Monitoring tools for MongoDB
At MongoLab we manage thousands of MongoDB clusters and regularly help customers optimize system performance. Some of the best tools available for gaining insight into our MongoDB deployments are the monitoring features of MongoDB Management Service (MMS). MMS allows us to quickly determine the health of a MongoDB system and identify the root cause of performance issues. This post covers our general approach to using MMS and MongoDB log files and provides techniques to help you optimize your own MongoDB deployment, whether you’re in the cloud or on your own hardware.
First, we will define the key metrics that we use to guide any performance investigation. Then we will go through the various combinations of metric values, discuss what they mean, and explore how to address the problems they indicate.
Here we focus primarily on the metrics provided by MMS but augment our analysis with specific log file metrics as well.
- PF/OP (derived from the Page Faults and Opcounters graphs)
- CPU Time (IOWait and User)
- Lock Percent and Queues
We find that by examining these key metrics you can very quickly get a good picture of what is going on inside a MongoDB system and what computing resources (CPU, RAM, disk) are performance bottlenecks. Let’s look a little more closely at each of these metrics.
PF/OP (Page Faults / Opcounters)
Between 5 and 10 page faults per second (left) compared to more than 4, 000 operations per second (right).
A PF/OP of 0.001 (5 / 4000) is close enough to zero to classify as a low disk I/O requirement.
MongoDB manages data in memory using memory mapped files. As indexes and documents are accessed, the data file pages containing them are brought into memory. Meanwhile, data that isn’t accessed remains on disk. If a given memory-mapped page is not in memory when the data in it is needed, a
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