A fast schema and data analyzer for MongoDB that provides detailed insights into database structure and content.
Mongoeye is a schema and data analysis tool for MongoDB databases, written in Go. It helps developers and administrators quickly understand the structure and content of their MongoDB collections by providing detailed statistics and visualizations. The tool supports both local and distributed analysis, making it suitable for various deployment scenarios.
MongoDB developers and database administrators who need to analyze and understand the schema and data distribution within their collections, especially those working with large or complex datasets.
Developers choose Mongoeye for its high-speed analysis, claiming to be the fastest schema analyzer for MongoDB, and its flexibility in offering both local parallel processing and remote aggregation-based analysis. Its single binary distribution with no dependencies and support for multiple output formats (table, JSON, YAML) makes it easy to integrate into workflows.
Schema and data analyzer for MongoDB written in Go.
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Claims to be the fastest schema analyzer for MongoDB, using parallel local algorithms as benchmarked in provided comparisons.
Available as single pre-built binaries for Windows, Linux, and macOS, requiring no additional software installation.
Supports both local parallel processing and remote aggregation-based analysis, adapting to different MongoDB versions and deployment scenarios.
Provides detailed outputs like histograms (value, length, weekday, hour), frequency analysis, and min/max/avg calculations across various data types.
Outputs results in JSON or YAML formats alongside table view, making it easy to pipe data into other tools or automation scripts.
Lacks a graphical user interface, with a web interface only listed as a TODO, limiting accessibility for non-technical users.
TLS/SSL support is not implemented and marked as a TODO, which could be a deal-breaker for secure database connections.
Primarily a Go binary with shared libraries for other languages still in TODO, making integration into non-Go projects cumbersome.
Features like --project require JSON strings with careful escaping in command line, which can be error-prone and frustrating.