http://spark.apache.org/docs/latest/sql-getting-started.html 官网写的很详细。 这里只是个人觉得一些必要内容的摘抄。
第一步：Starting Point: SparkSession：
import org.apache.spark.sql.SparkSession val spark = SparkSession .builder() .appName("Spark SQL basic example") .config("spark.some.config.option", "some-value") .getOrCreate() // For implicit conversions like converting RDDs to DataFrames import spark.implicits._第二步：Creating DataFrames：
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| // +----+-------+Untyped Dataset Operations (aka DataFrame Operations)：
studentDF.sort(\$"name".desc,\$"id".desc).show() ##先安name排序，在按id排序 studentDF.select(\$"phone".as("mobile")).show() ## 别名 stu1.join(stu2,stu1("id")===stu2("id"),"join方式").show() ## join stuDF.take(3).foreach(println) stuDF.first ###first调用head，head调用head（1） stuDF.head stuDF.head(3) stuDF.filter("name=''or name='null'").show ## where()底层也是调用filter // This import is needed to use the $-notation import spark.implicits._ // Print the schema in a tree format df.printSchema() // root // |-- age: long (nullable = true) // |-- name: string (nullable = true) // Select only the "name" column df.select("name").show() // +-------+ // | name| // +-------+ // |Michael| // | Andy| // | Justin| // +-------+ // Select everybody, but increment the age by 1 df.select($"name", $"age" + 1).show() ## 需要导入隐式转换 df.select(df("name"),df("age")+10).show() ## 也可 // +-------+---------+ // | name|(age + 1)| // +-------+---------+ // |Michael| null| // | Andy| 31| // | Justin| 20| // +-------+---------+ // Select people older than 21 df.filter($"age" > 21).show() // +---+----+ // |age|name| // +---+----+ // | 30|Andy| // +---+----+ // Count people by age df.groupBy("age").count().show() // +----+-----+ // | age|count| // +----+-----+ // | 19| 1| // |null| 1| // | 30| 1| // +----+-----+Spark SQL支持两种不同的方法将现有RDD转换为数据集。第一种方法使用反射来推断包含特定类型对象的RDD的模式。这种基于反射的方法可以提供更简洁的代码，并且在您编写Spark应用程序时已经了解模式时可以很好地工作。
创建数据集的第二种方法是通过编程接口，允许您构建模式，然后将其应用于现有RDD。虽然此方法更详细，但它允许您在直到运行时才知道列及其类型时构造数据集。
Inferring the Schema Using Reflection： Spark SQL的Scala接口支持自动将包含RDD的案例类转换为DataFrame。case类定义表的模式。使用反射读取case类的参数名称，并成为列的名称。案例类也可以嵌套或包含复杂类型，如Seqs或Arrays。此RDD可以隐式转换为DataFrame，然后注册为表。表可以在后续SQL语句中使用。
// 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() // +------------+ // | value| // +------------+ // |Name: Justin| // +------------+ // or by field name teenagersDF.map(teenager => "Name: " + teenager.getAs[String]("name")).show() // +------------+ // | value| // +------------+ // |Name: Justin| // +------------+ // 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, Any]] = 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))Programmatically Specifying the Schema： 如果无法提前定义案例类（例如，记录的结构以字符串形式编码，或者文本数据集将被解析，而字段将针对不同的用户进行不同的投影），DataFrame则可以通过三个步骤以编程方式创建a 。
1、从原始RDD 创建Rows的RDD;2、创建由StructType匹配步骤1中创建rows的RDD中的结构 表示的模式。3、通过SparkSession提供的createDataFrame方法将模式应用于Rows 的RDD 。 import org.apache.spark.sql.types._ // 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)) // Apply the schema to the RDD val peopleDF = spark.createDataFrame(rowRDD, schema) // Creates a temporary view using the DataFrame peopleDF.createOrReplaceTempView("people") // SQL can be run over a temporary view created using DataFrames val results = spark.sql("SELECT name FROM people") // The results of SQL queries are DataFrames and support all the normal RDD operations // The columns of a row in the result can be accessed by field index or by field name results.map(attributes => "Name: " + attributes(0)).show() // +-------------+ // | value| // +-------------+ // |Name: Michael| // | Name: Andy| // | Name: Justin| // +-------------+