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Description

MongoDB Error 15, "Overflow," indicates that an operation attempted to exceed an internal limit, often related to data size, numerical capacity, or processing resources. This typically occurs when a document, array, or an aggregation result becomes too large for MongoDB to process.

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Error Message

Overflow

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Known Causes

4 known causes

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Document Exceeds BSON Limit

Attempting to insert or update a document whose total size (e.g., 16MB) exceeds MongoDB's BSON document size limit.

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Array Exceeds Internal Limits

An array within a document grows excessively large, exceeding internal BSON representation limits for array size or nested depth.

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Large Number Overflow

Performing arithmetic operations with numbers that exceed the maximum capacity of 64-bit integers or doubles, leading to an overflow.

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Aggregation Memory Limit Exceeded

An aggregation pipeline operation (e.g., $sort, $group) attempts to process data that exceeds the configured memory limit (e.g., 100MB by default).

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Solutions

4 solutions available

1. Optimize Document Size and Structure medium

Reduce the size of individual documents to avoid exceeding BSON limits.

1

Analyze your documents for excessively large fields, such as large embedded arrays or binary data.

db.collection.findOne()

2

Consider denormalizing large embedded arrays or storing large binary data (like images or files) in GridFS or an external object storage service.

Use a library like `mongodb` driver's `GridFSBucket` for uploads.

3

Review your schema design. Can fields be moved to separate collections? Are there redundant or unnecessary fields?

No direct code snippet for schema review, but consider using tools like MongoDB Compass for schema analysis.

4

If you have arrays with a very large number of elements, consider pagination or splitting them into separate documents if logical.

No direct code snippet for this, but conceptually, instead of `[{item1}, {item2}, ..., {itemN}]`, you might have `[{item1}, {item2}]` in one document and `[{item3}, {item4}]` in another, related by a common ID.

2. Increase BSON Size Limit (with caution) medium

Temporarily increase the BSON object size limit if absolutely necessary, but understand the implications.

1

Stop your MongoDB server process.

For systemd: `sudo systemctl stop mongod`
For init.d: `sudo service mongod stop`

2

Edit your MongoDB configuration file (typically `mongod.conf`).

e.g., `/etc/mongod.conf`

3

Add or modify the `maxBsonObjectSize` parameter under the `storage` section. The default is 16MB. Increase it cautiously, as larger sizes can impact performance and memory usage.

storage:
  wiredTiger:
    engineConfig:
      maxBsonObjectSize: 32768000 # Example: 32MB

4

Save the configuration file and restart your MongoDB server.

For systemd: `sudo systemctl start mongod`
For init.d: `sudo service mongod start`

3. Review and Optimize Queries medium

Inefficient queries can lead to large intermediate results that exceed limits.

1

Identify queries that are causing the error. Use the `db.collection.explain()` method to understand query execution plans.

db.collection.explain('executionStats').find({ your_query_filter }).limit(100)

2

Look for stages in the execution plan that involve large data scans or sorting operations on unsorted fields. Consider adding indexes to support your queries.

db.collection.createIndex({ field_to_index: 1 })

3

Avoid fetching excessive amounts of data. Use projection to retrieve only the necessary fields.

db.collection.find({ your_query_filter }, { field1: 1, field2: 1, _id: 0 })

4

If performing aggregations, ensure that intermediate stages don't generate massive datasets. Consider using `$limit` or `$sample` earlier in the pipeline if appropriate.

db.collection.aggregate([
  { $match: { ... } },
  { $limit: 100 }, // Limit early if possible
  { $project: { ... } }
])

4. Address Large Array Operations medium

Operations on very large arrays within a single document can trigger this error.

1

If you are performing operations that modify or read from extremely large arrays (e.g., appending many elements, iterating over thousands of elements), this can be a bottleneck.

This is more of a conceptual issue than a direct code snippet.  Consider the impact of operations like `db.collection.update({}, { $push: { largeArray: { $each: [...] } } })` when `largeArray` is already huge.

2

Re-evaluate the design. If an array is growing to millions of elements, it's likely not the best structure. Consider splitting it into separate documents.

This would involve a data migration strategy.  You might read documents, extract array elements, and insert them as new documents in a related collection, linking them back with a reference.

3

For read operations on large arrays, consider if you truly need to retrieve the entire array. Use projection to select specific elements or slices if possible.

db.collection.findOne({}, { 'largeArray.0': 1, 'largeArray.5': 1 }) // Access specific elements by index

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Related Errors

5 related errors

MongoDB Error 15: Data Size or Operation Limit Exceeded