Map[String,java.lang.Object] to DataFrame schema issue
3
I have to determine the schema from the values (not the keys) of a Map[String, Object].
Sample map:
val myMap = Map("k1" -> 1, "k2" -> "", "k3"-> new Timestamp(new Date().getTime), "k4" -> 2.0 )
Currently I have created a schema from the keys like below:
// I have created a schema using keys
val schema = StructType(myMap.keys.toSeq.map {
StructField(_, StringType) // StringType is wrong since Object in the Map can be of any datatype
}
// I have created a RDD like below
val rdd = sc.parallelize(Seq(Row.fromSeq(myMap.values.toSeq)))
val df = sc.createDataFrame(rdd,schema)
But now my problem is that the object can be a double or date or timestamp or anything. But I have created a schema using StringType as described above which is wrong.
Any ideas of creating a schema from Map values that are objects?
scala apache-spark dataframe apache-spark-sql schema
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
add a comment |
3
I have to determine the schema from the values (not the keys) of a Map[String, Object].
Sample map:
val myMap = Map("k1" -> 1, "k2" -> "", "k3"-> new Timestamp(new Date().getTime), "k4" -> 2.0 )
Currently I have created a schema from the keys like below:
// I have created a schema using keys
val schema = StructType(myMap.keys.toSeq.map {
StructField(_, StringType) // StringType is wrong since Object in the Map can be of any datatype
}
// I have created a RDD like below
val rdd = sc.parallelize(Seq(Row.fromSeq(myMap.values.toSeq)))
val df = sc.createDataFrame(rdd,schema)
But now my problem is that the object can be a double or date or timestamp or anything. But I have created a schema using StringType as described above which is wrong.
Any ideas of creating a schema from Map values that are objects?
scala apache-spark dataframe apache-spark-sql schema
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07
add a comment |
3
3
3
1
I have to determine the schema from the values (not the keys) of a Map[String, Object].
Sample map:
val myMap = Map("k1" -> 1, "k2" -> "", "k3"-> new Timestamp(new Date().getTime), "k4" -> 2.0 )
Currently I have created a schema from the keys like below:
// I have created a schema using keys
val schema = StructType(myMap.keys.toSeq.map {
StructField(_, StringType) // StringType is wrong since Object in the Map can be of any datatype
}
// I have created a RDD like below
val rdd = sc.parallelize(Seq(Row.fromSeq(myMap.values.toSeq)))
val df = sc.createDataFrame(rdd,schema)
But now my problem is that the object can be a double or date or timestamp or anything. But I have created a schema using StringType as described above which is wrong.
Any ideas of creating a schema from Map values that are objects?
scala apache-spark dataframe apache-spark-sql schema
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
I have to determine the schema from the values (not the keys) of a Map[String, Object].
Sample map:
val myMap = Map("k1" -> 1, "k2" -> "", "k3"-> new Timestamp(new Date().getTime), "k4" -> 2.0 )
Currently I have created a schema from the keys like below:
// I have created a schema using keys
val schema = StructType(myMap.keys.toSeq.map {
StructField(_, StringType) // StringType is wrong since Object in the Map can be of any datatype
}
// I have created a RDD like below
val rdd = sc.parallelize(Seq(Row.fromSeq(myMap.values.toSeq)))
val df = sc.createDataFrame(rdd,schema)
But now my problem is that the object can be a double or date or timestamp or anything. But I have created a schema using StringType as described above which is wrong.
Any ideas of creating a schema from Map values that are objects?
scala apache-spark dataframe apache-spark-sql schema
scala apache-spark dataframe apache-spark-sql schema
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
edited Nov 6 '18 at 1:42
Shaido
12.6k122742
12.6k122742
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
asked Nov 5 '18 at 23:00
user3190018user3190018
381415
381415
@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07
add a comment |
@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07
@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07
add a comment |
1 Answer
1
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References : It is an idea from dataTypeFor of ScalaReflection from spark code
You can create struct like this
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
/**
*createStruct based on datatype
* @param myObject Object
* @return [[DataType]]
*/
def createStruct(myObject: Object): DataType = {
myObject match {
case t if t.isInstanceOf[String] => StringType
case t if t.isInstanceOf[Long] => LongType
case t if t.isInstanceOf[Integer] => IntegerType
case t if t.isInstanceOf[Float] => FloatType
case t if t.isInstanceOf[Double] => DoubleType
case t if t.isInstanceOf[java.sql.Timestamp] => TimestampType
}
}
Below is the sample snippet which calls the function above..
val a: Seq[(Object, Object)] = myMap.keys.toList.zip(columnsMap.values.toList)
logger.info("" + a.toString)
val list = ListBuffer.empty[StructField]
a.foreach { x => {
list += StructField(x._1.toString, createStruct(x._2), false)
//println(createStruct(x._2) + "--" + x.toString())
}
// )
}
println("list is " + list)
val schema = StructType(list.toList)
println("-----" + schema.treeString)
val df = sparkSession.sqlContext.createDataFrame(rdd, schema)
df.printSchema()
df.show
edited Dec 27 '18 at 18:08
user3190018
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
add a comment |
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1 Answer
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1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
2
References : It is an idea from dataTypeFor of ScalaReflection from spark code
You can create struct like this
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
/**
*createStruct based on datatype
* @param myObject Object
* @return [[DataType]]
*/
def createStruct(myObject: Object): DataType = {
myObject match {
case t if t.isInstanceOf[String] => StringType
case t if t.isInstanceOf[Long] => LongType
case t if t.isInstanceOf[Integer] => IntegerType
case t if t.isInstanceOf[Float] => FloatType
case t if t.isInstanceOf[Double] => DoubleType
case t if t.isInstanceOf[java.sql.Timestamp] => TimestampType
}
}
Below is the sample snippet which calls the function above..
