celery-batches
Experimental task class that buffers messages and processes them as a list.
Decision gist · record as of 2026-08-14
Yes, if you use Celery and have workloads where batching reduces overhead or improves throughput. The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. The Alpha status is not a blocker. Install it when you have a concrete use case for task batching; it is not a general-purpose upgrade.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later and Celery ~= 5.0; older Python or Celery versions require older celery-batches releases.
- Low install friction with a single runtime dependency on celery.
- Actively maintained with a recent release and ongoing repository activity.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is a permissive license that allows commercial and private use with minimal restrictions, requiring only preservation of copyright and license notices.
last release 2026-01-16 (210 days) · last repo commit 2026-04-13 · 101 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 197,110 downloads/mo, #9,769 on PyPI
Alternatives
Verify before relying
pip install celery-batches
from celery_batches import batch
from celery import Celery
app = Celery()
@app.task(base=batch)
def process_batch(tasks):
# tasks is a list of task arguments
pass- Whether the package's Alpha development status reflects stability concerns or is simply a conservative classification.
- Performance characteristics when batching large numbers of tasks or with high-frequency flush triggers.
- Specific configuration options for flush_every and flush_interval parameters.
What it is and what it does
Celery Batches is a task extension for Celery that groups multiple task calls into a single batch before execution. Instead of processing tasks individually, you define a batch task that receives a list of accumulated task arguments and processes them together. The batch is flushed either when a configurable number of tasks accumulate or after a timer interval expires, whichever comes first.
This pattern is useful when individual task execution has expensive setup costs that can be amortized across multiple items, or when you want to deduplicate similar requests, keep only the latest variant of a task, or bulk-insert data. The package was originally part of Celery's contrib module but was extracted as a standalone project to maintain compatibility with modern Celery versions (5.0+) and current Python releases (3.9–3.14).
Use it for
- Bulk database inserts or updates where individual row operations are inefficient compared to batch operations.
- De-duplicating or coalescing similar task requests that arrive in rapid succession.
- Processing tasks with expensive initialization amortized across multiple items.
- Accumulating metrics or log entries and writing them in bulk rather than individually.
- Rate-limiting or throttling task execution by grouping requests into batches.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you use Celery and have workloads where batching reduces overhead or improves throughput.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and uses a permissive license. The Alpha status is not a blocker. Install it when you have a concrete use case for task batching; it is not a general-purpose upgrade.
Install
celery-batches on PyPI
Before you install
Low install friction with a single runtime dependency on celery. Actively maintained with a recent release and ongoing repository activity.
Requires Python 3.9 or later and Celery ~= 5.0; older Python or Celery versions require older celery-batches releases.
License in practice
BSD-3-Clause is a permissive license that allows commercial and private use with minimal restrictions, requiring only preservation of copyright and license notices.
Quickstart
pip install celery-batches
from celery_batches import batch
from celery import Celery
app = Celery()
@app.task(base=batch)
def process_batch(tasks):
# tasks is a list of task arguments
passVerify before relying
- Whether the package's Alpha development status reflects stability concerns or is simply a conservative classification.
- Performance characteristics when batching large numbers of tasks or with high-frequency flush triggers.
- Specific configuration options for flush_every and flush_interval parameters.
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagecelery |
| Maintenance | Actively maintained 210 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 197,110 / month, #9,769 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: CPythonProgramming Language :: Python :: Implementation :: PyPyTopic :: Software Development :: Object BrokeringTopic :: System :: Distributed Computing |
Evidence: celery_batches-0.11-py3-none-any.whl
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See also celery · celery-stubs · celery-singleton · dvc-task · celery_once · django-celery-email · taskiq · apache-airflow-providers-celery · celery-progress · mixpanel-py-async