MapReduce functions written in JavaScript. You define how to extract keys and values from documents, and CouchDB builds indexes incrementally. Queries run against materialized views rather than scanning entire collections. The view engine updates indexes when you access them, not immediately when documents change.

Basic operations use standard HTTP:

// Create document
await fetch('http://localhost:5984/mydb', {
  method: 'POST',
  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 _rev field enforces optimistic locking. If another process updated the document first, your update fails with a 409 conflict. You fetch the latest version and retry with proper conflict resolution.

What is FaunaDB?

FaunaDB organizes documents into collections but enforces strict consistency globally. Every query executes at a specific timestamp, seeing a consistent snapshot of all data at that moment. The database uses optimistic concurrency control to detect conflicts and retries transactions automatically when conflicts occur.

The temporal model preserves every document version with its timestamp. You can query data as it existed at any previous point in time. Time-traveling queries enable auditing, debugging production issues, and implementing event sourcing without additional infrastructure. The database handles storage compaction automatically.

Transactions span collections, databases, and geographic regions with serializable isolation. Reading multiple documents returns a consistent snapshot. Writing multiple documents succeeds atomically or fails completely. The Calvin protocol coordinates distributed transactions without the latency penalties of traditional two-phase commit.

FaunaDB runs exclusively as a managed cloud service. You connect via HTTPS, authenticate with secret keys, and execute queries through FQL (Fauna Query Language). The service handles replication, failover, and scaling without requiring replica set configuration or sharding setup.

Queries use functional composition:

client.query(
  q.Let(
    {
      product: q.Get(q.Match(q.Index('products_by_id'), productId))
    },
    q.Update(q.Select('ref', q.Var('product')), {
      data: {
        stock: q.Subtract(
          q.Select(['data', 'stock'], q.Var('product')),
          quantity
        )
      }
    })
  )
)

The Let binding creates variables. Get retrieves documents. Update modifies fields. The entire operation executes transactionally with serializable isolation. Concurrent transactions attempting to modify the same document execute serially, with automatic retry handling.

CouchDB vs FaunaDB: quick comparison

Consistency models

CouchDB's eventual consistency requires explicit conflict handling:

// CouchDB - handle conflicts manually
async function updateStock(productId, quantity) {
  let attempts = 0;

  while (attempts < 5) {
    try {
      const product = await db.get(productId);

      if (product.stock < quantity) {
        throw new Error('Insufficient stock');
      }

      await db.put({
        ...product,
        stock: product.stock - quantity,
        _rev: product._rev
      });

      return;
    } catch (error) {
      if (error.status === 409) {
        attempts++;
        await new Promise(resolve => setTimeout(resolve, 100));
      } else {
        throw error;
      }
    }
  }
}

The revision check catches concurrent updates. When two clients modify stock simultaneously, one succeeds and the other receives a 409 conflict error. You implement retry logic with backoff. The approach works but requires understanding eventual consistency and handling race conditions explicitly.

FaunaDB's strict serializability eliminates explicit conflict handling:

// FaunaDB - automatic conflict resolution
client.query(
  q.Let(
    {
      product: q.Get(q.Match(q.Index('products_by_id'), productId))
    },
    q.If(
      q.GTE(q.Select(['data', 'stock'], q.Var('product')), quantity),
      q.Update(q.Select('ref', q.Var('product')), {
        data: {
          stock: q.Subtract(
            q.Select(['data', 'stock'], q.Var('product')),
            quantity
          )
        }
      }),
      q.Abort('Insufficient funds')
    )
  )
)

The check and update execute atomically. Concurrent transactions attempting to modify the same product execute serially. FaunaDB handles retries automatically. Stock never goes negative, and updates never get lost without explicit retry logic in application code.

Replication strategies

CouchDB replicates bidirectionally between any nodes:

// CouchDB - continuous bidirectional sync
const sfDB = new PouchDB('http://sf.example.com:5984/inventory');
const londonDB = new PouchDB('http://london.example.com:5984/inventory');

sfDB.replicate.to(londonDB, { live: true, retry: true });
londonDB.replicate.to(sfDB, { live: true, retry: true });

// Both accept writes with local latency
await sfDB.put({
  _id: 'product-1',
  name: 'Widget',
  stock: 100
});

Each datacenter runs an independent instance. Users write to their nearest server. Changes propagate asynchronously. When users in different locations modify the same document, CouchDB detects the conflict and preserves both versions for resolution.

FaunaDB distributes data transparently but coordinates writes globally:

// FaunaDB - single endpoint, global coordination
const client = new faunadb.Client({
  secret: 'your-secret-key',
  domain: 'db.fauna.com'
});

await client.query(
  q.Create(q.Collection('products'), {
    data: { name: 'Widget', stock: 100 }
  })
);

You connect to one logical database regardless of location. FaunaDB routes requests and coordinates writes to maintain consistency. Cross-region writes incur coordination latency. The tradeoff: higher latency for guaranteed consistency without application-level conflict resolution.

