A collection of Python utilities for analyzing MongoDB collection statistics, index usage, and identifying redundant indexes.
mongodb-tools is a collection of Python utilities designed to help MongoDB administrators and developers analyze and optimize their database deployments. It provides insights into collection statistics, index usage patterns, and identifies potentially redundant indexes that may impact performance. The tools generate detailed reports that help with capacity planning and performance tuning.
MongoDB administrators, database engineers, and developers who need to monitor and optimize MongoDB performance in production environments.
These tools offer specialized analysis capabilities not built into MongoDB's native tools, particularly for identifying redundant indexes and providing detailed size distribution metrics. They're lightweight, script-based utilities that can be easily integrated into monitoring workflows.
Tools for working w/ a MongoDB installation
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.
The collection-stats.py script provides comprehensive statistics including document count, size percentages, average object size, and index data, as shown in the formatted output table in the README.
Includes redundant-indexes.py to identify overlapping or unnecessary indexes, helping optimize storage and performance, which is a key feature highlighted in the project description.
Offers RAM headroom calculations and usage metrics, such as 'RAM Used: 2.74G (61.6%)' in the output, aiding in capacity planning and performance tuning.
As Python utilities, they are easily integrable into automation workflows and can be run on-demand, fitting into existing monitoring setups without heavy dependencies.
Requires Python virtualenv and running setup.sh, with dependencies like python-dev, making installation more complex compared to single-command tools or packaged solutions.
The tools only provide insights without automated fixes, as stated in the philosophy—users must manually act on recommendations, which can be time-consuming.
Linked blog posts are from 2012, and there's no mention of updates for newer MongoDB versions, raising concerns about compatibility with recent features or best practices.