how to split geojson data in columns with spark sql scala
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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
scala apache-spark-sql
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
126
add a comment |
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
scala apache-spark-sql
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
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
add a comment |
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
scala apache-spark-sql
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
126
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
scala apache-spark-sql
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
126
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
126
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
edited Nov 25 '18 at 19:51
Jacek Laskowski
46.5k18138278
46.5k18138278
asked Nov 22 '18 at 15:31
MakMak
126
asked Nov 22 '18 at 15:31
MakMak
126
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
add a comment |
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
add a comment |
1 Answer
1
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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($"features"))
.select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
"exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")
resultDf.show()
resultDf.printSchema()
Input data (formatted):
{
"features": [
{
"properties": {
"auswertezeit": "x",
"geschwindigkeit": 1,
"strecke_id": 11,
"verkehrsstatus": "xx"
}
},
{
"properties": {
"auswertezeit": "y",
"geschwindigkeit": 2,
"strecke_id": 22,
"verkehrsstatus": "yy"
}
}
],
"type": "xyz"
}
Result:
+------------+---------------+----------+--------------+
|auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
+------------+---------------+----------+--------------+
| x| 1| 11| xx|
| y| 2| 22| yy|
+------------+---------------+----------+--------------+
root
|-- auswertezeit: string (nullable = true)
|-- geschwindigkeit: long (nullable = true)
|-- strecke_id: long (nullable = true)
|-- verkehrsstatus: string (nullable = true)
answered Nov 22 '18 at 20:20
morsikmorsik
709815
add a comment |
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1 Answer
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1 Answer
1
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oldest
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oldest
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0
Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:
import sparkSession.implicits._
val geoDF = sparkSession.read.json("./src/test/resources/geo.json")
val resultDf = geoDF.withColumn("exploaded", functions.explode($"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)
answered Nov 22 '18 at 20:20
morsikmorsik
709815
add a comment |
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($"features"))
.select("exploaded.properties.auswertezeit", "exploaded.properties.geschwindigkeit",
"exploaded.properties.strecke_id", "exploaded.properties.verkehrsstatus")
resultDf.show()
resultDf.printSchema()
Input data (formatted):
{
"features": [
{
"properties": {
"auswertezeit": "x",
"geschwindigkeit": 1,
"strecke_id": 11,
"verkehrsstatus": "xx"
}
},
{
"properties": {
"auswertezeit": "y",
"geschwindigkeit": 2,
"strecke_id": 22,
"verkehrsstatus": "yy"
}
}
],
"type": "xyz"
}
Result:
+------------+---------------+----------+--------------+
|auswertezeit|geschwindigkeit|strecke_id|verkehrsstatus|
+------------+---------------+----------+--------------+
| x| 1| 11| xx|
| y| 2| 22| yy|
+------------+---------------+----------+--------------+
root
|-- auswertezeit: string (nullable = true)
|-- geschwindigkeit: long (nullable = true)
|-- strecke_id: long (nullable = true)
|-- verkehrsstatus: string (nullable = true)
answered Nov 22 '18 at 20:20
morsikmorsik
709815
add a comment |
0
0
0
Here's a bit simplified example, but this will give you the direction to adjust logic based on your own specs:
import sparkSession.implicits._
val geoDF = sparkSession.read.json("./src/test/resources/geo.json")
val resultDf = geoDF.withColumn("exploaded", functions.explode($"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)
answered Nov 22 '18 at 20:20
morsikmorsik
709815
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)
answered Nov 22 '18 at 20:20
morsikmorsik
709815
answered Nov 22 '18 at 20:20
morsikmorsik
709815
answered Nov 22 '18 at 20:20
morsikmorsik
709815
answered Nov 22 '18 at 20:20
morsikmorsik
709815
709815
add a comment |
add a comment |
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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