Large-scale data migration solution

Challenges and Requirements of Data Migration

Large-scale data migration in MongoDB environments faces numerous challenges, including data consistency, performance impact during migration, network bandwidth limitations, and compatibility issues between different versions or architectures. When the data volume reaches TB or even PB levels, traditional export/import methods often fail to meet business continuity requirements. For example, an e-commerce platform may need to migrate user behavior logs from a sharded cluster to a new time-series collection while ensuring the normal operation of analytical services and minimizing the impact on the production environment.

Migration Solution Selection

Logical Migration Tools

MongoDB's official mongodump/mongorestore combination is suitable for small to medium-scale data migration, with advantages in compatibility and support for BSON format compression. Typical use cases include data backup before cross-version upgrades:

// Export collection data (with gzip compression)
execSync(`mongodump --uri="mongodb://source:27017" --collection=products --gzip --out=/backup`);

// Restore to target cluster
execSync(`mongorestore --uri="mongodb://target:27017" --gzip /backup/mydb/products.bson.gz`);

Physical File Migration

For TB-level databases, directly copying data files (--dbpath) is more efficient. This requires:

1. Executing fsyncLock on the source database to lock writes
2. Using rsync for block-level incremental synchronization
3. Rebuilding indexes on the target database

# Freeze writes on the source database
mongo --eval "db.fsyncLock()"

# Sync data directory using rsync
rsync -avz /data/db/ backup-server:/migration/

# Automatically rebuild indexes when starting the target database
mongod --dbpath /migration/db --repair

Online Migration Strategies

Change Data Capture (CDC)

Using MongoDB Oplog for near-zero downtime migration, suitable for sharded cluster-to-sharded cluster scenarios. Key steps include:

1. Initial full synchronization
2. Continuously listening to and replaying the oplog
3. Switching traffic after verifying data consistency

const { MongoClient } = require('mongodb');
const sourceClient = new MongoClient('mongodb://source:27017/?replicaSet=rs0');
const targetClient = new MongoClient('mongodb://target:27017');

async function syncOplog() {
  await sourceClient.connect();
  await targetClient.connect();
  
  const oplog = sourceClient.db('local').collection('oplog.rs');
  const cursor = oplog.find({ ts: { $gt: lastSyncedTimestamp } });
  
  while (await cursor.hasNext()) {
    const doc = await cursor.next();
    await applyOperation(targetClient, doc);
    saveCheckpoint(doc.ts);
  }
}

function applyOperation(client, op) {
  const ns = op.ns.split('.');
  const coll = client.db(ns[0]).collection(ns[1]);
  
  switch(op.op) {
    case 'i': return coll.insertOne(op.o);
    case 'u': return coll.updateOne(op.o2, op.o);
    case 'd': return coll.deleteOne(op.o);
  }
}

Special Handling for Sharded Clusters

Migrating sharded clusters requires additional considerations:

1. Balancer state management
2. Shard key compatibility validation
3. Config server metadata synchronization

Best practice is to use the mongosync tool for shard-aware migration:

mongosync \
  --source-uri mongodb://src-shard1,src-shard2/srcdb \
  --destination-uri mongodb://dst-shard1,dst-shard2/dstdb \
  --cluster-to-cluster \
  --include-collections orders,customers

Data Validation and Rollback

Validation Methods

1. Document count comparison: db.collection.countDocuments()
2. Content validation: Use $group aggregation to calculate checksums
3. Sampling comparison: Randomly sample N records for full-field comparison

// Generate MD5 checksum for a collection
async function getChecksum(db, collName) {
  const result = await db.collection(collName).aggregate([
    { $sort: { _id: 1 } },
    { $project: { hash: { $md5: { $concat: Object.keys(schema).map(k => `$${k}`) } } } },
    { $group: { _id: null, total: { $sum: "$hash" } } }
  ]).toArray();
  return result[0].total;
}

Rollback Mechanism Design

1. Retain pre-migration snapshots for at least 7 days
2. Log migration timestamps
3. Prepare reverse synchronization scripts

Performance Optimization Techniques

1. Batch writes: Set --numInsertionWorkers parameter for parallel loading
2. Network compression: Enable --gzip and --ssl to reduce transfer volume
3. Resource isolation: Use --rateLimit to control migration traffic
4. Index strategy: Migrate data first, then create indexes

# Enable 4 parallel threads for data restoration
mongorestore --numInsertionWorkers 4 --gzip /backup

# Limit transfer bandwidth to 100MB/s
mongodump --rateLimit 100000000

Cloud Migration Practices

AWS DMS and MongoDB Atlas Live Migration configurations:

# AWS DMS task definition example
TaskSettings:
  TargetMetadata:
    ParallelLoadThreads: 8
    LOBChunkSize: 64
  FullLoadSettings:
  CommitRate: 50000

Atlas migration workflow:

1. Create a migration project in the Atlas console
2. Configure network peering
3. Set continuous synchronization thresholds
4. Trigger the final cutover window

Exception Handling Scenarios

1. Network interruption: Implement checkpoint restart mechanism
2. Schema conflicts: Use --ignoreBlanks to skip missing fields
3. Version differences: Avoid index compatibility issues with --noIndexRestore
4. Insufficient space: Pre-allocate data files with mongod --quotaFiles

// Typical retry logic for migration errors
async function safeMigrate() {
  let retries = 3;
  while(retries--) {
    try {
      await migrateBatch();
      break;
    } catch (err) {
      if (err.code === 'NetworkError') {
        await sleep(5000);
        continue;
      }
      throw err;
    }
  }
}