Visualize data from Streams in PixieDust.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize data from the Streaming Analytics service using PixieDust\n",
    "\n",
    "You can follow the steps and code in this Python notebook to create a simple Python Streams application that runs on the Streaming Analytics service on IBM Bluemix. \n",
    "You then connect to the service to retrieve the data and visualize it using PixieDust.\n",
    "\n",
    "The setting is a call center for an airline.  The goal is to visualize a stream of customer complaints in real time and see which categories account for the most complaints.\n",
    "\n",
    "Familiarity with Python and Jupyter Notebooks is recommended. This notebook runs on **Python 3.5.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of contents\n",
    "***\n",
    "1.\t[Setup](#setup)\n",
    "2.  [Create a Python application](#create_data)\n",
    "1.\t[View the data with PixieDust](#visualize)\n",
    "1.\t[Problem statement](#summary)\n",
    "5.  [Stop the application](#stop)\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"setup\"></a>\n",
    "### 1.1 Start the Streaming Analytics service\n",
    "\n",
    "Open the Bluemix web portal and <a href=\"https://console.ng.bluemix.net/\" target=\"_blank\" rel=\"noopener noreferrer\">log in or sign up for a free Bluemix account</a>.\n",
    "\n",
    "Ensure that your Streaming Analytics service is running in IBM Bluemix.\n",
    "\n",
    "**If you don’t have a service,** you can create one as follows:  \n",
    "1. Click **Catalog** or **Create Service**, browse for the `Streaming Analytics` and then click on it. <br>\n",
    "1. Follow the instructions on the  Streaming Analytics catalog page, type the *Service name* to set up your service.\n",
    "<img src='https://github.com/orzade/streamsx-notebooks/blob/master/servicename.png?raw=true' alt=\"Type your service name and click on Create\" title=\"Streaming Analytics catalog - Type your service name\"></img><br>\n",
    "1. Click **Create** to open the Streaming Analytics service dashboard.<br> Your service starts automatically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"access\"></a>\n",
    "### 1.2 Set up access to the service\n",
    "\n",
    "Before you create an app with this notebook, you must first provide the information that your streaming app needs to access the service. You can find this information on the Streaming Analytics service dashboard. It includes the service name, the service credentials, and the connection URL for the service. \n",
    "In the next cell you have to enter your service credentials. To copy your service credentials, open the Streaming Analytics service dashboard click **Service Credentials**, then **View Credentials**, and finally click the Copy icon.\n",
    "\n",
    "<img src='https://github.com/orzade/streamsx-notebooks/blob/master/copyservicecredentials.png?raw=true' alt=\"Copy your service credentials\" title=\"Streaming Analytics catalog - Copy your service credentials\"></img>\n",
    "Then paste those credentials where indicated in the cell below.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "\n",
    "def get_sa_credentials():\n",
    "    #Paste the credentials you got from the screenshot above here\n",
    "    credentials = {}\n",
    "    \n",
    "    \"\"\" #should look like this:\n",
    "      credentials = {  \"password\": \"212358pp8\",\n",
    "      \"rest_port\": \"443\",\n",
    "      \"bundles_path\": \"/jax-rs/bundles/service_instances/abcdef\",\n",
    "      \"resources_path\": \"/jax-rs/resources/service_instances/abcdef\",\n",
    "      \"stop_path\": \"/jax-rs/streams/stop/service_instances/\",\n",
    "      \"rest_host\": \"streams-app-service.ng.bluemix.net\",\n",
    "      \"size_path\": \"/jax-rs/streams/size/abcdef\",\n",
    "      \"jobs_path\": \"/jax-rs/j....\",\n",
    "      \"start_path\": \"/jax-rs/strea......22f\",\n",
    "      \"rest_url\": \"https://streams-app-service.ng.bluemix.net\",\n",
    "      \"userid\": \"abcdef\",\n",
    "      \"status_path\": \"xxxxxx41cc22f\"\n",
    "      }\n",
    "    \"\"\"\n",
    "    return json.dumps(credentials)\n",
    "\n",
    "def get_service_name():\n",
    "    return \"service_name\" #Change this to your service name..so in our screenshot above it would be \"test1\"\n",
    "\n",
    "#authenticate and connect using credenetials above\n",
    "def do_auth():\n",
    "    _creds = get_sa_credentials()\n",
    "    service_name = get_service_name()\n",
    "    vs={'streaming-analytics': [{'name': service_name, 'credentials': json.loads (_creds)}]}\n",
    "    cfg = {}\n",
    "    cfg[ConfigParams.VCAP_SERVICES] = vs\n",
    "    cfg[ConfigParams.SERVICE_NAME] = service_name\n",
