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User friendly dataset, dataframe generation for csv datasources without explicit StructType definitions. #24724

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User friendly dataset, dataframe generation for csv datasources without explicit StructType definitions. #24724

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43b7ab9
Merge pull request #1 from apache/master
swapnilushinde May 25, 2019
bdb6a1c
User friendly dataset, Datafame generation from csv datasource withou…
May 28, 2019
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53 changes: 53 additions & 0 deletions sql/core/src/main/scala/org/apache/spark/sql/SparkSession.scala
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Original file line number Diff line number Diff line change
Expand Up @@ -312,6 +312,23 @@ class SparkSession private(
Dataset.ofRows(self, ExternalRDD(rdd, self)(encoder))
}

/**
* Creates a `DataFrame` from a csv files with schema of Product (e.g. case classes).
* All options related to CSV data source can be used.
* @return
*/
def createDataFrame[A <: Product : TypeTag](options: Map[String, String], csvFilePath: String*)
: DataFrame = {
val structSchema = Encoders.product[A].schema
val inferredSchemaDisabled = options.map(x => (x._1,
if (x._1 == "inferSchema") "false" else x._2))

SparkSession.this.read
.options(inferredSchemaDisabled)
.schema(structSchema)
.csv(csvFilePath: _*)
}

/**
* :: Experimental ::
* Creates a `DataFrame` from a local Seq of Product.
Expand Down Expand Up @@ -527,6 +544,42 @@ class SparkSession private(
createDataset(data.asScala)
}

/**
* Creates a `[[Dataset]]` from a csv file with schema of Product (e.g. case classes).
* All options related to CSV data source can be used.
*
* == Example ==
*
* {{{
*
* import spark.implicits._
* case class Person(name: String, age: Long)
* val ds = spark.createDataset[Person](Map("header" -> "false"), csvFilePath)
*
* ds.show()
* // +-------+---+
* // | name|age|
* // +-------+---+
* // |Michael| 29|
* // | Andy| 30|
* // | Justin| 19|
* // +-------+---+
* }}}
* @return
*/
def createDataset[A <: Product : TypeTag](options: Map[String, String], csvFilePath: String*)
: Dataset[A] = {
import implicits._
val structSchema = Encoders.product[A].schema
val inferredSchemaDisabled = options.map(x => (x._1,
if (x._1 == "inferSchema") "false" else x._2))

SparkSession.this.read
.options(inferredSchemaDisabled)
.schema(structSchema)
.csv(csvFilePath: _*).as[A]
}

/**
* :: Experimental ::
* Creates a [[Dataset]] with a single `LongType` column named `id`, containing elements
Expand Down