parides 0.7.1

Efficiently convert Prometheus metrics to Pandas DataFrames and Parquet/CSV files for Data Science.

🦋 Parides

Prometheus metrics directly into Pandas and Parquet.

Parides is a high-performance bridge between Prometheus and the Data Science stack. It automatically handles pagination, pivoting, and tabular alignment, delivering perfectly formatted data for machine learning and statistical analysis.

🚀 Quick Start

pip install parides

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📊 Three Ways to Use Parides

1. Interactive Data Science (Python API)

Perfect for Jupyter Notebooks. Fetch aligned metrics directly into a Pandas DataFrame.

from parides.prom_conv import from_prom_to_df

# Automatically handles pagination and alignment
df = from_prom_to_df(
    url="http://localhost:9090",
    metrics_query='irate(node_cpu_seconds_total{mode="idle"}[5m])'
)

# Ready for Scikit-Learn or Matplotlib
df.plot()

2. ML Training Data (High-Performance CLI)

Extract months of historical data directly to Parquet or CSV. Parides streams data to disk to keep memory usage low even for multi-gigabyte exports.

# Export 3 months of data using 1-day chunks to bypass API limits
parides http://localhost:9090 'node_cpu_seconds_total' \
    --start-date "2024-01-01T00:00:00Z" \
    --end-date "2024-04-01T00:00:00Z" \
    --chunk-size "1d" \
    --format parquet

3. DevOps & ETL Pipelines (Docker)

Run Parides as a standalone tool in CI/CD or automated pipelines without local Python dependencies.

docker run -v $(pwd)/data:/app/timeseries \
    ghcr.io/goettl79/parides http://prometheus:9090 "up" --format parquet

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💡 Why Parides?

• Bypass API Limits: Automatically chunks large time-range queries (--chunk-size) so you never hit "too many samples" errors.
• Zero-Config Alignment: Pivots long-format JSON into wide-format tables (features as columns, time as rows) — exactly what scikit-learn or PyTorch expect.
• Timezone Safe: All timestamps are strictly converted to UTC to prevent catastrophic time-shifting bugs in your models.
• Big Data Ready: CLI uses streaming writes (CSV/Parquet) to handle datasets larger than your system RAM.

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🤝 Contributing

We welcome contributions! Please see our Contributing Guidelines to get started.

To set up for local development:

git clone https://github.com/goettl79/parides.git
cd parides
poetry install

📄 License

This project is licensed under the Apache License 2.0.