    "   \n",
    "    return cfg\n",
    "#submit job to service\n",
    "def submit_to_service(topo): \n",
    "    cfg = do_auth()\n",
    "    rc = submit('STREAMING_ANALYTICS_SERVICE', topo, cfg)\n",
    "    return rc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"create_data\"></a>\n",
    "# 2. Create the streams application\n",
    "\n",
    "Run the following cell to create the Python application that generates data.  The application is submitted to the Streaming Analytics service.  Every time you run the cell it creates a new job, so you only need to run this cell once unless you restart the kernel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Jul 13, 2017 11:33:10 AM com.ibm.streamsx.topology.internal.context.remote.BuildServiceRemoteRESTWrapper remoteBuildAndSubmit\n",
      "INFO: Streaming Analytics service (service_name): Checking status\n",
      "Jul 13, 2017 11:33:13 AM com.ibm.streamsx.topology.internal.streaminganalytics.RestUtils checkInstanceStatus\n",
      "INFO: Streaming Analytics service (service_name): instance status response:{\"state\":\"STARTED\",\"plan\":\"Standard\",\"enabled\":true,\"status\":\"running\"}\n",
      "Jul 13, 2017 11:33:13 AM com.ibm.streamsx.topology.internal.context.remote.BuildServiceRemoteRESTWrapper remoteBuildAndSubmit\n",
      "INFO: Streaming Analytics service (service_name): submitting build Complaints_7C08BFE79F5A5642\n",
      "Jul 13, 2017 11:33:55 AM com.ibm.streamsx.topology.internal.context.remote.BuildServiceRemoteRESTWrapper remoteBuildAndSubmit\n",
      "INFO: Streaming Analytics service (service_name): submitting job request.\n",
      "Jul 13, 2017 11:34:03 AM com.ibm.streamsx.topology.internal.context.remote.BuildServiceRemoteRESTWrapper doSubmitJobFromBuildArtifactPut\n",
      "INFO: Streaming Analytics service (service_name): submit job response: {\"artifact\":\"6114\",\"jobId\":\"1\",\"application\":\"ipythoninput6e265c42bcf67::Complaints\",\"name\":\"ipythoninput6e265c42bcf67::Complaints_1\",\"state\":\"STARTED\",\"plan\":\"Standard\",\"enabled\":true,\"status\":\"running\"}\n",
      "Submitted job to the service\n"
     ]
    }
   ],
   "source": [
    "from streamsx.topology.topology import Topology\n",
    "from streamsx.topology.context import *\n",
    "from streamsx.rest import StreamingAnalyticsConnection\n",
    "from streamsx.topology import schema\n",
    "import random\n",
    "import json\n",
    "import math\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def generate_data():\n",
    "    categories = [\"Punctuality\",\"Safety\",\"Ticketing\",\"Food\",\"Luggage\",\"Customer Service\"]\n",
    "    while True:\n",
    "        value =  random.random() * 100\n",
    "        index = math.floor(random.random() * 5 )\n",
    "        payload = {\"n_complaints\" : value, \"category\" : categories[index]} \n",
    "        yield payload\n",
    "    \n",
    "topo = Topology('Complaints')\n",
    "#data source is generated but could easily be from MQTT/Kafka etc\n",
    "data_stream = topo.source(generate_data) \n",
    "#use a filter to only see categories with more than 10 complaints\n",
    "filtered = data_stream.filter(lambda tuple : tuple[\"n_complaints\"] > 10)\n",
    "#create a view on that filtered stream\n",
    "filtered.print()\n",
    "complaints = filtered.view()\n",
    "\n",
    "\n",
    "#submit the job to the Streaming Analytics service\n",
    "result = submit_to_service(topo)\n",
    "print(\"Submitted job to the service\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"visualize\"></a>\n",
    "# 3. Visualize the data in PixieDust\n",
    "\n",
    "Create a pie chart to visualize the distribution of purchases. When you run the cell, the default display will be a table.\n",
    "\n",
    "To create a pie chart:\n",
    "- Select the chart option, choose \"Pie Chart\".\n",
    "- Set Keys to \"Category\" and \"Values\" to \"n_complaints\".\n",
    "- Under \"Aggregation\", select \"Count\" and enter 500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "pixiedust": {
     "displayParams": {
      "aggregation": "COUNT",
      "handlerId": "pieChart",
      "keyFields": "category",
      "legend": "true",
      "mpld3": "false",
      "rowCount": "500",
      "valueFields": "n_complaints"
     }
    }
   },
   "outputs": [
    {
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\" class=\"pd_save\"></center>\n",
       "                        \n",
       "                    \n",
       "                \n",
       "        </div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "import pandas\n",
    "import pixiedust\n",
    "\n",
    "#get data from the service \n",
    "def get_latest_batch():\n",
    "    list_items = []\n",