Conflict resolution

CouchDB surfaces conflicts explicitly:

// CouchDB - fetch and resolve conflicts
const doc = await db.get('product-1', { conflicts: true });

if (doc._conflicts) {
  const versions = await Promise.all([
    Promise.resolve(doc),
    ...doc._conflicts.map(rev => db.get('product-1', { rev }))
  ]);

  // Domain-specific merge logic
  const resolved = {
    _id: 'product-1',
    name: doc.name,
    stock: Math.max(...versions.map(v => v.stock)),
    _rev: doc._rev
  };

  await Promise.all(
    doc._conflicts.map(rev => db.remove('product-1', rev))
  );

  await db.put(resolved);
}

You fetch conflicting versions, implement merge logic, and delete losers. Different document types need different resolution strategies. CouchDB provides complete control but requires implementing resolution for every conflict scenario.

FaunaDB eliminates conflicts through coordination:

// FaunaDB - concurrent updates execute serially
await Promise.all([
  client.query(
    q.Update(productRef, { data: { stock: 100 } })
  ),
  client.query(
    q.Update(productRef, { data: { stock: 150 } })
  )
]);

Concurrent updates targeting the same document execute serially. The second update sees the first update's results. No conflicts arise because only one serialization order exists. Simpler application code but no ability to implement custom merge strategies.

Query capabilities

CouchDB requires predefined views:

// CouchDB - define view before querying
await db.put({
  _id: '_design/analytics',
  views: {
    sales_by_region: {
      map: function(doc) {
        if (doc.type === 'sale') {
          emit([doc.region, doc.date], doc.amount);
        }
      }.toString(),
      reduce: '_sum'
    }
  }
});

// Query materialized view
const results = await db.query('analytics/sales_by_region', {
  startkey: ['US', '2024-01-01'],
  endkey: ['US', '2024-12-31']
});

Map and reduce functions extract and aggregate data. CouchDB builds indexes incrementally. Queries operate on materialized views. Complex queries require combining multiple views in application code. The approach works well for known access patterns but makes ad-hoc exploration difficult.

FaunaDB supports composing queries dynamically:

// FaunaDB - build queries programmatically
client.query(
  q.Map(
    q.Paginate(
      q.Filter(
        q.Match(q.Index('sales_by_date')),
        q.Lambda(['date', 'ref'],
          q.And(
            q.GTE(q.Var('date'), q.Time('2024-01-01T00:00:00Z')),
            q.LTE(q.Var('date'), q.Time('2024-12-31T23:59:59Z'))
          )
        )
      )
    ),
    q.Lambda(['date', 'ref'], q.Get(q.Var('ref')))
  )
)

You compose FQL functions to build queries. Filter applies predicates. Map transforms results. The query runs at a consistent timestamp, seeing a snapshot of all data. More flexible than predefined views but requires understanding functional composition.

Temporal capabilities

CouchDB's revisions support conflict detection:

// CouchDB - revision history
const doc = await db.get('product-1', {
  revs: true,
  revs_info: true
});

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

Revisions track history for replication and conflict detection. You can fetch specific past revisions but can't query arbitrary historical states. The revision system serves replication needs rather than general temporal queries.

FaunaDB provides full time-travel queries:

// FaunaDB - query any historical timestamp
client.query(
  q.At(
    q.Time('2024-10-15T12:00:00Z'),
    q.Get(q.Match(q.Index('products_by_id'), productId))
  )
)

// Query entire collection at past time
client.query(
  q.At(
    q.Time('2024-10-15T12:00:00Z'),
    q.Map(
      q.Paginate(q.Match(q.Index('all_products'))),
      q.Lambda('ref', q.Get(q.Var('ref')))
    )
  )
)

The At function queries data as it existed at any timestamp. You debug production issues by examining historical state. Implement audit trails without separate event logs. Build temporal analytics showing how data changed over time.

Transaction scope

CouchDB supports atomicity per document:

// CouchDB - atomic single document only
await db.put({
  _id: 'order-1',
  _rev: currentRev,
  customerId: 'customer-123',
  items: [
    { productId: 'product-1', quantity: 2 },
    { productId: 'product-2', quantity: 1 }
  ],
  total: 79.97
});

// No multi-document transactions
// Embed related data or accept eventual consistency

Single document updates succeed or fail atomically. Updating multiple documents has no transactional guarantees. You embed related data in single documents or implement compensating transactions.

FaunaDB transactions span multiple documents:

// FaunaDB - multi-document transactions
client.query(
  q.Do(
    q.Create(q.Collection('orders'), {
      data: { customerId, items, total }
    }),
    q.Map(
      items,
      q.Lambda('item',
        q.Update(productRef, {
          data: {
            stock: q.Subtract(currentStock, quantity)
          }
        })
      )
    )
  )
)

The Do block creates an order and decrements stock for multiple products atomically. Either everything commits or nothing does. No temporary inconsistency. Transactional guarantees simplify application logic but add latency.

