prometheus-anomaly-detection-lstm 0.1.7

Prometheus time series anomaly detection with LSTM Autoencoder

Prometheus Time Series Anomaly Detection with LSTM Autoencoder

This project implements a system for detecting anomalies in time series data collected from Prometheus. It uses an LSTM (Long Short-Term Memory) autoencoder model built with TensorFlow/Keras to learn normal patterns from your metrics and identify deviations. The system includes scripts for data collection, preprocessing, model training, data filtering, and real-time anomaly detection, exposing results via a Prometheus exporter.

Features

• Data Collection: Fetches time series data from a Prometheus instance for specified PromQL queries. The resulting dataset contains day_of_week and hour_of_day columns derived from timestamps.
• Preprocessing: Handles missing values and normalizes/scales values for optimal model training. The day-of-week and hour-of-day features are also ensured at this stage.
• LSTM Autoencoder Training: Trains an LSTM autoencoder on the full preprocessed dataset.
• Data Filtering: An optional script to apply the trained model to filter out anomalous sequences from a dataset for further analysis.
• Real-time Anomaly Detection: Continuously monitors new data and processes it with the trained model to detect anomalies.
• Prometheus Exporter Integration: Exposes key anomaly detection metrics (e.g., reconstruction error, anomaly flag, per-feature errors) that can be scraped by Prometheus and monitored with tools like Grafana.
• Configurable: All stages are highly configurable via a central config.yaml file.

WIKI: deepwiki

Project Structure

.
├── config.yaml                 # Central configuration file for all scripts
├── cli.py                      # Command-line utility to run workflow stages
├── data_collector.py           # Script to collect historical data from Prometheus
├── preprocess_data.py          # Script to preprocess the collected data
├── train_autoencoder.py        # Script to train the LSTM autoencoder
├── filter_anomalous_data.py    # Optional script to filter data using the trained model
├── realtime_detector.py        # Script for real-time anomaly detection and Prometheus exporter
├── Pipfile                     # Dependency declarations
├── Pipfile.lock                # Locked versions of dependencies
└── README.md                   # This file

Prerequisites

• Python 3.12.
• Pipenv for managing dependencies.
• A running Prometheus instance (v2.x or later) that is scraping the metrics you want to analyze.
• (Optional) Exporters configured for your Prometheus to collect the desired metrics (e.g., windows_exporter).

Setup & Installation

1. Clone the Repository:

   git clone <your-repository-url>
   cd <repository-name>
   

2. Install Dependencies with Pipenv:

   pipenv install --dev
   

   After installation you can enter the environment using pipenv shell or run scripts with pipenv run.

3. Prometheus Setup: Ensure your Prometheus server is running and accessible. The scripts will query this server based on the URL and PromQL queries defined in config.yaml. The example queries in config.yaml might use metrics from windows_exporter; adapt these to your own available metrics.

Configuration (config.yaml)

The config.yaml file is central to running this project. Key sections include:

• prometheus_url: URL of your Prometheus server.
• queries: Dictionary of PromQL queries with friendly aliases.
• data_settings: Parameters for data_collector.py (e.g., collection_period_hours, step, output_filename).
• preprocessing_settings: Parameters for preprocess_data.py (e.g., nan_fill_strategy, scaler_type, processed_output_filename, scaler_output_filename).
• training_settings: Parameters for train_autoencoder.py.
  • model_output_filename: Filename for the trained model.
  • sequence_length, train_split_ratio, epochs, batch_size, learning_rate, early_stopping_patience: Standard training hyperparameters.
  • lstm_units_encoder1, etc.: LSTM autoencoder architecture definition.
• data_filtering_settings: Parameters for the optional filter_anomalous_data.py script.
  • normal_sequences_output_filename: Output file for sequences classified as normal.
  • anomalous_sequences_output_filename: Output file for sequences classified as anomalous.
• real_time_anomaly_detection: Parameters for realtime_detector.py.
  • query_interval_seconds: How often to fetch new data.
  • anomaly_threshold_mse: Crucial! MSE threshold for declaring an anomaly. Tune this based on the error histogram generated during training.
  • exporter_port: Port for the Prometheus exporter.
  • metrics_prefix: Prefix for exposed Prometheus metrics.

