Error: Overloaded Method Value createDataFrame
I Tried to Create Apache Spark Dataframe Val valuesCol = Seq(("Male","2019-09-06"), ("Female","2019-09-06"), ("Male","2019-09-07")) valuesCol: Seq[(String...
I tried to create Apache Spark dataframe
val valuesCol = Seq(("Male","2019-09-06"),("Female","2019-09-06"),("Male","2019-09-07"))
valuesCol: Seq[(String, String)] = List((Male,2019-09-06), (Female,2019-09-06), (Male,2019-09-07))
Schema
val someSchema = List(StructField("sex", StringType, true),StructField("date", DateType, true))
someSchema: List[org.apache.spark.sql.types.StructField] = List(StructField(sex,StringType,true), StructField(date,DateType,true))
It does not work
val someDF = spark.createDataFrame(spark.sparkContext.parallelize(valuesCol),StructType(someSchema))
I got error
<console>:30: error: overloaded method value createDataFrame with alternatives:
(data: java.util.List[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rdd: org.apache.spark.api.java.JavaRDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rdd: org.apache.spark.rdd.RDD[_],beanClass: Class[_])org.apache.spark.sql.DataFrame <and>
(rows: java.util.List[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
(rowRDD: org.apache.spark.api.java.JavaRDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame <and>
(rowRDD: org.apache.spark.rdd.RDD[org.apache.spark.sql.Row],schema: org.apache.spark.sql.types.StructType)org.apache.spark.sql.DataFrame
cannot be applied to (org.apache.spark.rdd.RDD[(String, String)], org.apache.spark.sql.types.StructType)
val someDF = spark.createDataFrame(spark.sparkContext.parallelize(valuesCol),StructType(someSchema))
Should I change date formatting in valuesCol? What actually causes this error?
2 Answers
With import spark.implicits._ you could convert Seq into Dataframe in place
val df: DataFrame = Seq(("Male","2019-09-06"),("Female","2019-09-06"),("Male","2019-09-07"))
.toDF() // <--- Here
Explicitly setting column names:
val df: DataFrame = Seq(("Male","2019-09-06"),("Female","2019-09-06"),("Male","2019-09-07"))
.toDF("sex", "date")
For the desired schema, you could either cast column or use a different type
//Cast
Seq(("Male","2019-09-06"),("Female","2019-09-06"),("Male","2019-09-07"))
.toDF("sex", "date")
.select($"sex", $"date".cast(DateType))
.printSchema()
//Types
val format = new java.text.SimpleDateFormat("yyyy-MM-dd")
Seq(
("Male", new java.sql.Date(format.parse("2019-09-06").getTime)),
("Female", new java.sql.Date(format.parse("2019-09-06").getTime)),
("Male", new java.sql.Date(format.parse("2019-09-07").getTime)))
.toDF("sex", "date")
.printSchema()
//Output
root
|-- sex: string (nullable = true)
|-- date: date (nullable = true)
Regarding your question, your rdd type is known, Spark will create schema accordingly to it.
val rdd: RDD[(String, String)] = spark.sparkContext.parallelize(valuesCol)
spark.createDataFrame(rdd)
root
|-- _1: string (nullable = true)
|-- _2: string (nullable = true)
You can specify your valuesCol as Seq of Row instead of Seq of Tuple :
val valuesCol = Seq(
Row("Male", "2019-09-06"),
Row ("Female", "2019-09-06"),
Row("Male", "2019-09-07"))
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