notebooks/examples/python/Ship2ShipTransfers/02. Data Ingestion.ipynb

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    "## Setup\n",
    "\n",
    "---\n",
    "__Last Updated:__ 27 NOV 2023 [Mosaic 0.3.12]"
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      "Python interpreter will be restarted.\nPython interpreter will be restarted.\n"
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    "%pip install \"databricks-mosaic<0.4,>=0.3\" --quiet # <- Mosaic 0.3 series\n",
    "# %pip install \"databricks-mosaic<0.5,>=0.4\" --quiet # <- Mosaic 0.4 series (as available)"
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    "# -- configure AQE for more compute heavy operations\n",
    "#  - choose option-1 or option-2 below, essential for REPARTITION!\n",
    "# spark.conf.set(\"spark.databricks.optimizer.adaptive.enabled\", False) # <- option-1: turn off completely for full control\n",
    "spark.conf.set(\"spark.sql.adaptive.coalescePartitions.enabled\", False) # <- option-2: just tweak partition management\n",
    "spark.conf.set(\"spark.sql.shuffle.partitions\", 1_024)                  # <-- default is 200\n",
    "\n",
    "# -- import databricks + spark functions\n",
    "from pyspark.databricks.sql import functions as dbf\n",
    "from pyspark.sql import functions as F\n",
    "from pyspark.sql.functions import col, udf\n",
    "from pyspark.sql.types import *\n",
    "\n",
    "# -- setup mosaic\n",
    "import mosaic as mos\n",
    "\n",
    "mos.enable_mosaic(spark, dbutils)\n",
    "# mos.enable_gdal(spark) # <- not needed for this example\n",
    "\n",
    "# --other imports\n",
    "import warnings\n",
    "\n",
    "warnings.simplefilter(\"ignore\")"
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   "source": [
    "__Configure Database__\n",
    "\n",
    "> Adjust this to settings from the Data Prep notebook."
   ]
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     "text": [
      "Out[2]: DataFrame[]"
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   ],
   "source": [
    "catalog_name = \"mjohns\"\n",
    "db_name = \"ship2ship\"\n",
    "\n",
    "sql(f\"use catalog {catalog_name}\")\n",
    "sql(f\"use schema {db_name}\")"
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   "source": [
    "\n",
    "We begin with loading from a table. Here we use captured `AIS` data.\n",
    "\n",
    "<p/>\n",
    "\n",
    "- `MMSI`: unique 9-digit identification code of the ship - numeric\n",
    "- `VesselName`: name of the ship - string\n",
    "- `CallSign`: unique callsign of the ship - string\n",
    "- `BaseDateTime`: timestamp of the AIS message - datetime\n",
    "- `LAT`: latitude of the ship (in degree: [-90 ; 90], negative value represents South, 91 indicates ‘not available’) - numeric\n",
    "- `LON`: longitude of the ship (in degree: [-180 ; 180], negative value represents West, 181 indicates ‘not available’) - numeric\n",
    "- `SOG`: speed over ground, in knots - numeric\n",
    "- `Status`: status of the ship - string"
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   "source": [
    "cargos = spark.read.table(\"AIS\")\n",
    "display(cargos.limit(5)) # <- limiting for ipynb only"
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    "## AIS Data Indexing\n",
    "\n",
    "> To facilitate downstream analytics it is also possible to create a quick point index leveraging a chosen H3 resolution.\n",
    "In this case, resolution `9` has an edge length of ~174 metres."
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         "type": "\"double\""
        },
        {
         "metadata": "{}",
         "name": "COG",
         "type": "\"double\""
        },
        {
         "metadata": "{}",
         "name": "Heading",
         "type": "\"double\""
        },
        {
         "metadata": "{}",
         "name": "VesselName",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "IMO",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "CallSign",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "VesselType",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Status",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Length",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Width",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "Draft",
         "type": "\"double\""
        },
        {
         "metadata": "{}",
         "name": "Cargo",
         "type": "\"integer\""
        },
        {
         "metadata": "{}",
         "name": "TranscieverClass",
         "type": "\"string\""
        },
        {
         "metadata": "{}",
         "name": "point_geom",
         "type": "{\"type\":\"struct\",\"fields\":[{\"name\":\"type_id\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"srid\",\"type\":\"integer\",\"nullable\":true,\"metadata\":{}},{\"name\":\"boundary\",\"type\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":\"double\",\"containsNull\":true},\"containsNull\":true},\"containsNull\":true},\"nullable\":true,\"metadata\":{}},{\"name\":\"holes\",\"type\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":{\"type\":\"array\",\"elementType\":\"double\",\"containsNull\":true},\"containsNull\":true},\"containsNull\":true},\"containsNull\":true},\"nullable\":true,\"metadata\":{}}]}"
        },
        {
         "metadata": "{}",
         "name": "ix",
         "type": "\"long\""
        },
        {
         "metadata": "{}",
         "name": "sog_kmph",
         "type": "\"double\""
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cargos_indexed = (\n",
    "    cargos.withColumn(\"point_geom\", mos.st_point(\"LON\", \"LAT\"))\n",
    "    .withColumn(\"ix\", mos.grid_pointascellid(\"point_geom\", resolution=F.lit(9)))\n",
    "    .withColumn(\"sog_kmph\", F.round(col(\"sog\") * 1.852, 2))\n",
    ")\n",
    "display(cargos_indexed.limit(5)) # <- limiting for ipynb only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "d2bc9c28-4db9-405b-b8a6-a6916204ccc0",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "_We will write the treated output to a new table._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "a05f901e-e3d6-4855-854e-fa6ea6492f9a",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "(\n",
    "    cargos_indexed.withColumn(\"point_geom\", mos.st_aswkb(\"point_geom\"))\n",
    "    .write.mode(\"overwrite\")\n",
    "    .saveAsTable(\"cargos_indexed\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "006d4a9d-473b-4477-89b9-c4906209f8f3",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "_We will optimise our table to colocate data and make querying faster._\n",
    "\n",
    "> This is showing [ZORDER](https://docs.databricks.com/en/delta/data-skipping.html); for newer runtimes (DBR 13.3 LTS+), can also consider [Liquid Clustering](https://docs.databricks.com/en/delta/clustering.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "implicitDf": true,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "136231e0-d292-482a-947d-6765f18e9459",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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       "    max-height: 300px;\n",