Before running any script, review and customize config.yaml thoroughly.

Usage / Workflow

The project follows a sequential workflow. Each stage can be launched via the cli.py utility:

python cli.py collect       # сбор данных
python cli.py preprocess    # предобработка
python cli.py train         # обучение модели
python cli.py detect        # запуск realtime детектора

The sequential workflow is as follows:

Step 1: Data Collection (data_collector.py) Collect historical data from your Prometheus instance.

python data_collector.py

Output: Raw data Parquet file (e.g., prometheus_metrics_data.parquet) which includes day_of_week and hour_of_day columns.

Step 2: Data Preprocessing (preprocess_data.py) Preprocess the collected data (handles NaNs, scales features).

python preprocess_data.py

Outputs: A processed data Parquet file (e.g., processed_metrics_data.parquet) and a saved scaler (e.g., fitted_scaler.joblib).

Step 3: Train Model (train_autoencoder.py) Train the LSTM autoencoder on the entire preprocessed dataset from Step 2.

python train_autoencoder.py

Outputs:

• A trained Keras model (e.g., lstm_autoencoder_model.keras).
• A training history plot (training_history_loss_...png).
• A reconstruction error histogram (reconstruction_error_histogram_...png). Use this histogram to determine an appropriate value for anomaly_threshold_mse in config.yaml.

Step 4: Real-time Anomaly Detection (realtime_detector.py) Run the real-time detector using the trained model from Step 3.

• Ensure model_output_filename in training_settings points to your trained model.
• Ensure anomaly_threshold_mse in real_time_anomaly_detection is correctly set based on the histogram from Step 3.

python realtime_detector.py

The detector starts a Prometheus exporter (e.g., on http://localhost:8901/metrics).

Optional Step: Filter Data (filter_anomalous_data.py) Use the trained model from Step 3 to classify sequences in your dataset as "normal" or "anomalous" for analysis.

• Ensure anomaly_threshold_mse is appropriately set in config.yaml.
• Configure output filenames in data_filtering_settings.

python filter_anomalous_data.py

Outputs: .npy files containing the normal and anomalous sequences.

Monitoring (Prometheus & Grafana)

Configure Prometheus to scrape the metrics endpoint from realtime_detector.py. Visualize metrics like:

• anomaly_detector_is_anomaly_detected
• anomaly_detector_total_anomalies_count_total
• 'anomaly_detector_feature_reconstruction_error_mse{feature_name="your_alias"}`

Interpreting Results

• Monitoring Metrics: Observe the is_anomaly_detected and latest_reconstruction_error_mse metrics in real time to evaluate detection behavior.
• Per-Feature Errors: When an anomaly is flagged, check the corresponding feature_reconstruction_error_mse metrics (and the logs of realtime_detector.py) to see which specific time series (features) are contributing most to the anomaly.

Customization & Extending

• Monitoring New Metrics: Add new PromQL queries to config.yaml. Retrain the model (run steps 2-3) to include these new features.
• Tuning Anomaly Threshold: The anomaly_threshold_mse value is critical. Adjust it based on the training error histogram and desired sensitivity.
• Model Architecture: Modify LSTM parameters in the training_settings section of config.yaml.

Troubleshooting

• Python Dependencies: Ensure Pipfile/Pipfile.lock are in sync and run pipenv install if packages change.
• Prometheus Connection: Verify prometheus_url and query validity in config.yaml.
• Data Issues: Check for "No data found" errors; inspect PromQL queries and Prometheus scrape targets. Review nan_fill_strategy if NaNs persist.
• Model Training: If loss doesn't decrease, adjust learning rate, batch size, or architecture. EarlyStopping is configured to prevent overfitting.
• File Not Found: Double-check filenames in config.yaml against actual generated files (models, scalers, datasets).
• Port in Use: If realtime_detector.py fails, the exporter_port might be occupied by another process.

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request.

License

This project is licensed under the MIT License.