how to split geojson data in columns with spark sql scala












1















I have GeoJSON data as structtype like this:



root
|-- features: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- geometry: struct (nullable = true)
| | | |-- coordinates: array (nullable = true)
| | | | |-- element: array (containsNull = true)
| | | | | |-- element: array (containsNull = true)
| | | | | | |-- element: double (containsNull = true)
| | | |-- type: string (nullable = true)
| | |-- properties: struct (nullable = true)
| | | |-- auswertezeit: string (nullable = true)
| | | |-- geschwindigkeit: long (nullable = true)
| | | |-- strecke_id: long (nullable = true)
| | | |-- verkehrsstatus: string (nullable = true)
| | |-- type: string (nullable = true)
|-- type: string (nullable = true)


and i will split data in Columns: strecke_id, auswertezeit, strecke_id, verkehrsstatus, geschwindigkeit and coordinates.
Thank you for your help










share|improve this question

























  • What version of Spark do you use? In 2.4 you have higher-order functions for this.

    – Jacek Laskowski
    Nov 25 '18 at 19:51
















1















I have GeoJSON data as structtype like this:



root
|-- features: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- geometry: struct (nullable = true)
| | | |-- coordinates: array (nullable = true)
| | | | |-- element: array (containsNull = true)
| | | | | |-- element: array (containsNull = true)
| | | | | | |-- element: double (containsNull = true)
| | | |-- type: string (nullable = true)
| | |-- properties: struct (nullable = true)
| | | |-- auswertezeit: string (nullable = true)
| | | |-- geschwindigkeit: long (nullable = true)
| | | |-- strecke_id: long (nullable = true)
| | | |-- verkehrsstatus: string (nullable = true)
| | |-- type: string (nullable = true)
|-- type: string (nullable = true)


and i will split data in Columns: strecke_id, auswertezeit, strecke_id, verkehrsstatus, geschwindigkeit and coordinates.
Thank you for your help










share|improve this question

























  • What version of Spark do you use? In 2.4 you have higher-order functions for this.

    – Jacek Laskowski
    Nov 25 '18 at 19:51














1












1








1


0






I have GeoJSON data as structtype like this:



root
|-- features: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- geometry: struct (nullable = true)
| | | |-- coordinates: array (nullable = true)
| | | | |-- element: array (containsNull = true)
| | | | | |-- element: array (containsNull = true)
| | | | | | |-- element: double (containsNull = true)
| | | |-- type: string (nullable = true)
| | |-- properties: struct (nullable = true)
| | | |-- auswertezeit: string (nullable = true)
| | | |-- geschwindigkeit: long (nullable = true)
| | | |-- strecke_id: long (nullable = true)
| | | |-- verkehrsstatus: string (nullable = true)
| | |-- type: string (nullable = true)
|-- type: string (nullable = true)


and i will split data in Columns: strecke_id, auswertezeit, strecke_id, verkehrsstatus, geschwindigkeit and coordinates.
Thank you for your help










share|improve this question
















I have GeoJSON data as structtype like this:



root
|-- features: array (nullable = true)
| |-- element: struct (containsNull = true)
| | |-- geometry: struct (nullable = true)
| | | |-- coordinates: array (nullable = true)
| | | | |-- element: array (containsNull = true)
| | | | | |-- element: array (containsNull = true)
| | | | | | |-- element: double (containsNull = true)
| | | |-- type: string (nullable = true)
| | |-- properties: struct (nullable = true)
| | | |-- auswertezeit: string (nullable = true)
| | | |-- geschwindigkeit: long (nullable = true)
| | | |-- strecke_id: long (nullable = true)
| | | |-- verkehrsstatus: string (nullable = true)
| | |-- type: string (nullable = true)
|-- type: string (nullable = true)


and i will split data in Columns: strecke_id, auswertezeit, strecke_id, verkehrsstatus, geschwindigkeit and coordinates.
Thank you for your help







scala apache-spark-sql






share|improve this question















share|improve this question













share|improve this question




share|improve this question








edited Nov 25 '18 at 19:51









Jacek Laskowski

45.3k18134273




45.3k18134273










asked Nov 22 '18 at 15:31









MakMak

126




126













  • What version of Spark do you use? In 2.4 you have higher-order functions for this.

    – Jacek Laskowski
    Nov 25 '18 at 19:51



















  • What version of Spark do you use? In 2.4 you have higher-order functions for this.

    – Jacek Laskowski
    Nov 25 '18 at 19:51

















What version of Spark do you use? In 2.4 you have higher-order functions for this.

– Jacek Laskowski
Nov 25 '18 at 19:51





What version of Spark do you use? In 2.4 you have higher-order functions for this.

