final_app.ipynb

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    "displayHTML(\"\"\"<center><font size=\"8\" color=\"#39ac73\" face=\"sans-serif\">AttractMe - Dublin</font></center> \"\"\")"
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       "<center><font size=\"8\" color=\"#39ac73\" face=\"sans-serif\">AttractMe - Dublin</font></center> "
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  {
   "cell_type": "markdown",
   "source": [
    "*Dear User,<br />\n",
    "Please choose your origin bus stop in one of the options above.<br /><br />\n",
    "For Stream Sources, enter your api in the \"API\" option.<br />\n",
    "For Batch Sources, enter your json path in the \"Json path\" option.<br />\n",
    "For a single source, choose one of the bus stops options presented in the \"Source Bus Stop\" option.*"
   ],
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  {
   "cell_type": "code",
   "source": [
    "from pyspark.sql.types import *\n",
    "from elasticsearch import Elasticsearch, helpers\n",
    "from pyspark.sql.functions import col, split, randn\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from json import dumps\n",
    "import json\n",
    "import pickle\n",
    "import pyspark.sql.functions as F\n",
    "from collections import defaultdict\n",
    "\n",
    "ES_HOST= '10.0.0.20'  #please change the ES_HOST to your VM server\n",
    "es = Elasticsearch([{'host': ES_HOST}], timeout=60000)"
   ],
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "title": "imports",
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  {
   "cell_type": "code",
   "source": [
    "schema_structfields = [\n",
    "                      StructField(\"_id\", MapType(StringType(), StringType(), True),True), \n",
    "                       StructField(\"actualDelay\",LongType(),True),\n",
    "                       StructField(\"angle\",DoubleType(),True),\n",
    "                       StructField(\"anomaly\",BooleanType(),True),\n",
    "                       StructField(\"areaId\",LongType(),True),\n",
    "                       StructField(\"areaId1\",LongType(),True),\n",
    "                       StructField(\"areaId2\",LongType(),True),\n",
    "                       StructField(\"areaId3\",LongType(),True),\n",
    "                       StructField(\"atStop\",BooleanType(),True),\n",
    "                       StructField(\"busStop\",LongType(),True),\n",
    "                       StructField(\"calendar\",MapType(StringType(), StringType(),True),True),\n",
    "                       StructField(\"congestion\",BooleanType(),True),\n",
    "                       StructField(\"currentHour\",LongType(),True),\n",
    "                       StructField(\"dateType\",LongType(),True),\n",
    "                       StructField(\"dateTypeEnum\",StringType(),True),\n",
    "                       StructField(\"delay\",LongType(),True),\n",
    "                       StructField(\"direction\",LongType(),True),\n",
    "                       StructField(\"distanceCovered\",DoubleType(),True),\n",
    "                       StructField(\"ellapsedTime\",LongType(),True),\n",
    "                       StructField(\"filteredActualDelay\",LongType(),True),\n",
    "                       StructField(\"gridID\",StringType(),True),\n",
    "                       StructField(\"journeyPatternId\",StringType(),True), \n",
    "                       StructField(\"justLeftStop\",BooleanType(),True),\n",
    "                       StructField(\"justStopped\",BooleanType(),True),\n",
    "                       StructField(\"latitude\",DoubleType(),True), \n",
    "                       StructField(\"lineId\",StringType(),True), \n",
    "                       StructField('loc',StructType([StructField('coordinates',ArrayType(DoubleType(),True),True),StructField('type',StringType(),True)]),True),\n",
    "                       StructField(\"longitude\",DoubleType(),True),  \n",
    "                       StructField(\"poiId\",LongType(),True),\n",
    "                       StructField(\"poiId2\",LongType(),True),\n",
    "                       StructField(\"probability\",DoubleType(),True),\n",
    "                       StructField(\"systemTimestamp\",DoubleType(),True),\n",
