SparkSQL(一)

一、概述

组件

运行机制

转 SparkSQL – 从0到1认识Catalyst  https://blog.csdn.net/qq_36421826/article/details/81988157

深入研究Spark SQL的Catalyst优化器（原创翻译）

更高效

查询优化

优化：把filter提前

数据源优化

编译优化 Code generation

DataSet和DataFrame

数据源

Parquet文件

Json文件

读取Hive中文件

外部数据源spark.read.format

二、程序设计

常规流程

API:SQL与DataFrame DSL

统计分析内容大小-全部内容大小，日志条数，最小内容大小，最大内容大小

package org.sparkcourse.log

import org.apache.spark.sql.{Row, SparkSession}

object LogAnalyzerSQL {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession.builder()
      .appName("Log Analyzer")
      .master("local")
      .getOrCreate()

    import spark.implicits._

    val accessLogs = spark
      .read
      .textFile("data/weblog/apache.access.log")
      .map(ApacheAccessLog.parseLogLine).toDF()

    accessLogs.createOrReplaceTempView("logs")

    // 统计分析内容大小-全部内容大小，日志条数，最小内容大小，最大内容大小
    val contentSizeStats: Row = spark.sql("SELECT SUM(contentSize), COUNT(*), MIN(contentSize), MAX(contentSize) FROM logs").first()
    val sum = contentSizeStats.getLong(0)
    val count = contentSizeStats.getLong(1)
    val min = contentSizeStats.getLong(2)
    val max = contentSizeStats.getLong(3)
    println("sum %s, count %s, min %s, max %s".format(sum, count, min, max))
    println("avg %s", sum / count)
    spark.close()

  }
}

ApacheAccessLog

package org.sparkcourse.log

import sun.security.x509.IPAddressName

case class ApacheAccessLog(ipAddress: String,
                           clientIdentd: String,
                           userId: String,
                           dateTime: String,
                           method: String,
                           endpoint: String,
                           protocol: String,
                           responseCode: Int,
                           contentSize: Long){
}

object ApacheAccessLog {
  // 64.242.88.10 - - [07/Mar/2004:16:05:49 -0800] "GET /twiki/bin/edit/Main/Double_bounce_sender?topicparent=Main.ConfigurationVariables HTTP/1.1" 401 12846
  val PATTERN = """^(\S+) (\S+) (\S+) \[([\w:/]+\s+\-\d{4})\] "(\S+) (\S+) (\S+)" (\d{3}) (\d+)""".r

  def parseLogLine(log: String): ApacheAccessLog = {
    log match {
      case PATTERN(ipAddress, clientIdentd, userId, dateTime, method, endpoint, protocol, responseCode, contentSize)
        => ApacheAccessLog(ipAddress, clientIdentd, userId, dateTime, method, endpoint, protocol, responseCode.toInt, contentSize.toLong)
      case _ => throw new RuntimeException(s"""Cannot parse log line: $log""")
    }
  }

String 的 .r 方法 将字符串转为正则表达式

方式一： 通过case class创建DataFrames（反射）

TestDataFrame1.scala

package com.bky

// 隐式类的导入
// 定义case class，相当于表结构
case class Dept(var id:Int, var position:String, var location:String)

// 需要导入SparkSession这个包
import org.apache.spark.sql.SparkSession

/**
  * 方式一： 通过case class创建DataFrames（反射）
  */
object TestDataFrame1 {

  def main(args: Array[String]): Unit = {

    /**
      * 直接使用SparkSession进行文件的创建。
      * 封装了SparkContext，SparkConf，SQLContext，
      * 为了向后兼容，SQLContext和HiveContext也被保存了下来
      */
    val spark = SparkSession
      .builder()  //构建sql
      .appName("TestDataFrame1") // 设置文件名
      .master("local[2]") // 设置executor
      .getOrCreate() //获取或创建

    import spark.implicits._  // 隐式转换
    // 将本地的数据读入RDD，将RDD与case class关联
    val deptRDD = spark.read.textFile("/Users/hadoop/data/dept.txt")
      .map(line => Dept(line.split("\t")(0).toInt,
        line.split("\t")(1),
        line.split("\t")(2).trim))

    // 将RDD转换成DataFrames（反射）
    val df = deptRDD.toDF()

    // 将DataFrames创建成一个临时的视图
    df.createOrReplaceTempView("dept")

    // 使用SQL语句进行查询
    spark.sql("select * from dept").show()

