datadog.ipynb

datadog.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNjv8oVNv6WDcDPjghc7bwD",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/reachlin/76077b6c5d982c02cc5090966d78d8ac/datadog.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install datadog_api_client python-dateutil numpy pandas"
      ],
      "metadata": {
        "id": "NUd71Y7RwOrb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGzOfEW-wEz0",
        "outputId": "fd87a31e-1f1d-4db1-b3b4-40df78ce6113"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                              metric     timestamp  value\n",
            "0  avg:aws.rds.database_connections{dbinstanceide...  1.684398e+12   75.0\n",
            "1  avg:aws.rds.database_connections{dbinstanceide...  1.684398e+12   76.0\n",
            "2  avg:aws.rds.database_connections{dbinstanceide...  1.684398e+12   80.0\n",
            "3  avg:aws.rds.database_connections{dbinstanceide...  1.684398e+12   81.0\n",
            "4  avg:aws.rds.database_connections{dbinstanceide...  1.684398e+12   72.0\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "Query Datadog timeseries points and save to csv.\n",
        "\"\"\"\n",
        "\n",
        "import os\n",
        "import pandas as pd\n",
        "from datetime import datetime\n",
        "from dateutil.relativedelta import relativedelta\n",
        "from datadog_api_client import ApiClient, Configuration\n",
        "from datadog_api_client.v1.api.metrics_api import MetricsApi\n",
        "\n",
        "\n",
        "os.environ['DD_SITE'] =\"datadoghq.com\"\n",
        "os.environ['DD_API_KEY'] =\"...\"\n",
        "os.environ['DD_APP_KEY'] =\"...\"\n",
        "\n",
        "df = pd.DataFrame()\n",
        "\n",
        "configuration = Configuration()\n",
        "with ApiClient(configuration) as api_client:\n",
        "    api_instance = MetricsApi(api_client)\n",
        "    response = api_instance.query_metrics(\n",
        "        _from=int((datetime.now() + relativedelta(minutes=-30)).timestamp()),\n",
        "        to=int(datetime.now().timestamp()),\n",
        "        query=\"avg:aws.rds.database_connections{env:production} by {dbinstanceidentifier}\",\n",
        "    )\n",
        "\n",
        "    for item in response['series']:\n",
        "      print(f\"{item['expression']} {item['start']} - {item['end']}\")\n",
        "      data = item['pointlist']\n",
        "      df_item = pd.DataFrame(\n",
        "        {\n",
        "        'metric': f\"{item['expression']}\",\n",
        "        'timestamp': [x.value[0] for x in data], \n",
        "        'value': [x.value[1] for x in data]  \n",
        "        }\n",
        "      )\n",
        "      df = pd.concat([df, df_item], ignore_index=True)\n",
        "\n",
        "print(df.head())\n",
        "df.to_csv('metrcs.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# last 30 mins\n",
        "a = 1684397280000.0 - 1684395720000.0\n",
        "b = 1684395780000.0 - 1684395720000.0\n",
        "\n",
        "print(a/1000/60, b/1000/60)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9tLgFXbj6f5f",
        "outputId": "a48c6ef5-539b-4d06-d8fc-2ae1b759f240"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "26.0 1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# last 10 mins\n",
        "b = (1684396320000.0 - 1684396260000.0)/1000/60\n",
        "a = (1684396380000.0 - 1684395900000.0)/1000/60\n",
        "print(a, b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2iCfri93yzY",
        "outputId": "37946804-809c-462b-b7c2-d0ebf5d1858e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8.0 1.0\n"
          ]
        }
      ]
    }
  ]
}