    "                       StructField(\"timestamp\",MapType(StringType(),StringType(),True)), \n",
    "                       StructField(\"vehicleId\",LongType(),True),\n",
    "                       StructField(\"vehicleSpeed\",LongType(),True)]\n",
    "\n",
    "schema = StructType(schema_structfields)"
   ],
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     "title": "dublin data schema",
     "showTitle": true,
     "inputWidgets": {},
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     "output_type": "display_data",
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       "datasetInfos": [],
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   "execution_count": 0
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  {
   "cell_type": "code",
   "source": [
    "def read_stream_data(api,schema):\n",
    "  # kafka_server = '10.0.0.30:9091'\n",
    "  kafka_server = api\n",
    "\n",
    "\n",
    "  # Subscribe to a pattern\n",
    "  kafka_raw_df = spark \\\n",
    "    .readStream \\\n",
    "    .format(\"kafka\") \\\n",
    "    .option(\"kafka.bootstrap.servers\", kafka_server) \\\n",
    "    .option(\"subscribePattern\", \"vehicleId_.*\") \\\n",
    "    .option(\"startingOffsets\", \"earliest\").load()\n",
    "\n",
    "  kafka_value_df = kafka_raw_df.selectExpr(\"CAST(value AS STRING)\")\n",
    "\n",
    "  # schema = pickle.load(open(\"/dbfs/mnt/schema.pkl\", \"rb\"))\n",
    "\n",
    "  stream_raw_df = kafka_value_df \\\n",
    "             .select(F.from_json(F.col(\"value\"), schema=schema).alias('json')) \\\n",
    "             .select(\"json.*\")\n",
    "\n",
    "  return stream_raw_df"
   ],
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     "title": "read stream data",
     "showTitle": true,
     "inputWidgets": {},
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     "output_type": "display_data",
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       "datasetInfos": [],
       "data": "<div class=\"ansiout\"></div>",
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   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "def read_static_data(path,schema):\n",
    "  raw_df = spark.read.json(path, schema=schema)\n",
    "  return raw_df"
   ],
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "title": "read static data",
     "showTitle": true,
     "inputWidgets": {},
     "nuid": "22693a0d-0f2c-4488-ba1b-58de497c34fe"
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    {
     "output_type": "display_data",
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "datasetInfos": [],
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   ],
   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "def fix_drop_cols_from_df(df):\n",
    "  df = df.withColumn('line_num', df['journeyPatternId'][0:4])\n",
    "  df = df.withColumn('line_num', F.regexp_replace('line_num', '^0+', '')) \n",
    "  df = df.withColumn('direction', df['journeyPatternId'][5:5][0:1])\n",
    "  df = df.withColumn('mins_delay', df.delay / 60)\n",
    "  df = df.withColumnRenamed('busStop','OriginStop')\n",
    "\n",
    "  df = df.withColumn('timestamp', F.map_values(df.timestamp)[0])\n",
    "  df = df.withColumn('timestamp', F.to_timestamp(F.from_unixtime(df.timestamp / (1000))))\n",
    "\n",
    "  input_df=df[\"line_num\",\"mins_delay\",\"OriginStop\",\"timestamp\"]\n",
    "  return input_df"
   ],
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     "title": "fix &keep needed columns ",
     "showTitle": true,
     "inputWidgets": {},
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    {
     "output_type": "display_data",
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "datasetInfos": [],
       "data": "<div class=\"ansiout\"></div>",
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   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "# def write_stream_to_elastic(df_to_elastic, index, settings_with_mapping, ES_HOST,checkpoints_location):\n",
    "#   if not es.indices.exists(index):\n",
    "#     es.indices.create(index=index, ignore=400, body=settings_with_mapping)\n",
    "#   df_to_elastic.writeStream \\\n",
    "#       .outputMode(\"append\") \\\n",