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       "    text-align: left;\n",
       "  }\n",
       "</style><div class='table-result-container'><table class='table-result'><thead style='background-color: white'><tr><th>path</th><th>metrics</th></tr></thead><tbody><tr><td>s3://databricks-e2demofieldengwest/b169b504-4c54-49f2-bc3a-adf4b128f36d/tables/935ef93c-fdc0-4db3-b1ba-bf9f32bc26fa</td><td>List(4, 4, List(3318525, 5963269, 4417031.0, 4, 17668124), List(1602001, 7746854, 4757181.75, 4, 19028727), 0, List(minCubeSize(107374182400), List(0, 0), List(4, 19028727), 0, List(4, 19028727), 1, null), 1, 4, 0, false, 0, 0, 1701117514920, 1701117559879, 4, 1, null, List(0, 0), 20, 20, 8027)</td></tr></tbody></table></div>"
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        {
         "metadata": "{}",
         "name": "path",
         "type": "\"string\""
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        {
         "metadata": "{}",
         "name": "metrics",
         "type": "{\"type\":\"struct\",\"fields\":[{\"name\":\"numFilesAdded\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"numFilesRemoved\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"filesAdded\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"min\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"max\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"avg\",\"type\":\"double\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalFiles\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalSize\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"filesRemoved\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"min\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"max\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"avg\",\"type\":\"double\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalFiles\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalSize\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"partitionsOptimized\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"zOrderStats\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"strategyName\",\"type\":\"string\",\"nullable\":true,\"metadata\":{}},{\"name\":\"inputCubeFiles\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"num\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"size\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"inputOtherFiles\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"num\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"size\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"inputNumCubes\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"mergedFiles\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"num\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"size\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"numOutputCubes\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"mergedNumCubes\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"numBatches\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalConsideredFiles\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalFilesSkipped\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"preserveInsertionOrder\",\"type\":\"boolean\",\"nullable\":false,\"metadata\":{}},{\"name\":\"numFilesSkippedToReduceWriteAmplification\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"numBytesSkippedToReduceWriteAmplification\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"startTimeMs\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"endTimeMs\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalClusterParallelism\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalScheduledTasks\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"autoCompactParallelismStats\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"maxClusterActiveParallelism\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"minClusterActiveParallelism\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"maxSessionActiveParallelism\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}},{\"name\":\"minSessionActiveParallelism\",\"type\":\"long\",\"nullable\":true,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"deletionVectorStats\",\"type\":{\"type\":\"struct\",\"fields\":[{\"name\":\"numDeletionVectorsRemoved\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"numDeletionVectorRowsRemoved\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]},\"nullable\":true,\"metadata\":{}},{\"name\":\"numTableColumns\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"numTableColumnsWithStats\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}},{\"name\":\"totalTaskExecutionTimeMs\",\"type\":\"long\",\"nullable\":false,\"metadata\":{}}]}"
        }
       ],
       "type": "table"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%sql OPTIMIZE ship2ship.cargos_indexed ZORDER by (ix, BaseDateTime)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "b6702ef5-b1cc-4611-8b2c-b8cc3d302f57",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "## Visualisation\n",
    "And we can perform a quick visual inspection of the indexed AIS data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
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      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "30322c23-052a-4ac2-b140-fa1e117e31ea",
     "showTitle": false,
     "title": ""
    },
    "jupyter": {
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "output_type": "display_data",
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     "metadata": {
      "application/vnd.databricks.v1+output": {
       "addedWidgets": {},
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width='100%'/>\"\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {},
     "inputWidgets": {},
     "nuid": "b59dd42d-7a9e-4005-b0c2-57350fff2ee4",
     "showTitle": false,
     "title": ""
    }
   },
   "source": [
    "_Uncomment the following within databricks for actual results._"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "application/vnd.databricks.v1+cell": {
     "cellMetadata": {
      "byteLimit": 2048000,
      "rowLimit": 10000
     },
     "inputWidgets": {},
     "nuid": "4e642c82-4421-45bb-8ca9-9ac27a66d7f5",
     "showTitle": false,
     "title": ""
    }
   },
   "outputs": [],
   "source": [
    "# %%mosaic_kepler\n",
    "# ship2ship.cargos_indexed \"ix\" \"h3\" 10_000"
   ]
  }
 ],
 "metadata": {
  "application/vnd.databricks.v1+notebook": {
   "dashboards": [],
   "language": "python",
   "notebookMetadata": {
    "mostRecentlyExecutedCommandWithImplicitDF": {
     "commandId": 85549842153077,
     "dataframes": [
      "_sqldf"
     ]
    },
    "pythonIndentUnit": 2
   },
   "notebookName": "02. Data Ingestion",
   "widgets": {}
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}