What are the steps to identify and fix MongoDB performance issues using Profiler?

1 Enable the Profiler

The first step is to enable the Profiler on your MongoDB server or cluster. You can do this by setting the profilingLevel parameter to one of the following values: 0 (off), 1 (on for slow operations), or 2 (on for all operations). You can also specify a slowOpThresholdMs parameter to define what constitutes a slow operation. For example, to enable the Profiler for operations that take longer than 100 milliseconds, you can run the following command in the MongoDB shell: db.setProfilingLevel(1, {slowOpThresholdMs: 100}) You can also enable the Profiler from the MongoDB Atlas UI, if you are using a cloud-hosted service.

2 Query the Profiler

The next step is to query the Profiler data, which is stored in a special collection called system.profile in each database. You can use the db.system.profile.find() command to view the Profiler documents, which contain information such as the operation type, namespace, query filter, execution time, number of documents scanned and returned, index usage, and error messages. You can also use the db.system.profile.aggregate() command to perform more complex analysis, such as grouping, sorting, filtering, or calculating statistics on the Profiler data. For example, to find the top 10 slowest queries by execution time, you can run the following command:

db.system.profile.aggregate([
  {$match: {op: "query"}},
  {$sort: {millis: -1}},
  {$limit: 10}
])        

3 Interpret the Profiler

The third step is to interpret the Profiler data and identify the possible causes and solutions for the performance issues. Common indicators of performance problems, such as high execution time, high number of documents scanned, or low number of documents returned, can reveal underlying issues such as inefficient indexes, complex query filters, large result sets, missing or inappropriate indexes, incorrect or incomplete query filters, data corruption, or replication lag. Solutions can include creating or improving indexes, simplifying or rewriting queries, limiting or projecting result fields, scaling up or out the hardware, aligning the queries with the index fields, correcting or completing query filters, repairing or restoring data, or resolving replication issues.

4 Optimize the Profiler

The fourth step is to optimize the Profiler settings and usage, to ensure that it does not affect the database performance or consume too much disk space. To do this, use the Profiler sparingly and selectively, only enabling it when necessary and disabling it when done. Set a reasonable slow operation threshold and use the lowest profiling level that meets your needs, to reduce the overhead and noise of the Profiler data. Additionally, you should rotate and archive the Profiler data periodically, to prevent it from growing too large. This can be done with the db.system.profile.createIndex({ts: 1}, {expireAfterSeconds: N}) command, which creates a TTL index on the ts field that automatically deletes the Profiler documents after N seconds. Alternatively, you can use the mongodump and mongorestore tools to backup and restore the Profiler data. Lastly, consider using external tools like MongoDB Compass or MongoDB Charts to visualize and analyze the Profiler data in a more user-friendly way. This will help you spot patterns, trends, and outliers in the database performance.

5 Test and verify the Profiler

The final step is to test and verify the impact of the Profiler and the performance improvements on your database. You can use benchmarking tools, such as MongoDB Benchmarking Tool, YCSB, or JMeter, to run tests before and after enabling the Profiler and applying the performance optimizations. This will help you measure metrics such as throughput, latency, concurrency, and scalability. Additionally, you can use tools such as MongoDB Ops Manager, MongoDB Cloud Manager, or MongoDB Atlas to monitor database metrics like operations per second, disk I/O, CPU usage, memory usage, and network traffic. This will provide insight into performance changes and anomalies of your database.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?