How to integrate Mtools with mongodb compass/atlas


I wanted to parse, filter, and visualize MongoDB log files using Mtools, but I haven’t figured out how to do that?

Welcome to the MongoDB Community @Viswateja_b!

mtools, Compass, and Atlas all have different purposes:

  • mtools is a set of Python scripts for standing up local test deployments and working with log files. The current version of mtools (1.7.0) doesn’t support the newer JSON structured log format in MongoDB 4.4+ and its log parsing options are possibly obviated by other tools which have more comprehensive insights (for example, Keyhole). mtools was created in era when MongoDB log files had to be parsed with regular expressions and many many assumptions. With modern JSON logging, specialised parsing tools are no longer a necessity. There are some helpful examples using jq in the MongoDB manual: Parsing Structured Log Messages.

  • MongoDB Compass is an interactive GUI application for working with MongoDB deployments, and has features more focused around exploring and manipulating data rather than server diagnostics. There is a Performance Tab that provides some real-time performance metrics including identifying slow operations in a MongoDB cluster, but this isn’t a tool for working with log files.

  • MongoDB Atlas is a multi-cloud application data platform with an integrated suite of cloud database and data services. Atlas has built-in charts, alerts, and integrations to help you Monitor Your Database Deployments. You can also View and Download MongoDB Logs if you prefer offline analysis using other tools.

If you are using Atlas, I would start by learning the available integrated tools and documentation such as Deployment Metrics, Analyzing Slow Queries, and Improving Schema. These provide insight into the most common performance problems and are generally much faster (and less effort) than digging through log files.

Are there specific types of diagnostic problem you are trying to solve?

If you can share some more details about your common diagnostic challenges and environment (version of MongoDB; deployment type: standalone, replica set, sharded cluster, Atlas Serverless; O/S version) there may be some more relevant suggestions on tools or approach.


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