Github user maropu commented on a diff in the pull request:
https://github.com/apache/spark/pull/17719#discussion_r112805934
--- Diff:
sql/core/src/main/scala/org/apache/spark/sql/DataFrameReader.scala ---
@@ -68,6 +68,18 @@ class DataFrameReader private[sql](sparkSession:
SparkSession) extends Logging {
}
/**
+ * Specifies the schema by using the input DDL-formatted string. Some
data sources (e.g. JSON) can
+ * infer the input schema automatically from data. By specifying the
schema here, the underlying
+ * data source can skip the schema inference step, and thus speed up
data loading.
+ *
+ * @since 2.3.0
+ */
+ def schema(schemaString: String): DataFrameReader = {
+ this.userSpecifiedSchema = Option(StructType.fromDDL(schemaString))
--- End diff --
Sorry, but I probably missed your point. What's the API consistency you
pointed out here?
I just made the python APIs the same with the Scala ones like:
```
--- python
>>> from pyspark.sql.types import *
>>> fields = [StructField('a', IntegerType(), True), StructField('b',
StringType(), True), StructField('c', DoubleType(), True)]
>>> schema = StructType(fields)
>>> spark.read.schema(schema).csv("/Users/maropu/Desktop/test.csv").show()
+---+----+---+
| a| b| c|
+---+----+---+
| 1| aaa|0.3|
+---+----+---+
>>> spark.read.schema("a INT, b STRING, c
DOUBLE").csv("/Users/maropu/Desktop/test.csv").show()
+---+----+---+
| a| b| c|
+---+----+---+
| 1| aaa|0.3|
+---+----+---+
--- scala
scala> import org.apache.spark.sql.types._
scala> fields = StructField("a", IntegerType) :: StructField("b",
StringType) :: StructField("c", DoubleType) :: Nil
scala> val schema = StructType(fields)
scala> spark.read.schema(schema).csv("/Users/maropu/Desktop/test.csv").show
+---+----+---+
| a| b| c|
+---+----+---+
| 1| aaa|0.3|
+---+----+---+
scala> spark.read.schema("a INT, b STRING, c
DOUBLE").csv("/Users/maropu/Desktop/test.csv").show
+---+----+---+
| a| b| c|
+---+----+---+
| 1| aaa|0.3|
+---+----+---+
```
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