# Celery vs RQ: source lines of code compared

The Celery vs RQ comparison on OctoCounts shows side-by-side source line counts for celery/celery and rq/rq, counted with the same engine and analysis options on the dates shown in the methodology line: files, total lines, code lines, comment lines, blank lines, and per-language totals, pinned to the exact commits listed below.

As of 2026-09-15, celery/celery contains 114,944 total lines (90,762 code) across 482 files, while rq/rq contains 14,513 total lines (10,999 code) across 56 files as of 2026-09-16. celery/celery is about 8.3x the size of rq/rq by code lines. Code size is not code quality: a larger count only means more source material, not a better or worse project.

| Metric | [celery/celery](https://octocounts.com/github/celery/celery) | [rq/rq](https://octocounts.com/github/rq/rq) |
| --- | ---: | ---: |
| Files | 482 | 56 |
| Total lines | 114,944 | 14,513 |
| Code lines | 90,762 | 10,999 |
| Comment lines | 5,082 | 1,278 |
| Blank lines | 19,100 | 2,236 |
| Languages counted | 12 | 7 |

Top languages in celery/celery: Python (91.0% of code), ReStructuredText (4.5% of code), JSON (2.7% of code). Top languages in rq/rq: Python (98.2% of code), TOML (1.2% of code), INI (0.4% of code). Python appears in both top language lists; ReStructuredText, JSON, Shell, YAML appear only in celery/celery's top languages; TOML, INI, Makefile, Dockerfile appear only in rq/rq's top languages.

Methodology: both counts come from cached OctoCounts reports generated with tokei. celery/celery was counted at ref b6fa6b304c0ed010c23d7a4e4ece0b72ee4b29bb (commit b6fa6b304c0e) on 2026-09-15; rq/rq was counted at ref 0e9ffe2901a68b1847721fd0b9c45d872f2887d3 (commit 0e9ffe2901a6) on 2026-09-16. See the [counting methodology](https://octocounts.com/docs/methodology) for ignored directories and analysis options.
## About this comparison

celery/celery counts the Celery repository: a distributed task queue for Python, with its core worker and result backends, a broad set of integration tests, and extensive documentation sources. rq/rq counts RQ (Redis Queue): a Python library that puts jobs in Redis and runs workers against them. Both are Python background-job libraries, counted on the dates shown above.

Both trees are predominantly Python as the per-language tables show, so the difference in totals is about scope, not implementation language: Celery's count covers brokers, result backends, and scheduling features, while RQ deliberately centers on a Redis-backed worker.

Celery's counted tree includes a large documentation and test corpus relative to its core code, a pattern typical of a long-lived project supporting many configurations — visible in the code/comment/test split of the report rather than in the headline total.

_The smaller RQ total should not be read as RQ being a lesser tool: it implements a deliberately narrower feature set. Task-queue choice depends on the features an application actually uses, not on the size of either source tree._

Sources: [celery/celery repository](https://github.com/celery/celery) · [rq/rq repository](https://github.com/rq/rq). Statements verified 2026-09-16.

Evidence and next steps:

- [celery/celery SLOC report](https://octocounts.com/github/celery/celery)
- [rq/rq SLOC report](https://octocounts.com/github/rq/rq)
- [Compare celery/celery and rq/rq interactively](https://octocounts.com/compare?left=https%3A%2F%2Fgithub.com%2Fcelery%2Fcelery&right=https%3A%2F%2Fgithub.com%2Frq%2Frq)

Note: code size is not code quality. OctoCounts only reports reproducible line counts and makes no claim that either project is better.

## Compare FAQ

### Which has more lines of code, celery/celery or rq/rq?

celery/celery has more code: 90,762 code lines versus 10,999 for rq/rq, about 8.3x as much, based on cached OctoCounts reports as of 2026-09-16.

### Does more source lines of code mean more complexity?

Not necessarily. SLOC measures size, not complexity, quality, or maintainability. A larger codebase can mean more features, more generated or vendored code, more verbose language idioms, or more tests — none of which imply the code is harder to work with. Use SLOC to gauge the scale of what you'd be reading or maintaining, not as a quality signal for celery/celery, rq/rq, or any repository.

### How is this comparison calculated?

Methodology: both counts come from cached OctoCounts reports generated with tokei. celery/celery was counted at ref b6fa6b304c0ed010c23d7a4e4ece0b72ee4b29bb (commit b6fa6b304c0e) on 2026-09-15; rq/rq was counted at ref 0e9ffe2901a68b1847721fd0b9c45d872f2887d3 (commit 0e9ffe2901a6) on 2026-09-16. See the counting methodology for ignored directories and analysis options.

### Can I compare a different branch, tag, or commit?

Yes. This page shows the default branch for each repository. Use the interactive comparison tool to pick any public GitHub repository, branch, tag, or commit SHA for both sides and get a fresh side-by-side report.

## Related OctoCounts pages

- [Interactive repository comparison](https://octocounts.com/compare)
- [Recently analyzed repositories](https://octocounts.com/recent)
- [Popular SLOC reports](https://octocounts.com/popular)
- [Trending GitHub repositories](https://octocounts.com/trending)
- [Hall of Monoliths](https://octocounts.com/hall-of-monoliths)
- [Original research: how filtering changes SLOC counts](https://octocounts.com/research)
- [GitHub SLOC counter guide](https://octocounts.com/docs/github-sloc-counter)
- [Counting methodology](https://octocounts.com/docs/methodology)
- [SLOC and code metrics glossary](https://octocounts.com/docs/glossary)
- [OctoCounts API docs](https://octocounts.com/docs/api)