Github user liancheng commented on a diff in the pull request:
https://github.com/apache/spark/pull/14119#discussion_r70263767
--- Diff:
examples/src/main/scala/org/apache/spark/examples/sql/SparkSqlExample.scala ---
@@ -0,0 +1,201 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package org.apache.spark.examples.sql
+
+// $example on:schema_inferring$
+import org.apache.spark.sql.catalyst.encoders.ExpressionEncoder
+import org.apache.spark.sql.Encoder
+// $example off:schema_inferring$
+import org.apache.spark.sql.Row
+// $example on:init_session$
+import org.apache.spark.sql.SparkSession
+// $example off:init_session$
+// $example on:programmatic_schema$
+import org.apache.spark.sql.types.StringType
+import org.apache.spark.sql.types.StructField
+import org.apache.spark.sql.types.StructType
+// $example off:programmatic_schema$
+
+object SparkSqlExample {
+
+ // $example on:create_ds$
+ // Note: Case classes in Scala 2.10 can support only up to 22 fields. To
work around this limit,
+ // you can use custom classes that implement the Product interface
+ case class Person(name: String, age: Long)
+ // $example off:create_ds$
+
+ def main(args: Array[String]) {
+ // $example on:init_session$
+ val spark = SparkSession
+ .builder()
+ .appName("Spark SQL Example")
+ .config("spark.some.config.option", "some-value")
+ .getOrCreate()
+
+ // For implicit conversions like converting RDDs to DataFrames
+ import spark.implicits._
+ // $example off:init_session$
+
+ runBasicDataFrameExample(spark)
+ runDatasetCreationExample(spark)
+ runInferSchemaExample(spark)
+ runProgrammaticSchemaExample(spark)
+
+ spark.stop()
+ }
+
+ private def runBasicDataFrameExample(spark: SparkSession): Unit = {
+ // $example on:create_df$
+ val df = spark.read.json("examples/src/main/resources/people.json")
+
+ // Displays the content of the DataFrame to stdout
+ df.show()
+ // age name
+ // null Michael
+ // 30 Andy
+ // 19 Justin
+ // $example off:create_df$
+
+ // $example on:untyped_ops$
+ // Print the schema in a tree format
+ df.printSchema()
+ // root
+ // |-- age: long (nullable = true)
+ // |-- name: string (nullable = true)
+
+ // Select only the "name" column
+ df.select("name").show()
+ // name
+ // Michael
+ // Andy
+ // Justin
+
+ // Select everybody, but increment the age by 1
+ df.select(df("name"), df("age") + 1).show()
+ // name (age + 1)
+ // Michael null
+ // Andy 31
+ // Justin 20
+
+ // Select people older than 21
+ df.filter(df("age") > 21).show()
+ // age name
+ // 30 Andy
+
+ // Count people by age
+ df.groupBy("age").count().show()
+ // age count
+ // null 1
+ // 19 1
+ // 30 1
+ // $example off:untyped_ops$
+
+ // $example on:run_sql$
+ // Register the DataFrame as a SQL temporary view
+ df.createOrReplaceTempView("people")
+
+ val sqlDF = spark.sql("SELECT * FROM people")
+ sqlDF.show()
+ // $example off:run_sql$
+ }
+
+ private def runDatasetCreationExample(spark: SparkSession): Unit = {
+ import spark.implicits._
+ // $example on:create_ds$
+ // Encoders are created for case classes
+ val caseClassDS = Seq(Person("Andy", 32)).toDS()
+ caseClassDS.show()
+
+ // Encoders for most common types are automatically provided by
importing spark.implicits._
+ val primitiveDS = Seq(1, 2, 3).toDS()
+ primitiveDS.map(_ + 1).collect() // Returns: Array(2, 3, 4)
+
+ // DataFrames can be converted to a Dataset by providing a class.
Mapping will be done by name
+ val path = "examples/src/main/resources/people.json"
+ val peopleDS = spark.read.json(path).as[Person]
+ peopleDS.show()
+ // $example off:create_ds$
+ }
+
+ private def runInferSchemaExample(spark: SparkSession): Unit = {
+ // $example on:schema_inferring$
+ // For implicit conversions from RDDs to DataFrames
+ import spark.implicits._
+
+ // Create an RDD of Person objects from a text file, convert it to a
Dataframe
+ val peopleDF = spark.sparkContext
+ .textFile("examples/src/main/resources/people.txt")
+ .map(_.split(","))
+ .map(attributes => Person(attributes(0), attributes(1).trim.toInt))
+ .toDF()
+ // Register the DataFrame as a temporary view
+ peopleDF.createOrReplaceTempView("people")
+
+ // SQL statements can be run by using the sql methods provided by Spark
+ val teenagersDF = spark.sql("SELECT name, age FROM people WHERE age
BETWEEN 13 AND 19")
+
+ // The columns of a row in the result can be accessed by field index
+ teenagersDF.map(teenager => "Name: " + teenager(0)).show()
+
+ // or by field name
+ teenagersDF.map(teenager => "Name: " +
teenager.getAs[String]("name")).show()
+
+ // No pre-defined encoders for Dataset[Map[K,V]], define explicitly
+ implicit val mapEncoder =
org.apache.spark.sql.Encoders.kryo[Map[String, Any]]
+ // Primitive types and case classes can be also defined as
+ implicit val stringIntMapEncoder: Encoder[Map[String, Int]] =
ExpressionEncoder()
+
+ // row.getValuesMap[T] retrieves multiple columns at once into a
Map[String, T]
+ teenagersDF.map(teenager => teenager.getValuesMap[Any](List("name",
"age"))).collect()
+ // Array(Map("name" -> "Justin", "age" -> 19))
+ // $example off:schema_inferring$
+ }
+
+ private def runProgrammaticSchemaExample(spark: SparkSession): Unit = {
+ import spark.implicits._
+ // $example on:programmatic_schema$
+ // Create an RDD
+ val peopleRDD =
spark.sparkContext.textFile("examples/src/main/resources/people.txt")
+
+ // The schema is encoded in a string
+ val schemaString = "name age"
+
+ // Generate the schema based on the string of schema
+ val fields = schemaString.split(" ")
+ .map(fieldName => StructField(fieldName, StringType, nullable =
true))
+ val schema = StructType(fields)
+
+ // Convert records of the RDD (people) to Rows
+ val rowRDD = peopleRDD
+ .map(_.split(","))
+ .map(attributes => Row(attributes(0), attributes(1).trim))
--- End diff --
Nit: Use 2-space indentation here.
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