MongoDB performance bottlenecks are mainly reflected in low query efficiency, improper index use, insufficient memory, high write pressure and unreasonable sharding strategy. 1. If the query is not indexed or the index poorly designed, it will lead to full set scanning. The composite index sequence should be analyzed and optimized through explain(); 2. Insufficient memory causes frequent disk reads, and the page faults should be monitored and the WiredTiger cache should be configured reasonably; 3. High concurrent writes may cause lock contention. It is recommended to upgrade the engine, use batch inserts and adjust write attention; 4. Improper selection of shard keys affects performance, high cardinality, uniform distribution fields should be selected, and chunk balance should be checked regularly. Targeted tuning in combination with business scenarios is the key.

MongoDB's performance bottlenecks are often not easy to detect directly, but common problems are basically focused on query efficiency, index usage, hardware resources, and configuration optimization. If you find that the database response is slow, the write delay is increased, or the system load is abnormally increased, you may have encountered a performance bottleneck.

The following is a few common perspectives to talk about how to identify and deal with these problems.

1. The query does not go through the index or the index design is unreasonable

This is one of the most common performance issues. Although MongoDB supports full collection scanning, this operation can be very time-consuming if the data volume is large.

• Performance : IXSCAN does not appear in the explain() output, but COLLSCAN , indicating that the index is useless.
• Solution :
  • Analyze frequently executed queries to ensure that the WHERE condition fields have appropriate indexes.
  • Composite indexes should be established in the order of query conditions, and do not disrupt the order at will.
  • Avoid too many indexes, as they can also affect write performance.

A small detail is: Sometimes even if you have indexed, the index may not be hit if the order of the fields in the query does not match the prefix of the composite index. For example, if an index is created for {a:1, b:1} , only b=5 is used for querying, and the index will not work at this time.

2. Insufficient memory causes frequent disk readings

MongoDB relies on memory to cache data and indexes, and once memory is insufficient, the performance will drop sharply.

• Performance : Monitoring tools such as MongoDB Atlas or mongostat show that the number of page faults has surged.
• Diagnosis method :
  • Use db.collection.stats() to see if the data size is far beyond the available memory.
  • Observe the memory usage of the server, especially the use of the WiredTiger cache.

suggestion :

• If it is a single node deployment, consider upgrading the machine configuration.
• If the data volume is too large, you can consider sharding or compressing the data.
• Reasonably set the WiredTiger cache size (by default, half of physical memory) to avoid being squeezed by other services.

3. The write pressure is too high or the lock contention is serious

In high concurrency write scenarios, MongoDB may experience a problem of lock waiting time too long, especially in older versions (such as using the MMAPv1 engine).

• Performance : currentOp() shows that a large number of write operations are in the "waiting for lock" state.
• Troubleshooting methods :
  • Use db.currentOp() to view the currently running operation.
  • Use top or mongotop to see which sets are most stressful to write.

Optimization direction :

• Upgraded to the latest stable version of MongoDB, the WiredTiger engine has better concurrency control.
• For write-intensive services, bulk insert (bulkWrite) can be considered to reduce network round trips.
• Use write concern reasonably to avoid requiring fsync every time you write.

4. Unreasonable sharding strategies affect performance

The original purpose of sharding is to improve performance, but if the shard key is selected improperly, it will cause hot spots or queries to be unable to be pushed down.

• FAQ :
  • The shard key selects monotonic increments (such as timestamps), resulting in the writes being concentrated in a shard.
  • The query conditions do not contain shard keys, resulting in broadcast queries and inefficient.

Suggested practices :

• The shard key should have the characteristics of high cardinality, uniform distribution, and common use in query.
• Regularly check whether the chunk distribution is balanced and use sh.status() to view it.
• If a shard is found to have a significantly higher load than others, the shard key may need to be reevaluated.

Basically these common MongoDB performance bottleneck types. Some problems can be quickly located through monitoring, while others require in-depth analysis of logs and execution plans. The key is to adjust it in a targeted manner based on actual business scenarios.

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