– Jacek Laskowski
Nov 25 '18 at 19:51












1 Answer
1






active

oldest

votes


















0














Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:



import sparkSession.implicits._

val geoDF = sparkSession.read.json("./src/test/resources/geo.json")

val resultDf = geoDF.withColumn("exploaded", functions.explode($"features"))
.select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
"exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")

resultDf.show()
resultDf.printSchema()


Input data (formatted):



{
"features": [
{
"properties": {
"auswertezeit": "x",
"geschwindigkeit": 1,
"strecke_id": 11,
"verkehrsstatus": "xx"
}
},
{
"properties": {
"auswertezeit": "y",
"geschwindigkeit": 2,
"strecke_id": 22,
"verkehrsstatus": "yy"
}
}
],
"type": "xyz"
}


Result:



+------------+---------------+----------+--------------+
|auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
+------------+---------------+----------+--------------+
| x| 1| 11| xx|
| y| 2| 22| yy|
+------------+---------------+----------+--------------+

root
|-- auswertezeit: string (nullable = true)
|-- geschwindigkeit: long (nullable = true)
|-- strecke_id: long (nullable = true)
|-- verkehrsstatus: string (nullable = true)





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    oldest

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    active

    oldest

    votes









    0














    Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:



    import sparkSession.implicits._

    val geoDF = sparkSession.read.json("./src/test/resources/geo.json")

    val resultDf = geoDF.withColumn("exploaded", functions.explode($"features"))
    .select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
    "exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")

    resultDf.show()
    resultDf.printSchema()


    Input data (formatted):



    {
    "features": [
    {
    "properties": {
    "auswertezeit": "x",
    "geschwindigkeit": 1,
    "strecke_id": 11,
    "verkehrsstatus": "xx"
    }
    },
    {
    "properties": {
    "auswertezeit": "y",
    "geschwindigkeit": 2,
    "strecke_id": 22,
    "verkehrsstatus": "yy"
    }
    }
    ],
    "type": "xyz"
    }


    Result:



    +------------+---------------+----------+--------------+
    |auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
    +------------+---------------+----------+--------------+
    | x| 1| 11| xx|
    | y| 2| 22| yy|
    +------------+---------------+----------+--------------+

    root
    |-- auswertezeit: string (nullable = true)
    |-- geschwindigkeit: long (nullable = true)
    |-- strecke_id: long (nullable = true)
    |-- verkehrsstatus: string (nullable = true)





    share|improve this answer




























      0














      Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:



      import sparkSession.implicits._

      val geoDF = sparkSession.read.json("./src/test/resources/geo.json")

      val resultDf = geoDF.withColumn("exploaded", functions.explode($"features"))
      .select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
      "exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")

      resultDf.show()
      resultDf.printSchema()


      Input data (formatted):



      {
      "features": [
      {
      "properties": {
      "auswertezeit": "x",
      "geschwindigkeit": 1,
      "strecke_id": 11,
      "verkehrsstatus": "xx"
      }
      },
      {
      "properties": {
      "auswertezeit": "y",
      "geschwindigkeit": 2,
      "strecke_id": 22,
      "verkehrsstatus": "yy"
      }
      }
      ],
      "type": "xyz"
      }


      Result:



      +------------+---------------+----------+--------------+
      |auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
      +------------+---------------+----------+--------------+
      | x| 1| 11| xx|
      | y| 2| 22| yy|
      +------------+---------------+----------+--------------+

      root
      |-- auswertezeit: string (nullable = true)
      |-- geschwindigkeit: long (nullable = true)
      |-- strecke_id: long (nullable = true)
      |-- verkehrsstatus: string (nullable = true)





      share|improve this answer


























        0












        0








        0







        Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:



        import sparkSession.implicits._

        val geoDF = sparkSession.read.json("./src/test/resources/geo.json")

        val resultDf = geoDF.withColumn("exploaded", functions.explode($"features"))
        .select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
        "exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")

        resultDf.show()
        resultDf.printSchema()


        Input data (formatted):



        {
        "features": [
        {
        "properties": {
        "auswertezeit": "x",
        "geschwindigkeit": 1,
        "strecke_id": 11,
        "verkehrsstatus": "xx"
        }
        },
        {
        "properties": {
        "auswertezeit": "y",
        "geschwindigkeit": 2,
        "strecke_id": 22,
        "verkehrsstatus": "yy"
        }
        }
        ],
        "type": "xyz"
        }


        Result:



        +------------+---------------+----------+--------------+
        |auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
        +------------+---------------+----------+--------------+
        | x| 1| 11| xx|
        | y| 2| 22| yy|
        +------------+---------------+----------+--------------+

        root
        |-- auswertezeit: string (nullable = true)
        |-- geschwindigkeit: long (nullable = true)
        |-- strecke_id: long (nullable = true)
        |-- verkehrsstatus: string (nullable = true)





        share|improve this answer













        Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:



        import sparkSession.implicits._

        val geoDF = sparkSession.read.json("./src/test/resources/geo.json")

        val resultDf = geoDF.withColumn("exploaded", functions.explode($"features"))
        .select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
        "exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")

        resultDf.show()
        resultDf.printSchema()


        Input data (formatted):



        {
        "features": [
        {
        "properties": {
        "auswertezeit": "x",
        "geschwindigkeit": 1,
        "strecke_id": 11,
        "verkehrsstatus": "xx"
        }
        },
        {
        "properties": {
        "auswertezeit": "y",
        "geschwindigkeit": 2,
        "strecke_id": 22,
        "verkehrsstatus": "yy"
        }
        }
        ],
        "type": "xyz"
        }


        Result:



        +------------+---------------+----------+--------------+
        |auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
        +------------+---------------+----------+--------------+
        | x| 1| 11| xx|
        | y| 2| 22| yy|
        +------------+---------------+----------+--------------+

        root
        |-- auswertezeit: string (nullable = true)
        |-- geschwindigkeit: long (nullable = true)
        |-- strecke_id: long (nullable = true)
        |-- verkehrsstatus: string (nullable = true)






        share|improve this answer












        share|improve this answer



        share|improve this answer










        answered Nov 22 '18 at 20:20









        morsikmorsik

        699815




        699815
































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