Scaling approaches

CouchDB scales by adding master nodes:

# Add more independent CouchDB instances
# Each accepts writes locally
# Replicate between nodes for sync
# No single coordinator or leader

# Example topology:
# US West: CouchDB node 1
# US East: CouchDB node 2
# EU: CouchDB node 3
# All accept writes, sync bidirectionally

You add capacity by deploying more nodes. Each node accepts writes independently. Data syncs between nodes asynchronously. No coordination overhead during writes. Conflicts arise and require resolution.

FaunaDB scales transparently:

// Same code regardless of scale
const client = new faunadb.Client({
  secret: 'your-secret-key'
});

// FaunaDB handles distribution automatically
// Queries work identically at any scale
// No configuration changes needed

FaunaDB distributes data automatically. You don't configure sharding or add nodes manually. The service scales capacity based on usage. Transactions work globally without configuration changes.

Pricing models

CouchDB uses infrastructure-based pricing:

# Self-hosted costs
# 3 servers for redundancy: $150/month
# Storage: $50/month
# Bandwidth: $30/month
# Total: ~$230/month fixed

# Scales by adding servers
# Costs increase linearly with infrastructure

You pay for compute and storage directly. Query volume doesn't affect costs. Scaling requires provisioning more hardware. The model works well for steady workloads but requires capacity planning.

FaunaDB charges per operation:

// Operation-based pricing
client.query(q.Get(q.Match(q.Index('products_by_id'), id)))
// Cost: 1 read operation

client.query(
  q.Map(
    q.Paginate(q.Match(q.Index('orders')), { size: 50 }),
    q.Lambda('ref', q.Get(q.Var('ref')))
  )
)
// Cost: 1 read for index + 50 reads = 51 operations

Reading 50 documents costs 51 operations. Write operations cost more than reads. You optimize queries to minimize operation counts. Operation-based pricing matches variable workloads but makes costs less predictable.

Use case alignment

CouchDB excels for offline-first applications:

// Mobile app with offline sync
const localDB = new PouchDB('tasks');
const remoteDB = new PouchDB('http://example.com:5984/tasks');

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

// Works offline, syncs when connected
await localDB.put({
  _id: 'task-1',
  title: 'Buy groceries',
  completed: false
});

Mobile apps with intermittent connectivity benefit from bidirectional sync. Field service tools operate independently at remote sites. IoT devices collect data at the edge and sync to datacenters. Peer-to-peer systems distribute data without central coordination.

FaunaDB works better for applications requiring strong consistency:

// Payment processing with strict consistency
client.query(
  q.If(
    q.GTE(currentBalance, amount),
    q.Do(
      q.Update(accountRef, {
        data: { balance: q.Subtract(currentBalance, amount) }
      }),
      q.Create(q.Collection('transfers'), {
        data: { amount, timestamp: q.Now() }
      })
    ),
    q.Abort('Insufficient funds')
  )
)

Financial systems prevent double-charges through serializable transactions. E-commerce platforms avoid overselling inventory. Collaborative tools maintain consistent views across users. Serverless applications scale automatically with usage-based pricing.

Operational considerations

CouchDB requires traditional database administration:

tar -czf backup.tar.gz /var/lib/couchdb

tar -xzf backup.tar.gz -C /var/lib/couchdb

curl http://localhost:5984/_active_tasks

curl -X POST http://localhost:5984/mydb/_compact

Database administrators manage backups, monitor replication lag, trigger compaction, and plan capacity. The operational model follows traditional database practices with mature tooling.

FaunaDB handles operations automatically:

// Monitor through dashboard
// Set rate limits if needed
// Automatic backups
// Automatic scaling
// No maintenance windows

The managed service handles backups, scaling, and upgrades automatically. You monitor through the dashboard and adjust rate limits. No operational expertise required. The tradeoff: less control and no self-hosting option.

Final thoughts

CouchDB and FaunaDB solve distribution differently. CouchDB embraces eventual consistency with explicit conflict detection, letting any node accept writes independently. It works best for offline-first applications, mobile sync, and scenarios where availability during network partitions matters more than immediate consistency.

FaunaDB enforces strict serializability globally, coordinating writes automatically to prevent conflicts. It simplifies application logic for financial systems, e-commerce, and scenarios where strong consistency prevents entire classes of bugs.

If your application needs offline support and geographic distribution without write bottlenecks, CouchDB's multi-master replication fits naturally. If you need strong consistency without operational complexity, FaunaDB's managed service and automatic coordination eliminate conflict resolution code.

Most teams pick based on consistency requirements and operational preferences. CouchDB requires handling conflicts explicitly but provides complete control over infrastructure. FaunaDB eliminates conflicts through coordination but runs only as a managed service. Your choice depends on whether your application can tolerate eventual consistency and whether your team wants to manage database infrastructure.

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