dag-cost-tracker 0.1.1

A DAG cost tracking plugin for Apache Airflow

💰 DAG Cost Tracker

DAG Cost Tracker is a powerful observability and FinOps tool designed for Apache Airflow. It tracks execution time and compute resource usage to provide granular cost estimates for individual DAG runs, helping engineering teams optimize their data pipeline spending.

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🚀 Features

• Granular Cost Tracking: Track costs at the DAG and task level.
• Customizable Pricing: Configure warehouse rates (e.g., Snowflake credits) to match your contract.
• CLI Reporting: Generate beautiful cost reports and inspect expensive DAGs from your terminal.
• Seamless Integration: Plugs directly into Airflow task hooks—no DAG code changes required.
• Lightweight: Stores cost history in a local SQLite database (zero external infrastructure dependencies).

📦 Installation

Install easily via pip:

pip install dag-cost-tracker

⚙️ Configuration

1. Create Config File: Create a config.yaml at ~/.dag_cost_tracker/config.yaml to define your compute pricing.

   warehouses:
     COMPUTE_WH_SMALL:
       credits_per_hour: 1
       cost_per_credit: 3.00
     COMPUTE_WH_LARGE:
       credits_per_hour: 4
       cost_per_credit: 3.00
   

2. Enable Plugin: The plugin auto-registers with Airflow upon installation. Restart your Airflow Scheduler and Webserver to start tracking.

📊 Usage

Generate a Cost Report

See your most expensive DAGs at a glance:

dag-cost report --top 10 --period 30d

Output:

+-------------------+--------+--------------+----------------+
| DAG ID            |   Runs | Total Cost   | Avg Cost/Run   |
+===================+========+==============+================+
| etl_daily         |      7 | $148.66      | $21.24         |
+-------------------+--------+--------------+----------------+
| ml_training       |      7 | $115.77      | $16.54         |
+-------------------+--------+--------------+----------------+

Inspect a Specific DAG

Drill down to find which tasks are driving up costs:

dag-cost inspect etl_daily

🛠️ Development

1. Clone the repository.
2. Install dependencies: pip install -e .
3. Run tests: pytest

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👤 Author

Azmat Siddique

If you found this tool useful, consider buying me a coffee!