    "#       .queryName(f\"{index}_to_es\") \\\n",
    "#       .format(\"org.elasticsearch.spark.sql\") \\\n",
    "#       .option(\"es.nodes.wan.only\",\"true\") \\\n",
    "#       .option(\"checkpointLocation\", checkpoints_location) \\\n",
    "#       .option(\"es.resource\", index) \\\n",
    "#       .option(\"es.nodes\", ES_HOST) \\\n",
    "#       .option(\"es.port\",\"9200\") \\\n",
    "#       .start()\n",
    "\n",
    "  \n",
    "# index=\"lines_delay_time\"\n",
    "# # es.indices.delete(index=index)\n",
    "# lines_delay_time_schema  = {\n",
    "#     \"settings\": {\n",
    "#         \"number_of_shards\": 1,\n",
    "#         \"number_of_replicas\": 0,\n",
    "#         \"refresh_interval\" : -1\n",
    "#     },\n",
    "#      \"mappings\": {\n",
    "#       \"properties\": {\n",
    "#           \"mins_delay\" : { \"type\": \"long\" },#*\n",
    "#           \"busStop\" : { \"type\": \"long\" },#*\n",
    "#           \"timestamp\" : { \"type\": \"date\"}, \n",
    "#           \"line_num\" : { 'type' : 'keyword'},#*\n",
    "#       }\n",
    "#      }\n",
    "# }\n",
    "# write_stream_to_elastic(input_stream_df, index, lines_delay_time_schema, ES_HOST,\"/tmp/almog.gueta/streaming\")\n"
   ],
   "metadata": {
    "application/vnd.databricks.v1+cell": {
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     "showTitle": true,
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     "output_type": "display_data",
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      "application/vnd.databricks.v1+output": {
       "datasetInfos": [],
       "data": "<div class=\"ansiout\"></div>",
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   ],
   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "lines_att_df=spark.read.csv('atrractions_with_busStop.csv',header=True)\n",
    "lines_att_pd=lines_att_df.toPandas()\n",
    "good_sources=list(set(lines_att_pd[\"sourceStop\"]))\n",
    "good_sources.sort()"
   ],
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "title": "read lines_att_df",
     "showTitle": true,
     "inputWidgets": {},
     "nuid": "62a2e528-4fae-4649-9f65-05d825c24b57"
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   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "datasetInfos": [
        {
         "name": "lines_att_df",
         "typeStr": "pyspark.sql.dataframe.DataFrame",
         "schema": {
          "fields": [
           {
            "metadata": {},
            "name": "sourceStop",
            "nullable": true,
            "type": "string"
           },
           {
            "metadata": {},
            "name": "line_num",
            "nullable": true,
            "type": "string"
           },
           {
            "metadata": {},
            "name": "attraction",
            "nullable": true,
            "type": "string"
           },
           {
            "metadata": {},
            "name": "catagory",
            "nullable": true,
            "type": "string"
           },
           {
            "metadata": {},
            "name": "destStop",
            "nullable": true,
            "type": "string"
           }
          ],
          "type": "struct"
         },
         "tableIdentifier": null
        }
       ],
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   ],
   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "def main_app(input_stream_df,source_bus_stop):\n",
    "  q={\n",
    "    \"aggs\": {\n",
    "      \"all_lines\": {\"terms\": {\"field\": \"line_num\",\"size\":300 },\n",
    "        \"aggs\": {\"avg_delay\": {\"top_metrics\": { \"metrics\": {\"field\": \"mins_delay\"},\"sort\": {\"timestamp\": \"desc\"} }}}}}\n",
    "  }\n",
    "  lines_delay = es.search(index='lines_delay_time', body=q)\n",
    "  lines = [line['key'] for line in lines_delay['aggregations']['all_lines']['buckets']]\n",
    "  delays = [_delay[\"avg_delay\"][\"top\"][0]['metrics'][\"mins_delay\"] for _delay in lines_delay['aggregations']['all_lines']['buckets']]\n",
    "  lines_delay_df = pd.DataFrame({\"line_num\":lines,\"last_delay\":delays})\n",
    "  def play_app(source):\n",
    "    optional_atts=lines_att_pd[lines_att_pd.sourceStop==str(source)]#.filter(lines_att_df.sourceStop==str(source))\n",
