Spark-SQL教程

创建SparkSession

val session = SparkSession
      .builder()
      .config(new SparkConf())
      .appName("test01") // 如果在conf中设置了，就不需要在此设置
      .master("local") // 如果在conf中设置了，就不需要在此设置
      .enableHiveSupport()   //开启这个选项时  spark sql on  hive  才支持DDL，没开启，spark只有catalog
      .config("hive.metastore.uris", "thrift://192.168.7.11:9083")
      .getOrCreate()

通过SparkSession创建SparkContext

val sc: SparkContext = session.sparkContext
sc.setLogLevel("ERROR")

SparkContext读写文件的操作

# sparkContext读文件
sc.textFile(bigFile,minPartitions = 2)  -> hadoopFile(path,TextInputFormat,keyClass,valueClass,minPartitions)  -> HadoopRDD
sc.wholeTextFiles(bigFile) ->  WholeTextFileRDD -继承了 NewHadoopRDD
sc.newAPIHadoopFile(bigFile)  -> 

//sc.textFile(bigFile,minPartitions = 2).take(10).foreach(println)
sc.hadoopFile(bigFile,
  classOf[TextInputFormat],
  classOf[LongWritable],
  classOf[Text],
  2).map(_._2.toString).setName(bigFile).take(10).foreach(println)


# 写入HDFS
删除HDFS文件
val hadoopConf = sparkContext.hadoopConfiguration
val hdfs = org.apache.hadoop.fs.FileSystem.get(hadoopConf)
if(hdfs.exists(path)){
     //为防止误删，禁止递归删除
     hdfs.delete(path,false)
   }

sc.textFile(bigFile).map(t => ("filename.txt",t)).saveAsHadoopFile(  // (键，值)
      "/data/hdfsPath", // 文件目录路径
      classOf[String], // 键的类型
      classOf[String], // 值的类型
      classOf[PairRDDMultipleOTextOutputFormat],  //重写以下方法 generateFileNameForKeyValue() 文件名, generateActualKey() 是否有键值在内容中
      //classOf[SnappyCodec]
    )

SparkSession文件读写

package com.chauncy.spark_sql.file_read_write

import com.chauncy.spark_sql.InitSparkSession
import com.chauncy.spark_sql.project.PairRDDMultipleOTextOutputFormat
import org.apache.hadoop.conf.Configuration
import org.apache.hadoop.fs.{FileSystem, Path}
import org.apache.spark.sql.SaveMode

/**
 * @author Chauncy Jin
 *         Tool: 
 * @date 2021/12/15
 */
object MyTextReadWrite {
  def deleteFile(hdfs: FileSystem, path: String): AnyVal = {
    val pathFile: Path = new Path(path)
    if (hdfs.exists(pathFile)) {
      //为防止误删，禁止递归删除
      hdfs.delete(pathFile, false)
    }
  }

  def main(args: Array[String]): Unit = {
    val sparkSession = InitSparkSession.sparkSession

    val sc = sparkSession.sparkContext
    val conf: Configuration = sc.hadoopConfiguration
    val hdfs: FileSystem = org.apache.hadoop.fs.FileSystem.get(conf)
    sc.setLogLevel("Error")

    val bigFile = "file:///Users/jinxingguang/java_project/bigdata3/spark-demo/data/pvuvdata"
    val jsonPath = "file:///Users/jinxingguang/java_project/bigdata3/spark-demo/data/test.json"

    /**
     * sparkContext读写文件
     */
    
    val outputPath = "/data/hdfsPath/text"
    deleteFile(hdfs, outputPath)
    sc.textFile(bigFile).map(t => ("filename.txt", t)).saveAsHadoopFile(
      outputPath,
      classOf[String],
      classOf[String],
      classOf[PairRDDMultipleOTextOutputFormat], //重写以下方法 generateFileNameForKeyValue() 文件名, generateActualKey() 是否有键值在内容中
      //classOf[SnappyCodec] 加入codec就有问题
    )


