Error
Error Code:
46
MongoDB Error 46: Database Lock Contention
Description
Error 46, 'Lock Busy', indicates that MongoDB cannot acquire a necessary lock because another operation is currently holding a conflicting lock on the same resource. This typically occurs during periods of high concurrency, preventing operations such as writes, index builds, or certain administrative commands from proceeding.
Error Message
Lock Busy
Known Causes
4 known causesLong-Running Write Operations
Extensive write operations, such as large bulk inserts, updates, or index builds, can hold database-level or collection-level locks for extended periods.
High Concurrency on Shared Resources
Many concurrent operations attempting to modify or access the same collection or document range can lead to contention for the necessary locks.
Inefficient Queries or Transactions
Poorly optimized queries or transactions that scan large datasets or involve complex aggregations might acquire and hold locks longer than necessary.
Ongoing Maintenance Tasks
Administrative operations like backups, repairs, or re-indexing can acquire global or database-wide locks, blocking other operations.
Solutions
4 solutions available1. Identify and Terminate Long-Running Operations medium
Find and stop queries that are holding locks for too long.
1
Connect to your MongoDB instance using the `mongosh` shell.
mongosh2
Use `db.currentOp()` to inspect currently running operations. Look for operations that have been running for an unusually long time, especially those that might be holding locks.
db.currentOp()3
If you identify a problematic operation, use `db.killOp(opid)` to terminate it. Replace `opid` with the operation ID from `db.currentOp()` output.
db.killOp(123456789)4
Monitor your application and database performance after terminating the operation to ensure the lock contention is resolved.
2. Optimize Slow Queries and Indexes advanced
Improve query performance to reduce lock duration.
1
Use `db.collection.explain().find(...)` or `db.collection.explain().aggregate(...)` to analyze the execution plan of slow queries.
db.myCollection.explain().find({ field: 'value' })2
Identify missing indexes or inefficient query patterns. For example, a query performing a collection scan instead of using an index can lead to prolonged lock holding.
3
Create appropriate indexes based on your query patterns. Consider compound indexes for queries that filter and sort on multiple fields.
db.myCollection.createIndex({ field1: 1, field2: -1 })4
Refactor queries to be more efficient, for example, by reducing the number of documents processed or using more targeted queries.
3. Adjust Write Concern and Read Concern medium
Configure write and read concerns to manage consistency and performance trade-offs.
1
Review your application's write concern settings. A high `w` value (e.g., `w: 'majority'`) can increase the time it takes for a write to be acknowledged, potentially leading to lock contention if many writes are happening concurrently.
db.collection.insertOne({ data: 'example' }, { writeConcern: { w: 'majority', wtimeout: 5000 } })2
Consider if a less stringent write concern is acceptable for your use case, such as `w: 1` or `w: 'numberOfOtherNodes'`, to reduce acknowledgement latency. Be aware of the consistency implications.
db.collection.insertOne({ data: 'example' }, { writeConcern: { w: 1 } })3
Examine read concern settings. While less common for lock contention, certain read concerns might indirectly impact lock acquisition if they require waiting for specific replication states.
db.collection.find().readConcern({ level: 'majority' })4
Test changes to write and read concerns in a staging environment before applying them to production.
4. Scale Database Resources or Sharding advanced
Increase hardware capacity or distribute the load across multiple servers.
1
Monitor your MongoDB server's CPU, memory, and disk I/O utilization. High resource utilization can exacerbate lock contention.
2
If resources are consistently strained, consider upgrading your server hardware (e.g., more CPU, RAM, faster disks).
3
For large datasets and high throughput, implement sharding to distribute data and operations across multiple MongoDB instances. This is a more involved process and requires careful planning.
4
Consult the MongoDB documentation for detailed guidance on scaling strategies, including sharding setup and maintenance.