    "    num_options=optional_atts.head(2)\n",
    "    if len(num_options)==0:\n",
    "      return \"We are sorry, there is no recommended attraction that can be reached from your location with a single bus ride\"\n",
    "    elif len(num_options)==1:\n",
    "      output_list = optional_atts.to_dict(\"records\")\n",
    "      return f\"Take line number {output_list[0]['line_num']}, to bus stop number {output_list[0]['destStop']} and enjoy the '{output_list[0]['attraction']}' attraction\" \n",
    "    else:\n",
    "      joined=pd.merge(optional_atts,lines_delay_df,how=\"left\",on=[\"line_num\"]).sort_values(by=['last_delay'])\n",
    "      selected=joined.iloc[0]\n",
    "      return f\"Take line number {selected['line_num']}, to bus stop number {selected['destStop']} and enjoy the '{selected['attraction']}' attraction\"\n",
    "  if source_bus_stop:\n",
    "    return play_app(source_bus_stop)\n",
    "  appUDF = F.udf(lambda source:play_app(source))   \n",
    "  return input_stream_df.withColumn('Output', appUDF(F.col('OriginStop')))"
   ],
   "metadata": {
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     "title": "play udf",
     "showTitle": true,
     "inputWidgets": {},
     "nuid": "12977b3d-f120-4c70-8a07-9d2f1b0c92ac"
    }
   },
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    {
     "output_type": "display_data",
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "datasetInfos": [],
       "data": "<div class=\"ansiout\"></div>",
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       "<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"
      ]
     }
    }
   ],
   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "API=dbutils.widgets.get(\"API\")\n",
    "Path=dbutils.widgets.get(\"Json path\")\n",
    "Source=dbutils.widgets.get(\"Source Bus Stop\")\n",
    "\n",
    "api_choosed=int(API!=\"Please enter your API here\" )\n",
    "path_choosed=int(Path!=\"Please enter your path here\")\n",
    "source_choosed=int(Source!=\"Choose source\")\n",
    "sum_cond=api_choosed+path_choosed+source_choosed\n",
    "not_finished = True\n"
   ],
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "title": "Get starting paramenters",
     "showTitle": true,
     "inputWidgets": {},
     "nuid": "853208d5-280b-4f34-aee7-b4e6f6ad1684"
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "metadata": {
      "application/vnd.databricks.v1+output": {
       "datasetInfos": [],
       "data": "<div class=\"ansiout\"></div>",
       "removedWidgets": [],
       "addedWidgets": {},
       "type": "html",
       "arguments": {
        "API": "Please enter your API here",
        "Source Bus Stop": "1013",
        "Json path": "Please enter your path here"
       }
      }
     },
     "data": {
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       "<style scoped>\n  .ansiout {\n    display: block;\n    unicode-bidi: embed;\n    white-space: pre-wrap;\n    word-wrap: break-word;\n    word-break: break-all;\n    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n    font-size: 13px;\n    color: #555;\n    margin-left: 4px;\n    line-height: 19px;\n  }\n</style>\n<div class=\"ansiout\"></div>"
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     }
    }
   ],
   "execution_count": 0
  },
  {
   "cell_type": "code",
   "source": [
    "if sum_cond!=1:\n",
    "  print(\"Please Choose only one option\")\n",
    "\n",
    "elif api_choosed:\n",
    "  stream_raw_df=read_stream_data(API,schema)\n",
    "  indput_df=fix_drop_cols_from_df(stream_raw_df)\n",
    "  play_df=main_app(indput_df,None)   \n",
    "  play_df=play_df[\"OriginStop\",\"Output\"]\n",
    "#   play_df=play_df.sort('Output')\n",
    "  display(play_df)\n",
    "  not_finished = False\n",
    "\n",
    "elif path_choosed:\n",
    "  raw_df=read_static_data(Path,schema)\n",
    "  input_df=fix_drop_cols_from_df(raw_df)\n",
    "  play_df=main_app(input_df,None)   \n",
    "  not_finished = False\n",
    "  display(play_df[\"OriginStop\",\"Output\"])\n",
    "\n",
    "elif source_choosed:\n",
    "#   print(main_app(None,Source)) \n",
    "  play_df=pd.DataFrame({'OriginStop': [Source],'Output': [main_app(None,Source)]})\n",
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