apache/fesod

Fast. Easy. Done. Processing spreadsheets without worrying about large files causing OOM.

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Summary Information

Updated 9 minutes ago
Added to GitGenius on September 10th, 2026
Created on October 5th, 2024
Open Issues & Pull Requests: 150 (+0)
GitHub issues: Enabled
Number of forks: 532
Total Stargazers: 6,208 (+0)
Total Subscribers: 84 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 16.7 hours
Mean response time: 21.8 days
90th percentile: 35.1 days
Tracked items: 440

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How this project is maintained

Around half of the issues opened in the past year never receive a reply. 84% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Work labelled "discussion welcome" is answered fastest, typically in about an hour, while "community support" waits about 4 weeks. Only 5% of issues opened in the past year have been closed. Three people close 80% of everything that gets resolved.

Charts & Analytics

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Issue Activity (beta)

Open issues: 110
New in 7 days: 6
Closed in 7 days: 5
Avg open age: 428 days
Stale 30+ days: 87
Stale 90+ days: 81

Recent activity

Opened in 7 days: 5
Closed in 7 days: 3
Comments in 7 days: 9
Events in 7 days: 21

Top labels

  • community support (71)
  • bug (63)
  • enhancement (63)
  • help wanted (48)
  • question (31)
  • good first issue (27)
  • suggest (24)
  • wontfix (23)

Detailed Description

Fesod is a Java library for processing spreadsheets at scale without memory exhaustion.

The tool addresses the challenge of handling large Excel and CSV files that would typically cause out-of-memory errors in traditional spreadsheet processing approaches. It achieves this through streaming-based processing that reads and writes data incrementally rather than loading entire files into memory at once. This design allows developers to work with spreadsheets of arbitrary size while maintaining predictable memory consumption.

Fesod suits projects that need to parse, transform, or generate Excel and CSV files as part of data pipelines, ETL workflows, or batch processing systems. It is particularly valuable when file sizes are unpredictable or when processing must run in memory-constrained environments. The library supports both reading and writing operations across XLS, XLSX, and CSV formats, making it applicable to a wide range of spreadsheet-based data workflows.

The project shows consistent maintenance activity with regular updates addressing bug fixes and feature improvements. Development follows a structured release cycle with attention to stability and backward compatibility. The codebase demonstrates active engagement with user-reported issues and a commitment to keeping dependencies current.