  }
}

精简版 TestDataFrame1.scala

package com.bky

import org.apache.spark.sql.SparkSession

object TestDataFrame1 extends App {
    val spark = SparkSession
      .builder()  //构建sql
      .appName("TestDataFrame1")
      .master("local[2]")
      .getOrCreate() 

    import spark.implicits._
    val deptRDD = spark.read.textFile("/Users/hadoop/data/dept.txt")
      .map(line => Dept(line.split("\t")(0).toInt,
        line.split("\t")(1),
        line.split("\t")(2).trim))

    val df = deptRDD.toDF()
    df.createOrReplaceTempView("dept")
    spark.sql("select * from dept").show()
}

case class Dept(var id:Int, var position:String, var location:String)

方式二：通过创建structType创建DataFrames（编程接口)

TestDataFrame2.scala

package com.bky

import org.apache.spark.sql.types._
import org.apache.spark.sql.{Row, SparkSession}

/**
  *
  * 方式二：通过创建structType创建DataFrames（编程接口）
  */
object TestDataFrame2 extends App {

  val spark = SparkSession
    .builder()
    .appName("TestDataFrame2")
    .master("local[2]")
    .getOrCreate()

  /**
    * 将RDD数据映射成Row，需要导入import org.apache.spark.sql.Row
    */
  import spark.implicits._
  val path = "/Users/hadoop/data/dept.txt"
  val fileRDD = spark.read.textFile(path)
  val rowRDD= fileRDD.map(line => {
    val fields = line.split("\t")
    Row(fields(0).toInt, fields(1), fields(2).trim)
  })

  // 创建StructType来定义结构
  val innerStruct = StructType(
    // 字段名，字段类型，是否可以为空
      StructField("id", IntegerType, true) ::
        StructField("position", StringType, true) ::
        StructField("location", StringType, true) :: Nil
  )

  val df = spark.createDataFrame(innerStruct)
  df.createOrReplaceTempView("dept")
  spark.sql("select * from dept").show()

}

方式三：通过json文件创建DataFrames

TestDataFrame3.scala

package com.bky

import org.apache.spark.sql.SparkSession

/**
  * 方式三：通过json文件创建DataFrames
  */
object TestDataFrame3 extends App {

  val spark = SparkSession
    .builder()
    .master("local[2]")
    .appName("TestDataFrame3")
    .getOrCreate()

  val path = "/Users/hadoop/data/test.json"
  val fileRDD = spark.read.json(path)
  fileRDD.createOrReplaceTempView("test")
  spark.sql("select * from test").show()
}

统计每种返回码的数量

package org.sparkcourse.log

import org.apache.spark.sql.{Row, SparkSession}

object LogAnalyzerSQL {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession.builder()
      .appName("Log Analyzer")
      .master("local")
      .getOrCreate()

    import spark.implicits._

    val accessLogs = spark
      .read
      .textFile("data/weblog/apache.access.log")
      .map(ApacheAccessLog.parseLogLine).toDF()

    accessLogs.createOrReplaceTempView("logs")

    // 统计每种返回码的数量.
    val responseCodeToCount = spark.sql("SELECT responseCode, COUNT(*) FROM logs GROUP BY responseCode LIMIT 100")
      .map(row => (row.getInt(0), row.getLong(1)))
      .collect()
    responseCodeToCount.foreach(print(_))
  }
}

统计哪个IP地址访问服务器超过10次

package org.sparkcourse.log

import org.apache.spark.sql.{Row, SparkSession}

object LogAnalyzerSQL {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession.builder()
      .appName("Log Analyzer")
      .master("local")
      .getOrCreate()

    import spark.implicits._

    val accessLogs = spark
      .read
      .textFile("data/weblog/apache.access.log")
      .map(ApacheAccessLog.parseLogLine).toDF()

    accessLogs.createOrReplaceTempView("logs")

    // 统计哪个IP地址访问服务器超过10次
    val ipAddresses = spark.sql("SELECT ipAddress, COUNT(*) AS total FROM logs GROUP BY ipAddress HAVING total > 10 LIMIT 100")
      .map(row => row.getString(0))
      .collect()
    ipAddresses.foreach(println(_))
  }
}

查询访问量最大的访问目的地址

package org.sparkcourse.log

import org.apache.spark.sql.{Row, SparkSession}

object LogAnalyzerSQL {
  def main(args: Array[String]): Unit = {

    val spark = SparkSession.builder()
      .appName("Log Analyzer")
      .master("local")
      .getOrCreate()

    import spark.implicits._

    val accessLogs = spark
      .read
      .textFile("data/weblog/apache.access.log")
      .map(ApacheAccessLog.parseLogLine).toDF()

    accessLogs.createOrReplaceTempView("logs")

    // 查询访问量最大的访问目的地址
    val topEndpoints = spark.sql("SELECT endpoint, COUNT(*) AS total FROM logs GROUP BY endpoint ORDER BY total DESC LIMIT 10")
      .map(row => (row.getString(0), row.getLong(1)))
      .collect()
    topEndpoints.foreach(println(_))

  }
}