    "    queue = complaints.start_data_fetch()\n",
    "    #print (\"Fetching data from view\")\n",
    "    for i in range(500): #just get the last 500 complaints for now, but queue.get is a potentially infinite stream\n",
    "       val = queue.get()\n",
    "       list_items.append(val)\n",
    "    return list_items\n",
    "\n",
    "\n",
    "list_items = get_latest_batch()\n",
    "buffer = json.dumps(list_items)\n",
    "pandas_df = pandas.DataFrame(data=json.loads(buffer))\n",
    "display(pandas_df)\n",
    "\n",
    "\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<a id=\"summary\"></a>\n",
    "# 4. Problem\n",
    "\n",
    "The stream of data is potentially infinite.  It would be nice if the graph was refreshed periodically, but there's no way to refresh the data with PixieDust. I have to re-run the above cell periodically to refresh the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"stop\"></a>\n",
    "# 5. Stop the application\n",
    "Run this cell to cancel the application, otherwise it runs indefinitely, consuming resources in your service. If you run this cell you need to re-run cell #2 to [generate data for the chart](#create_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "No resource matching: 44",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-64-cc52e538440f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjob\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mjob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcancel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cancelled the job. Re-run step 2 to start generating data again.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/gpfs/fs01/user/s623-f8b248de4f938e-3d6cff768616/.local/lib/python3.5/site-packages/streamsx/topology/context.py\u001b[0m in \u001b[0;36mjob\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    711\u001b[0m             \u001b[0msc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstreamsConnection\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    712\u001b[0m             \u001b[0minst\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_instance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minstanceId\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 713\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0minst\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_job\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjobId\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    714\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    715\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/gpfs/fs01/user/s623-f8b248de4f938e-3d6cff768616/.local/lib/python3.5/site-packages/streamsx/rest_primitives.py\u001b[0m in \u001b[0;36mget_job\u001b[1;34m(self, id)\u001b[0m\n\u001b[0;32m    476\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    477\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_job\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 478\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_element_by_id\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjobs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'jobs'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mJob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    479\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    480\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_imported_streams\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m/gpfs/fs01/user/s623-f8b248de4f938e-3d6cff768616/.local/lib/python3.5/site-packages/streamsx/rest_primitives.py\u001b[0m in \u001b[0;36m_get_element_by_id\u001b[1;34m(self, url, key, eclass, id)\u001b[0m\n\u001b[0;32m     97\u001b[0m         \u001b[0melements\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_elements\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0meclass\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     98\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0melements\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 99\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"No resource matching: {0}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    100\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0melements\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0melements\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: No resource matching: 44"
     ]
    }
   ],
   "source": [
    "job = result.job\n",
    "if job.cancel():\n",
    "    print(\"Cancelled the job. Re-run step 2 to start generating data again.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Copyright © 2017 IBM. This notebook and its source code are released under the terms of the MIT License."
   ]
  }
 ],
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