FaunaDB vs MongoDB

Database architecture determines how your application scales and what guarantees you get when things go wrong. FaunaDB provides distributed transactions with strict serializable isolation, while MongoDB offers flexible documents with eventual consistency by default. This difference affects data integrity and performance characteristics, not just query syntax.

FaunaDB launched in 2016 as a distributed database built on Calvin, a protocol for consistent transactions across data centers. The system guarantees ACID semantics globally without coordination overhead. MongoDB appeared in 2009 to power web applications that needed flexible schemas and horizontal scaling, trading immediate consistency for availability and partition tolerance.

Modern applications often choose based on consistency requirements and operational complexity. FaunaDB handles consistency automatically through its temporal model and distributed transactions. MongoDB gives you control over consistency levels and replica configuration. Your choice affects how you handle conflicts, design schemas, and reason about concurrent updates.

What is FaunaDB?

FaunaDB stores documents in collections but enforces strict consistency across all operations. Every transaction sees a snapshot of data at a specific point in time. The database automatically resolves conflicts using optimistic concurrency control. You write queries in FQL (Fauna Query Language), a functional language that composes operations.

The temporal model means every document version remains accessible. You can query data as it existed at any previous timestamp. Time-traveling queries let you audit changes, implement event sourcing, or debug issues by examining historical state. The database handles storage reclamation automatically.

Transactions work across collections, databases, and even geographic regions. When you read multiple documents, you see a consistent snapshot. When you write, either all changes commit or none do. The system prevents anomalies like lost updates, dirty reads, and write skew without requiring you to manage locks.

FaunaDB runs as a managed service across multiple cloud providers. You connect via HTTPS, authenticate with secrets, and pay for operations and storage. The service handles replication, backups, and scaling automatically. No servers to provision, no replica sets to configure.

Queries use functional composition to build complex operations:

client.query(
  q.Let(
    {
      account: q.Get(q.Match(q.Index('accounts_by_id'), accountId))
    },
    q.Update(q.Select('ref', q.Var('account')), {
      data: {
        balance: q.Add(
          q.Select(['data', 'balance'], q.Var('account')),
          100
        )
      }
    })
  )
)

The Let binding creates variables. Get retrieves documents. Update modifies fields. The entire operation runs in a transaction with serializable isolation. If another transaction modifies the account concurrently, FaunaDB detects the conflict and retries automatically.

What is MongoDB?

MongoDB stores BSON documents in collections without enforcing schemas. You insert whatever structure makes sense for your application. Fields can vary between documents in the same collection. The database provides indexes, aggregation pipelines, and a rich query language for filtering and transforming data.

The default consistency model uses eventually consistent reads from secondaries. Primary nodes handle writes and propagate changes to replicas asynchronously. You can configure read preferences and write concerns to balance consistency, availability, and latency. Causal consistency lets you track operations across sessions.

Transactions arrived in MongoDB 4.0 for replica sets and 4.2 for sharded clusters. Multi-document transactions provide ACID semantics within a single replica set or shard. Cross-shard transactions work but add coordination overhead. Most applications structure data to avoid needing transactions across shards.

MongoDB runs on your infrastructure or through Atlas, the managed service. You configure replica sets for redundancy, shard clusters for horizontal scaling, and connection pools for performance. The operational model gives you control over deployment topology and resource allocation.

Queries use a JSON-like syntax with operators:

const session = client.startSession();
session.startTransaction();

try {
  await db.collection('accounts').updateOne(
    { _id: accountId },
    { $inc: { balance: 100 } },
    { session }
  );

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

You explicitly start sessions and transactions. updateOne modifies the document. The session ensures all operations commit together or roll back. You handle transaction lifecycle and errors manually.

FaunaDB vs MongoDB: quick comparison

Consistency guarantees shape application logic

The consistency difference became obvious when I built a payment system. FaunaDB's strict serializability meant I could write straightforward code without worrying about race conditions:

// FaunaDB - atomic balance update
client.query(
  q.If(
    q.GTE(q.Select(['data', 'balance'], q.Get(accountRef)), amount),
    q.Do(
      q.Update(accountRef, {
        data: { balance: q.Subtract(currentBalance, amount) }
      }),
      q.Create(q.Collection('transfers'), { data: { amount } })
    ),
    q.Abort('Insufficient funds')
  )
)

The check-and-update happens atomically. No other transaction can modify the balance between the check and the update. If two transfers try to execute simultaneously, FaunaDB serializes them. One succeeds, the other sees the updated balance and either succeeds or aborts. No lost updates, no race conditions.