val a: Seq[(Object, Object)] = myMap.keys.toList.zip(columnsMap.values.toList)
logger.info("" + a.toString)
val list = ListBuffer.empty[StructField]
a.foreach { x => {
list += StructField(x._1.toString, createStruct(x._2), false)
//println(createStruct(x._2) + "--" + x.toString())
}
// )
}
println("list is " + list)
val schema = StructType(list.toList)
println("-----" + schema.treeString)
val df = sparkSession.sqlContext.createDataFrame(rdd, schema)
df.printSchema()
df.show
edited Dec 27 '18 at 18:08
user3190018
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
add a comment |
2
References : It is an idea from dataTypeFor of ScalaReflection from spark code
You can create struct like this
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
/**
*createStruct based on datatype
* @param myObject Object
* @return [[DataType]]
*/
def createStruct(myObject: Object): DataType = {
myObject match {
case t if t.isInstanceOf[String] => StringType
case t if t.isInstanceOf[Long] => LongType
case t if t.isInstanceOf[Integer] => IntegerType
case t if t.isInstanceOf[Float] => FloatType
case t if t.isInstanceOf[Double] => DoubleType
case t if t.isInstanceOf[java.sql.Timestamp] => TimestampType
}
}
Below is the sample snippet which calls the function above..
val a: Seq[(Object, Object)] = myMap.keys.toList.zip(columnsMap.values.toList)
logger.info("" + a.toString)
val list = ListBuffer.empty[StructField]
a.foreach { x => {
list += StructField(x._1.toString, createStruct(x._2), false)
//println(createStruct(x._2) + "--" + x.toString())
}
// )
}
println("list is " + list)
val schema = StructType(list.toList)
println("-----" + schema.treeString)
val df = sparkSession.sqlContext.createDataFrame(rdd, schema)
df.printSchema()
df.show
edited Dec 27 '18 at 18:08
user3190018
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
add a comment |
2
2
2
References : It is an idea from dataTypeFor of ScalaReflection from spark code
You can create struct like this
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
/**
*createStruct based on datatype
* @param myObject Object
* @return [[DataType]]
*/
def createStruct(myObject: Object): DataType = {
myObject match {
case t if t.isInstanceOf[String] => StringType
case t if t.isInstanceOf[Long] => LongType
case t if t.isInstanceOf[Integer] => IntegerType
case t if t.isInstanceOf[Float] => FloatType
case t if t.isInstanceOf[Double] => DoubleType
case t if t.isInstanceOf[java.sql.Timestamp] => TimestampType
}
}
Below is the sample snippet which calls the function above..
val a: Seq[(Object, Object)] = myMap.keys.toList.zip(columnsMap.values.toList)
logger.info("" + a.toString)
val list = ListBuffer.empty[StructField]
a.foreach { x => {
list += StructField(x._1.toString, createStruct(x._2), false)
//println(createStruct(x._2) + "--" + x.toString())
}
// )
}
println("list is " + list)
val schema = StructType(list.toList)
println("-----" + schema.treeString)
val df = sparkSession.sqlContext.createDataFrame(rdd, schema)
df.printSchema()
df.show
edited Dec 27 '18 at 18:08
user3190018
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
References : It is an idea from dataTypeFor of ScalaReflection from spark code
You can create struct like this
import org.apache.spark.sql.SparkSession
import org.apache.spark.sql.types.{StringType, StructField, StructType}
/**
*createStruct based on datatype
* @param myObject Object
* @return [[DataType]]
*/
def createStruct(myObject: Object): DataType = {
myObject match {
case t if t.isInstanceOf[String] => StringType
case t if t.isInstanceOf[Long] => LongType
case t if t.isInstanceOf[Integer] => IntegerType
case t if t.isInstanceOf[Float] => FloatType
case t if t.isInstanceOf[Double] => DoubleType
case t if t.isInstanceOf[java.sql.Timestamp] => TimestampType
}
}
Below is the sample snippet which calls the function above..
val a: Seq[(Object, Object)] = myMap.keys.toList.zip(columnsMap.values.toList)
logger.info("" + a.toString)
val list = ListBuffer.empty[StructField]
a.foreach { x => {
list += StructField(x._1.toString, createStruct(x._2), false)
//println(createStruct(x._2) + "--" + x.toString())
}
// )
}
println("list is " + list)
val schema = StructType(list.toList)
println("-----" + schema.treeString)
val df = sparkSession.sqlContext.createDataFrame(rdd, schema)
df.printSchema()
df.show
edited Dec 27 '18 at 18:08
user3190018
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
edited Dec 27 '18 at 18:08
user3190018
381415
edited Dec 27 '18 at 18:08
user3190018
381415
edited Dec 27 '18 at 18:08
user3190018
381415
381415
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
answered Nov 8 '18 at 18:19
Ram GhadiyaramRam Ghadiyaram
16.8k64477
16.8k64477
add a comment |
add a comment |
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@shaido : any ideas ?
– user3190018
Nov 6 '18 at 3:04
@ramesh-maharjan : infact I followed one of the post by you which is related to this question. which was working for normal types. but in this case any suggestions?
– user3190018
Nov 6 '18 at 6:07