semvec/pypi-packages huggingface dataset:
airflow-memray 0.1.1
Memory profiling for Airflow with Memray.
License: Apache License 2.0
Requires Python: ~=3.11

# Airflow Memray

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Memory profiling for Airflow with [Memray](https://bloomberg.github.io/memray/).

## Configuration Reference

This section contains the list of all the available Airflow Memray configurations that you can set in `airflow.cfg` file or using environment variables.

### base_folder

The base folder under which Airflow Memray will store profiling results. Possible values can be anything what is supported by [Airflow Object Storage](https://airflow.apache.org/docs/apache-airflow/stable/core-concepts/objectstorage.html).

If it refers to a local file system path, then it must be accessible by the task and the webserver.

**Default**: `"file:///tmp/airflow/memray"`

**Environment Variable**: `AIRFLOW__MEMRAY__BASE_FOLDER`

### storage_conn_id

The Airflow Connection to use if [base_folder](#base_folder) is set to a remote cloud storage location.

**Default**: `None`

**Environment Variable**: `AIRFLOW__MEMRAY__STORAGE_CONN_ID`

### tasks

The tasks to be profiled as a comma separated list of wildcard pattern as implemented by the [fnmatch](https://docs.python.org/3/library/fnmatch.html) module. The pattern are applied against the full task ID in the form `<dag_id>.<task_id>`.

Set it to `"*"` to profile all tasks.

**Default**: `""`

**Environment Variable**: `AIRFLOW__MEMRAY__TASKS`

## Airflow Summit 2024

I have given a presentation about this package at Airflow Summit 2024.

You can visit the [official page of the presentation](https://airflowsummit.org/sessions/2024/profiling-airflow-tasks-with-memray/) or directly watch video on YouTube by clicking on the following picture:

[![Profiling Airflow tasks with Memray](https://img.youtube.com/vi/QHLedv-j8Hc/0.jpg)](https://www.youtube.com/watch?v=QHLedv-j8Hc)