MongoDB required explicit transaction handling and careful error management:

// MongoDB - manual transaction for balance update
const session = client.startSession();
session.startTransaction({
  readConcern: { level: 'snapshot' },
  writeConcern: { w: 'majority' }
});

try {
  const account = await db.collection('accounts').findOne(
    { _id: accountId },
    { session }
  );

  if (account.balance < amount) {
    throw new Error('Insufficient funds');
  }

  await db.collection('accounts').updateOne(
    { _id: accountId },
    { $inc: { balance: -amount } },
    { session }
  );

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

I had to start sessions, configure read and write concerns, and handle commit failures. The transaction might abort due to conflicts, requiring retry logic. Without proper read concern, another transaction could read stale data. The code works but requires understanding MongoDB's consistency model deeply.

Query language affects development velocity

That transaction handling difference extended to query complexity. FaunaDB's functional composition let me build complex operations by combining simple pieces:

// FaunaDB - find users with recent purchases over $100
client.query(
  q.Map(
    q.Paginate(q.Match(q.Index('users_all'))),
    q.Lambda('userRef',
      q.Let(
        {
          purchases: q.Sum(
            q.Map(recentPurchases, 
              q.Lambda('p', q.Select(['data', 'amount'], q.Var('p')))
            )
          )
        },
        q.If(q.GT(q.Var('purchases'), 100), q.Get(q.Var('userRef')), null)
      )
    )
  )
)

The query reads dense but composes logically. Map iterates users. Let binds their purchases. Sum aggregates amounts. If filters users. Each operation returns data that feeds the next. The entire query runs in a single transaction at a consistent timestamp.

MongoDB's aggregation pipeline felt more imperative:

// MongoDB - find users with recent purchases over $100
db.collection('users').aggregate([
  {
    $lookup: {
      from: 'purchases',
      localField: '_id',
      foreignField: 'userId',
      as: 'purchases'
    }
  },
  {
    $addFields: {
      totalSpent: { $sum: '$purchases.amount' }
    }
  },
  {
    $match: { totalSpent: { $gt: 100 } }
  }
])

The pipeline stages transform data step by step. $lookup joins collections. $addFields computes the total. $match filters results. The syntax feels more familiar to SQL developers but doesn't guarantee consistency across stages. If purchases change during execution, you might see inconsistent results.

Indexes work differently between systems

Those query patterns revealed different indexing strategies. FaunaDB requires covering indexes for every query path:

// FaunaDB - create indexes for queries
client.query(
  q.CreateIndex({
    name: 'users_by_email',
    source: q.Collection('users'),
    terms: [{ field: ['data', 'email'] }]
  })
)

Every query must use an index. You can't scan collections. The terms field defines what you search by. The values field determines what data the index returns. This strictness forces you to think about access patterns upfront but makes queries predictable and fast.

MongoDB allows collection scans but encourages indexes for performance:

// MongoDB - create indexes
db.collection('users').createIndex({ email: 1 }, { unique: true })

db.collection('purchases').createIndex({ userId: 1, date: -1 })

// Queries can run without indexes but warn about performance
db.collection('purchases').find({ userId: ObjectId('...') })

You can query any field without an index. MongoDB scans the collection and warns about missing indexes. Composite indexes support multiple query patterns. The flexibility helps during development but can cause performance issues in production if you forget to add indexes.

Temporal queries enable powerful debugging

FaunaDB's time-travel feature became invaluable when debugging production issues. Every document version remains queryable:

// FaunaDB - query data as it existed yesterday
client.query(
  q.At(
    q.Time('2024-10-30T12:00:00Z'),
    q.Get(q.Match(q.Index('accounts_by_id'), accountId))
  )
)

// Get all versions of a document
client.query(
  q.Paginate(q.Events(q.Ref(q.Collection('accounts'), accountId)))
)

The At function queries historical state. Events returns all document versions with timestamps. When a customer reported an incorrect balance, I queried their account at different timestamps to find when it changed. No need to parse logs or reconstruct state from events.

