pam-python 0.1.39

Pam Python Library

pam-python-data-plugin-framework

This repository provides the pam CLI and runtime framework to build Data Plugin services for PAM Real CDP. It generates a ready-to-run project, standardizes service lifecycle, and handles common tasks like input handling, temp storage, uploads, and service monitoring.

This README is a practical, step-by-step guide you can follow to create and run a real service.

What you get

• CLI to initialize a project and scaffold services
• Service lifecycle contract (start, data input, upload, exit)
• Temp file and SQLite helpers
• A monitoring loop for service timeouts and periodic cleanup

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Table of Contents

1. Prerequisites
2. Install
3. Initialize a Project
4. Create a Service
5. Understand the Lifecycle
6. Using Temp Files Correctly
7. Running the Server
8. Testing a Service
9. Configuration
10. Project Structure
11. Troubleshooting

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Prerequisites

• Python 3.8+ recommended
• pip and a working virtual environment

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Install Create a project folder and a virtual environment.

mkdir my_data_plugin
cd my_data_plugin
python3 -m venv venv
source venv/bin/activate

Install the framework:

pip install pam-python

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Initialize a Project This creates a runnable project with templates (including AGENT.md).

pam init

When requirements.txt already exists, you will be prompted to choose how to proceed:

• overwrite
• keep
• merge

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Create a Service Generate a service scaffold. Do not hand-create service templates.

pam new service rfm_segment

This creates a new folder (e.g. rfm_segment/) with:

• a service class (RfmSegmentSvc.py)
• functions.py for your logic
• service.yaml for registration
• a test file

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Understand the Lifecycle The runtime calls your service in two main phases.

1. on_start

• Called once at the beginning
• Read parameters from self.request.runtime_parameters
• Should return quickly (start a thread for long work)

2. on_data_input

• Called when CDP sends input files
• req.input_files contains ordered CSV files
• Should also return quickly (use a thread if needed)

When your service is done:

• Call self._upload_result(...) or self._upload_report(...)
• Call self._exit() to signal completion

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Using Temp Files Correctly Temp storage is managed by the framework. Do not delete temp files manually.

Standard helpers:

• TempfileUtils.get_temp_path_for_service(self, self.service_name)
• TempfileUtils.get_temp_file_name_for_service(self, self.service_name, prefix, extension)

Notes:

• get_temp_path_for_service(...) returns a directory path without a trailing slash.
• The temp path includes date/service/token in this structure: TEMP_DATASOURCE_PATH/YYYY_MM_DD/<service>/<token>

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Uploading Results in Batches If your service produces too many rows (or too few per event), use the batch uploader to handle chunking and flushing automatically.

Recommended usage:

from pam.result_batch_uploader import ResultBatchUploader

batch_uploader = ResultBatchUploader(self, batch_size=50000)
batch_uploader.upload(df, "data-name")
batch_uploader.flush()
status = batch_uploader.get_status()

Notes:

• name separates different result streams (A/B) to avoid schema conflicts.
• flush() uploads any remaining rows that are below the batch size.

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Running the Server The generated main.py runs the Flask server.

python main.py

By default it binds to 0.0.0.0:8000. You can override with:

export SERVER_HOST=0.0.0.0
export SERVER_PORT=8000

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Testing a Service Run unit tests for a service:

pam test rfm_segment

If you write custom tests, place them in the service folder and name them test_<service>.py.

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Configuration Environment variables you can set:

• SERVER_HOST
• SERVER_PORT
• TEMP_BASE_PATH (default /app/data)
• TEMP_DATASOURCE_PATH (default /app/data/data_sources)
• TEMP_CLEAN_DAYS (default 10)
• TEMP_CLEAN_INTERVAL_HOURS (default 6, set empty to disable periodic cleanup)

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Project Structure After pam init and one service:

.
├── main.py
├── AGENT.md
├── Dockerfile
├── rfm_segment/
│   ├── RfmSegmentSvc.py
│   ├── functions.py
│   ├── service.yaml
│   └── test_rfm_segment.py
├── requirements.txt
└── run_unit_test.sh

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Troubleshooting

• If pam command is missing, ensure your virtualenv is activated.
• If pam new service fails, confirm the service name is provided.
• If temp cleanup is too frequent or too slow, adjust TEMP_CLEAN_INTERVAL_HOURS and TEMP_CLEAN_DAYS.

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Next Steps

• Implement your logic in functions.py.
• Wire it into on_start and on_data_input in your service class.
• Use the temp utilities to write intermediate files.
• Use _upload_result to return output to CDP.