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Description

This MongoDB error indicates that a query attempted to consume more memory than its configured limit, and the operation was unable to spill data to disk. It commonly occurs during memory-intensive operations like large aggregations, sorts, or other complex queries that cannot be processed entirely in RAM.

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Error Message

Query Exceeded Memory Limit No Disk Use Allowed

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Known Causes

4 known causes

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Large Result Sets or Sorts

Queries that process or return an exceptionally large number of documents, or perform extensive in-memory sorting, can quickly exhaust the allocated memory.

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Complex Aggregation Pipelines

Memory-intensive aggregation stages like $group, $sort, or $lookup operating on substantial datasets can exceed the query memory limit.

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Unoptimized Query Design

Inefficient query patterns, such as fetching unnecessary fields or performing operations that don't leverage indexes effectively, can lead to excessive memory consumption.

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Low Server Query Memory Limit

The 'queryMemoryMaxBytes' or 'aggregationBatchSize' server configuration might be set too low for the demands of complex queries, leading to frequent memory limit breaches.

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Solutions

4 solutions available

1. Optimize Query with Indexing medium

Add indexes to your collections to help MongoDB efficiently locate and retrieve data, reducing memory usage.

1

Identify slow or memory-intensive queries. You can use the `db.system.profile.find()` command (if profiling is enabled) or monitor the `currentOp` command to find these queries.

db.setProfilingLevel(1)

2

Analyze the query's `explain()` output to understand which fields are being scanned and if indexes are being used effectively. Look for `COLLSCAN` which indicates a full collection scan.

db.collection.find({ field1: 'value1', field2: 'value2' }).explain()

3

Create indexes on the fields used in your query's filter, sort, and projection stages. For compound queries, consider compound indexes.

db.collection.createIndex({ field1: 1, field2: -1 })

4

Re-run the query and verify that it now uses the newly created index. You should see `IXSCAN` in the `explain()` output.

db.collection.find({ field1: 'value1', field2: 'value2' }).explain()

2. Refine Query Selectivity and Projection easy

Reduce the amount of data processed by making queries more specific and only retrieving necessary fields.

1

Examine your query's filter criteria. Are you fetching more data than you need? Try to narrow down the results using more precise conditions.

db.collection.find({ status: 'active' }) // Consider if you can add more filters

2

Review the fields being returned by your query. Use projection to include only the fields essential for your application.

db.collection.find({ status: 'active' }, { _id: 0, name: 1, email: 1 })

3

Avoid using operators that might lead to large intermediate result sets or full collection scans, such as `$regex` without an index or `$where` clauses.

db.collection.find({ description: { $regex: '^prefix' } }) // Consider indexing if possible

3. Increase WiredTiger Cache Size medium

Allocate more RAM to the WiredTiger storage engine's cache, allowing more frequently accessed data to reside in memory.

1

Determine the current WiredTiger cache size. This is typically configured in the `mongod.conf` file or via command-line arguments.

grep 'storage.wiredTiger.engineConfig.cacheSizeGB' /etc/mongod.conf

2

Edit your MongoDB configuration file (e.g., `/etc/mongod.conf`). Increase the `storage.wiredTiger.engineConfig.cacheSizeGB` value. A common recommendation is to allocate 50% of the available RAM to the cache, but ensure you leave enough for the OS and other processes.

storage:
  wiredTiger:
    engineConfig:
      cacheSizeGB: 4  # Example: Increase from 1GB to 4GB

3

Restart the MongoDB service to apply the configuration changes.

sudo systemctl restart mongod

4

Monitor MongoDB's memory usage and query performance after the change to ensure it has a positive impact without causing other resource issues.

mongostat --rowcount 5

4. Review and Optimize Aggregation Pipelines advanced

Optimize aggregation stages, especially those that might materialize large in-memory datasets.

1

Identify aggregation pipelines that are causing memory issues. Use `db.collection.aggregate([...]).explain('executionStats')` to analyze the execution plan and identify memory-intensive stages.

db.collection.aggregate([
  { $group: { _id: '$user', totalAmount: { $sum: '$amount' } } }
]).explain('executionStats')

2

Ensure that `$group` and `$sort` stages are preceded by a `$match` stage that filters the data as early as possible. This significantly reduces the number of documents processed by these stages.

db.collection.aggregate([
  { $match: { date: { $gte: ISODate('2023-01-01') } } },
  { $group: { _id: '$user', totalAmount: { $sum: '$amount' } } }
])

3

If possible, use `$project` to reshape documents before expensive operations like `$group` to reduce the data size. Also, avoid using `$unwind` on arrays with a very large number of elements if not strictly necessary.

db.collection.aggregate([
  { $match: { ... } },
  { $project: { _id: 0, relevantField: 1 } },
  { $group: { ... } }
])

4

For very large aggregations, consider using the aggregation pipeline's `allowDiskUse` option if disk I/O is acceptable for intermediate results. However, this error specifically states 'No Disk Use Allowed', so this is more of a general aggregation optimization tip.

db.collection.aggregate([
  { $group: { ... } }
], { allowDiskUse: true })

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Related Errors

5 related errors

MongoDB Error 292: Query Memory Limit Exceeded