MongoDB's change streams provide recent history but not arbitrary time travel:

// MongoDB - watch collection changes
const changeStream = db.collection('accounts').watch([
  { $match: { 'fullDocument._id': accountId } }
]);

changeStream.on('change', (change) => {
  console.log('Account updated:', change.fullDocument);
});

Change streams capture insert, update, and delete operations as they happen. You get the new document state but not historical versions. To debug the balance issue, I had to check application logs and piece together what happened. No way to query "show me this document yesterday."

Scaling strategies differ fundamentally

The time-travel feature worked because FaunaDB handles distribution transparently. When my application traffic grew, I didn't change anything:

// FaunaDB - same code regardless of scale
const client = new faunadb.Client({
  secret: 'your-secret-key',
  domain: 'db.fauna.com'
})

// Queries work identically whether handling
// 10 requests/second or 10,000 requests/second

FaunaDB automatically distributes data across nodes. Transactions work globally without configuration. The service scales read and write capacity based on load. I monitored operation counts and adjusted rate limits, but the database handled the distribution mechanics.

MongoDB required manual sharding configuration when I hit single-server limits:

// MongoDB - enable sharding
sh.enableSharding('mydb')
sh.shardCollection('mydb.purchases', { userId: 'hashed' })

// Queries must consider shard key
db.collection('purchases').find({
  userId: ObjectId('...')  // Single shard
})

db.collection('purchases').find({
  date: { $gte: new Date('2024-10-01') }  // Scatter-gather
})

I chose userId as the shard key to distribute purchases across shards. Queries including userId route to a single shard. Queries without it scatter across all shards and gather results. Transactions across shards became expensive. I redesigned schemas to keep related data on the same shard.

Schema flexibility versus validation

That sharding experience made me think carefully about schemas. MongoDB's flexibility helped during rapid development:

// MongoDB - documents can vary freely
db.collection('products').insertOne({
  name: 'Widget',
  price: 19.99
})

db.collection('products').insertOne({
  name: 'Premium Widget',
  price: 49.99,
  features: ['waterproof', 'durable'],
  manufacturer: { name: 'ACME Corp', country: 'USA' }
})

Premium products got extra fields. No migrations needed. I added features arrays and nested manufacturer objects as requirements evolved. The flexibility accelerated feature development but caused bugs when code assumed fields existed.

FaunaDB supports flexible documents but I used schema validation to catch errors:

// FaunaDB - optional schema validation
client.query(
  q.Update(q.Collection('products'), {
    data: {
      schema: {
        name: q.Query(q.Lambda('v', 
          q.And(q.IsString(q.Var('v')), q.GT(q.Length(q.Var('v')), 0))
        )),
        price: q.Query(q.Lambda('v',
          q.And(q.IsNumber(q.Var('v')), q.GT(q.Var('v'), 0))
        ))
      }
    }
  })
)

The schema validation runs on every write. Documents that violate constraints get rejected. I caught type errors and missing required fields before they caused runtime exceptions. The validation added overhead but saved debugging time.

Pricing models affect architecture decisions

Those validation benefits came with different cost structures. FaunaDB charges per operation, which made me optimize query patterns:

// FaunaDB - each operation counts toward billing
client.query(q.Get(q.Match(q.Index('users_by_email'), email)))
// Cost: 1 read operation

client.query(
  q.Map(
    q.Paginate(q.Match(q.Index('purchases_by_user')), { size: 100 }),
    q.Lambda('ref', q.Get(q.Var('ref')))
  )
)
// Cost: 1 read for index + 100 reads for documents = 101 operations

Reading 100 purchases cost 101 operations. I optimized by including document data in index values, reducing multi-step queries to single operations. The operation-based pricing made me think about efficiency but felt unpredictable as traffic varied.

MongoDB's Atlas pricing uses compute and storage, which felt more predictable:

// MongoDB - pay for cluster size, not operations
// M30 cluster: $0.54/hour = ~$400/month
// Unlimited operations within cluster capacity

db.collection('purchases').find({ userId: ObjectId('...') })
// No per-query cost

I paid a fixed monthly cost for a cluster size. Whether I ran 1,000 queries or 1,000,000, the price stayed the same. Easier to budget but required capacity planning. Undersized clusters caused performance issues. Oversized clusters wasted money.

