The MaxTimeMSExpired error occurs when a MongoDB operation exceeds the maximum execution time limit specified by the maxTimeMS parameter. This server-side timeout mechanism prevents long-running queries from consuming excessive database resources and helps maintain overall system performance.
The MaxTimeMSExpired error (error code 50) is MongoDB's way of enforcing query execution time limits. When you set a maxTimeMS value on a query or operation, MongoDB monitors how long the operation takes to complete. If the operation exceeds this time limit, MongoDB terminates it and returns this error to prevent long-running operations from consuming excessive server resources. This is particularly important in production environments where database performance needs to be maintained for all users. The timeout applies to various operations including queries, aggregation pipelines, update operations, and other database commands that support the maxTimeMS parameter.
First, examine the query that's timing out. Use MongoDB's explain() method to analyze query performance:
// Run explain on your query to see execution stats
db.collection.find({ /* your query */ }).explain("executionStats")
// For aggregation pipelines
db.collection.aggregate([ /* your pipeline */ ]).explain("executionStats")Look for:
- High "executionTimeMillis" values
- Large "totalDocsExamined" counts
- "COLLSCAN" operations (collection scans) instead of "IXSCAN" (index scans)
- Inefficient query patterns or missing indexes
If your query is doing collection scans, create indexes to improve performance:
// Create a single field index
db.collection.createIndex({ fieldName: 1 })
// Create a compound index for queries on multiple fields
db.collection.createIndex({ field1: 1, field2: -1 })
// Create a text index for text search queries
db.collection.createIndex({ description: "text" })
// Check existing indexes
db.collection.getIndexes()Remember that indexes should match your query patterns. Too many indexes can slow down write operations, so find a balance.
If the operation genuinely needs more time, increase the maxTimeMS value:
// For find queries
db.collection.find({ /* query */ }).maxTimeMS(60000) // 60 seconds
// For aggregation pipelines
db.collection.aggregate(
[ /* pipeline */ ],
{ maxTimeMS: 120000 } // 120 seconds
)
// For update operations
db.collection.updateMany(
{ /* filter */ },
{ /* update */ },
{ maxTimeMS: 30000 } // 30 seconds
)Set reasonable timeouts based on your application requirements. Consider:
- 30 seconds for most user-facing queries
- 60-120 seconds for complex aggregations
- 300+ seconds for batch processing jobs
For operations that process large datasets, consider breaking them into smaller batches:
// Instead of updating all documents at once
// db.collection.updateMany({status: "old"}, {$set: {status: "new"}})
// Process in batches
const batchSize = 1000;
let processed = 0;
let hasMore = true;
while (hasMore) {
const result = db.collection.updateMany(
{ status: "old" },
{ $set: { status: "new" } },
{ limit: batchSize, maxTimeMS: 10000 }
);
processed += result.modifiedCount;
hasMore = result.modifiedCount === batchSize;
print(`Processed ${processed} documents`);
}This approach:
- Reduces lock contention
- Allows other operations to run concurrently
- Makes progress visible
- Can be resumed if interrupted
Use MongoDB's monitoring tools to identify performance bottlenecks:
// Check current operations
db.currentOp()
// Check database stats
db.stats()
// Check collection stats
db.collection.stats()
// Use MongoDB Atlas Performance Advisor or mongostat/mongotopCommon performance improvements:
- Add more RAM to increase working set
- Scale up your database instance
- Implement connection pooling
- Use read preferences to distribute load
- Consider sharding for very large datasets
The maxTimeMS parameter is a server-side timeout that applies to the entire operation execution on the MongoDB server. It's different from client-side timeouts or network timeouts.
Important considerations:
1. Killop: Operations terminated by maxTimeMS can be viewed in db.currentOp() with "killop" status
2. Atomicity: For write operations, the timeout applies to the entire batch, not individual documents
3. Aggregation: For aggregation pipelines, the timeout applies to the entire pipeline execution
4. Transactions: In multi-document transactions, maxTimeMS applies to each operation within the transaction
5. Index builds: maxTimeMS does not apply to index creation operations
Best practices:
- Set maxTimeMS at the driver level for all operations
- Use different timeout values for different operation types
- Implement retry logic with exponential backoff for transient timeouts
- Log timeout occurrences to identify patterns and optimize proactively
- Consider using MongoDB's $out stage for complex aggregations that write to temporary collections
StaleShardVersion: shard version mismatch
How to fix "StaleShardVersion: shard version mismatch" in MongoDB
MongoOperationTimeoutError: Operation timed out
How to fix "MongoOperationTimeoutError: Operation timed out" in MongoDB
MongoServerError: PlanExecutor error during aggregation :: caused by :: Sort exceeded memory limit of 104857600 bytes, but did not opt in to external sorting. Aborting operation.
How to fix "QueryExceededMemoryLimitNoDiskUseAllowed" in MongoDB
MissingSchemaError: Schema hasn't been registered for model
How to fix "MissingSchemaError: Schema hasn't been registered for model" in MongoDB/Mongoose
CastError: Cast to ObjectId failed for value "abc123" at path "_id"
How to fix "CastError: Cast to ObjectId failed" in MongoDB