Spark学习笔记之Spark SQL的具体使用
1. Spark SQL是什么?
- 处理结构化数据的一个spark的模块
- 它提供了一个编程抽象叫做DataFrame并且作为分布式SQL查询引擎的作用
2. Spark SQL的特点
- 多语言的接口支持(java python scala)
- 统一的数据访问
- 完全兼容hive
- 支持标准的连接
3. 为什么学习SparkSQL?
我们已经学习了Hive,它是将Hive SQL转换成MapReduce然后提交到集群上执行,大大简化了编写MapReduce的程序的复杂性,由于MapReduce这种计算模型执行效率比较慢。所有Spark SQL的应运而生,它是将Spark SQL转换成RDD,然后提交到集群执行,执行效率非常快!
4. DataFrame(数据框)
- 与RDD类似,DataFrame也是一个分布式数据容器
- 然而DataFrame更像传统数据库的二维表格,除了数据以外,还记录数据的结构信息,即schema
- DataFrame其实就是带有schema信息的RDD
5. SparkSQL1.x的API编程
<dependency> <groupId>org.apache.spark</groupId> <artifactId>spark-sql_2.11</artifactId> <version>${spark.version}</version> </dependency>
5.1 使用sqlContext创建DataFrame(测试用)
object Ops3 { def main(args: Array[String]): Unit = { val conf = new SparkConf().setAppName("Ops3").setMaster("local[3]") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) val rdd1 = sc.parallelize(List(Person("admin1", 14, "man"),Person("admin2", 16, "man"),Person("admin3", 18, "man"))) val df1: DataFrame = sqlContext.createDataFrame(rdd1) df1.show(1) } } case class Person(name: String, age: Int, sex: String);
5.2 使用sqlContxet中提供的隐式转换函数(测试用)
import org.apache.spark val conf = new SparkConf().setAppName("Ops3").setMaster("local[3]") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) val rdd1 = sc.parallelize(List(Person("admin1", 14, "man"), Person("admin2", 16, "man"), Person("admin3", 18, "man"))) import sqlContext.implicits._ val df1: DataFrame = rdd1.toDF df1.show() 5.3 使用SqlContext创建DataFrame(常用) val conf = new SparkConf().setAppName("Ops3").setMaster("local[3]") val sc = new SparkContext(conf) val sqlContext = new SQLContext(sc) val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest/") val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val rowRDD: RDD[Row] = linesRDD.map(line => { val lineSplit: Array[String] = line.split(",") Row(lineSplit(0), lineSplit(1).toInt, lineSplit(2)) }) val rowDF: DataFrame = sqlContext.createDataFrame(rowRDD, schema) rowDF.show()
6. 使用新版本的2.x的API
val conf = new SparkConf().setAppName("Ops5") setMaster ("local[3]") val sparkSession: SparkSession = SparkSession.builder().config(conf).getOrCreate() val sc = sparkSession.sparkContext val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest/") //数据清洗 val rowRDD: RDD[Row] = linesRDD.map(line => { val splits: Array[String] = line.split(",") Row(splits(0), splits(1).toInt, splits(2)) }) val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val df: DataFrame = sparkSession.createDataFrame(rowRDD, schema) df.createOrReplaceTempView("p1") val df2 = sparkSession.sql("select * from p1") df2.show()
7. 操作SparkSQL的方式
7.1 使用SQL语句的方式对DataFrame进行操作
val conf = new SparkConf().setAppName("Ops5") setMaster ("local[3]") val sparkSession: SparkSession = SparkSession.builder().config(conf).getOrCreate()//Spark2.x新的API相当于Spark1.x的SQLContext val sc = sparkSession.sparkContext val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest/") //数据清洗 val rowRDD: RDD[Row] = linesRDD.map(line => { val splits: Array[String] = line.split(",") Row(splits(0), splits(1).toInt, splits(2)) }) val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val df: DataFrame = sparkSession.createDataFrame(rowRDD, schema) df.createOrReplaceTempView("p1")//这是Sprk2.x新的API 相当于Spark1.x的registTempTable() val df2 = sparkSession.sql("select * from p1") df2.show()
7.2 使用DSL语句的方式对DataFrame进行操作
DSL(domain specific language ) 特定领域语言 val conf = new SparkConf().setAppName("Ops5") setMaster ("local[3]") val sparkSession: SparkSession = SparkSession.builder().config(conf).getOrCreate() val sc = sparkSession.sparkContext val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest/") //数据清洗 val rowRDD: RDD[Row] = linesRDD.map(line => { val splits: Array[String] = line.split(",") Row(splits(0), splits(1).toInt, splits(2)) }) val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val rowDF: DataFrame = sparkSession.createDataFrame(rowRDD, schema) import sparkSession.implicits._ val df: DataFrame = rowDF.select("name", "age").where("age>10").orderBy($"age".desc) df.show()
8. SparkSQL的输出
8.1 写出到JSON文件
val conf = new SparkConf().setAppName("Ops5") setMaster ("local[3]") val sparkSession: SparkSession = SparkSession.builder().config(conf).getOrCreate() val sc = sparkSession.sparkContext val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest") //数据清洗 val rowRDD: RDD[Row] = linesRDD.map(line => { val splits: Array[String] = line.split(",") Row(splits(0), splits(1).toInt, splits(2)) }) val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val rowDF: DataFrame = sparkSession.createDataFrame(rowRDD, schema) import sparkSession.implicits._ val df: DataFrame = rowDF.select("name", "age").where("age>10").orderBy($"age".desc) df.write.json("hdfs://uplooking02:8020/sparktest1")
8.2 写出到关系型数据库(mysql)
val conf = new SparkConf().setAppName("Ops5") setMaster ("local[3]") val sparkSession: SparkSession = SparkSession.builder().config(conf).getOrCreate() val sc = sparkSession.sparkContext val linesRDD: RDD[String] = sc.textFile("hdfs://uplooking02:8020/sparktest") //数据清洗 val rowRDD: RDD[Row] = linesRDD.map(line => { val splits: Array[String] = line.split(",") Row(splits(0), splits(1).toInt, splits(2)) }) val schema = StructType(List(StructField("name", StringType), StructField("age", IntegerType), StructField("sex", StringType))) val rowDF: DataFrame = sparkSession.createDataFrame(rowRDD, schema) import sparkSession.implicits._ val df: DataFrame = rowDF.select("name", "age").where("age>10").orderBy($"age".desc) val url = "jdbc:mysql://localhost:3306/test" //表会自动创建 val tbName = "person1"; val prop = new Properties() prop.put("user", "root") prop.put("password", "root") //SaveMode 默认为ErrorIfExists df.write.mode(SaveMode.Append).jdbc(url, tbName, prop)
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持脚本之家。
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