Connection model impacts application design

The pricing difference stemmed partly from connection models. FaunaDB uses stateless HTTPS requests:

// FaunaDB - stateless HTTP connections
const client = new faunadb.Client({ secret: 'your-secret-key' })

// Each query is independent HTTP request
await client.query(q.Get(q.Match(q.Index('users_by_email'), email)))

// No connection pooling needed
// Works great with serverless functions

Every query sends an HTTPS POST to FaunaDB. No persistent connections. No connection pool configuration. This model works perfectly with serverless functions that spin up and down. But HTTPS overhead adds latency compared to persistent connections.

MongoDB uses persistent TCP connections that require pooling:

// MongoDB - connection pooling required
const client = new MongoClient(uri, {
  maxPoolSize: 50,
  minPoolSize: 10
})

await client.connect()

// Queries reuse connections from pool
await db.collection('users').findOne({ email })

// Must manage connection lifecycle
await client.close()

I configured pool sizes based on expected concurrency. Too few connections caused queuing. Too many exhausted server resources. The persistent connections reduced latency but complicated serverless deployments. Lambda functions needed to reuse clients across invocations.

Transaction capabilities reveal different strengths

Those connection differences highlighted transaction capabilities. FaunaDB's transactions work globally without coordination:

// FaunaDB - transaction across collections
client.query(
  q.Do(
    q.Create(q.Collection('orders'), {
      data: { userId, items, total, status: 'pending' }
    }),
    q.Update(userRef, {
      data: { orderCount: q.Add(currentCount, 1) }
    }),
    q.Map(items, q.Lambda('item',
      q.Update(productRef, {
        data: { stock: q.Subtract(currentStock, quantity) }
      })
    ))
  )
)

The Do block creates an order, updates user stats, and decrements stock for multiple products. All operations commit atomically with serializable isolation. If any operation fails or conflicts occur, everything rolls back automatically. Works identically whether data lives in one region or spans continents.

MongoDB transactions work within replica sets but add overhead across shards:

// MongoDB - transaction within replica set
const session = client.startSession()
session.startTransaction()

try {
  await db.collection('orders').insertOne(order, { session })
  await db.collection('users').updateOne(
    { _id: userId },
    { $inc: { orderCount: 1 } },
    { session }
  )

  for (const item of items) {
    await db.collection('products').updateOne(
      { _id: item.productId },
      { $inc: { stock: -item.quantity } },
      { session }
    )
  }

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

The transaction works if orders, users, and products live on the same replica set. If sharded, I needed to ensure related data shared a shard key. Cross-shard transactions worked but doubled latency in my tests. I denormalized data to avoid cross-shard transactions when possible.

Operational complexity shapes team requirements

That transaction complexity extended to operations. FaunaDB's managed service eliminated operational work:

// FaunaDB - no operational tasks
// No servers to provision
// No replica sets to configure  
// No backup schedules to manage
// No version upgrades to plan

// Monitor through dashboard
// Set rate limits if needed

I signed up, got an API key, and started building. No servers to patch. No replica lag to monitor. No backup windows to schedule. FaunaDB handled everything. The tradeoff: less control over deployment topology and no option to self-host.

MongoDB required significant operational expertise:

# MongoDB - ongoing operational tasks
# Provision and size replica set members
# Configure mongos routers for sharding
# Set up automated backups
# Monitor replica lag
# Plan and execute version upgrades

rs.status()  # Check replica set status
db.printReplicationInfo()  # Monitor replication lag

I configured three-member replica sets for redundancy. Set up mongos routers when sharding. Scheduled backups through Atlas or configured mongodump scripts. Monitored replica lag during high write volumes. Planned maintenance windows for version upgrades. Atlas managed service reduced this burden but still required understanding MongoDB's architecture.

Final thoughts

FaunaDB is great if you want strong consistency and easy setup. It handles global transactions automatically, offers time-travel queries for debugging, and removes the need for database management. It’s ideal for finance apps, collaboration tools, or serverless projects where data accuracy matters most.

MongoDB works best when you need flexibility and control. You can tune performance, scale with sharding, and host it yourself if needed. It’s a solid choice for analytics, content systems, or large-scale apps run by teams with database experience.

In short, choose FaunaDB for simplicity and safety, or MongoDB for control and performance. The right pick depends on your team’s skills and what matters most to your project.

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