{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generic Adder\n", "\n", "This presentations goal it to introduce the features of the `Generic Adder` and how to configure it." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The challenge\n", "\n", "I want add fields or values depending on a matching filter." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "from this:" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "document = {\n", " 'message': {\n", " \"time_in_ms\": \"bla\",\n", " \"tags\": [\"hello\"]\n", " }\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "to this:" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "expected = {\n", " 'message': {\n", " \"time_in_ms\": \"bla\",\n", " \"tags\": [\"hello\", \"new\"]\n", " }\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Create rule and processor" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create the rule:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create the processor config:" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "processor_config = {\n", " \"almighty generic adder\":{ \n", " \"type\": \"generic_adder\",\n", " \"rules\": [{\"filter\": \"*\", \"generic_adder\": {\"extend_target_list\": True, \"add\": {\"message.tags\": \"New\"}} }],\n", " }\n", " }" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "create the processor with the factory:" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "generic_adder" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from unittest import mock\n", "from logprep.factory import Factory\n", "\n", "mock_logger = mock.MagicMock()\n", "calculator = Factory.create(processor_config)\n", "calculator" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Process event" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "before: {'message': {'time_in_ms': 'bla', 'tags': ['hello']}}\n", "after: {'message': {'time_in_ms': 'bla', 'tags': ['hello', 'New']}}\n", "False\n" ] } ], "source": [ "from copy import deepcopy\n", "mydocument = deepcopy(document)\n", "\n", "\n", "print(f\"before: {mydocument}\")\n", "calculator.process(mydocument)\n", "print(f\"after: {mydocument}\")\n", "print(mydocument == expected)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.11.0 ('.venv': venv)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "586280540a85d3e21edc698fe7b86af2848b9b02644e6c22463da25c40a3f1be" } } }, "nbformat": 4, "nbformat_minor": 2 }