    /**
     * sparkSession文件的读写
     */
    val dataFrame = sparkSession.read.json(jsonPath)
    //dataFrame.show()
    val jsonOutputPath = "/data/hdfsPath/json"
    val orcOutputPath = "/data/hdfsPath/orc"
    deleteFile(hdfs, jsonOutputPath)
    deleteFile(hdfs, orcOutputPath)
    dataFrame.write.mode(SaveMode.Append).json(jsonOutputPath)
    dataFrame.write.mode(SaveMode.Append).orc(orcOutputPath)
    //println(new String("hello\n".getBytes, "GBK")) // 读取Windows格式的文件
    
  }
}

spark catalog操作

session.catalog.listDatabases().show() // 查看hive库
session.catalog.listTables().show()  // 查看表

// 将json文件映射为表
session.read.json("path.json").createTempView("user_json") // 将文件映射为user_json 表
val data = session.sql("select * from user_json") //  查询表
data.show() // 显示查询结果
data.printSchema() // 打印表头

session.catalog.cacheTable("user_json") // 将表缓存起来
session.catalog.clearCache() // 释放缓冲

读取数据并生成DataFrame实例

    // {"name":"zhangsan","age":20}...
    val spark = SparkSession.builder().config(new SparkConf()).master("local").appName("hello").getOrCreate()
    val frame = spark.read.json("file:///Users/jinxingguang/java_project/bigdata3/spark-demo/data/json")
    frame.show()
    println(frame.count())
    import spark.implicits._
    frame.filter($"age" >=20).show()
    
frame.createTempView("user") // 通过sparkSession向catalog注册表
frame.write.saveAsTable("hive_user") // 在hive中将数据保存成user表,不是临时向catalog注册的表

手动创建DataFrame

使用sparkSession简化生成DataFrame

package com.chauncy

import org.apache.spark.sql.{DataFrame, Dataset, SparkSession}

/**
 * @author Chauncy Jin
 *  Tool:  SparkSession 快速生成dataFrame
 * @date 2022/1/20
 */
object DataSetTest {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .appName("test")
      .master("local")
      .config("spark.sql.shuffle.partitions", "10")
      .config("hive.metastore.uris", "thrift://192.168.7.11:9083")
      .enableHiveSupport()
      .getOrCreate()
    val sc = spark.sparkContext
    sc.setLogLevel("Error")

    //读取数据并生成DataSet实例
    // 1087718415194492928&229071822400790528&升级&0&3&1&null&913683594017214464&2019-01-22 22:27:34.0&0&null&null&null
    import spark.implicits._
    val das = spark.read.textFile("/project/jxc.db/jxc/ods/ODS_AGENT_UPGRADE_LOG/*")
    val rddSet: Dataset[(String, String, String, String)] = das.filter(_.split("&").length > 8)
      .map((line: String) => {
        val ste = line.split("&")
       (ste(1), ste(3), ste(4), ste(8))
      })

    val frame: DataFrame = rddSet.toDF("uid", "ago_lev", "after_lev", "date")
    frame.printSchema()
  }
}

生成dataFrame – createDataFrame(rdd,StructType(StructFields))

package com.chauncy

import org.apache.spark.sql.types.{DataTypes, StructField, StructType}
import org.apache.spark.sql.{Row, SparkSession}

/**
 * @author Chauncy Jin
 * Tool: spark读取文件生成dataFrame 保存数据到hive
 * @date 2022/1/20
 */
object DataTest {
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder()
      .master("local")
      .appName("test")
      .config("spark.sql.shuffle.partitions", "10")
      .config("hive.metastore.uris", "thrift://192.168.7.11:9083")
      .enableHiveSupport()
      .getOrCreate()
    val sc = spark.sparkContext
    sc.setLogLevel("Error")

    // 数据+元数据  ==  DataFrame 类似表
    // 第一种方式 row类型的rdd + structType
    // 1. 数据 RDD[ROW]  一行一行的数据
    //读取数据并生成DataFrame实例
    // 1087718415194492928&229071822400790528&升级&0&3&1&null&913683594017214464&2019-01-22 22:27:34.0&0&null&null&null
    val das = spark.sparkContext.textFile("/project/jxc.db/jxc/ods/ODS_AGENT_UPGRADE_LOG/*")
    val rowRDD = das.filter(_.split("&").length > 8)
      .map((line: String) => {
        val ste = line.split("&")
        Row.apply(ste(1), ste(3), ste(4), ste(8))
      })

    // 2. 元数据 : StructType
    val fields = Array[StructField](
      StructField.apply("uid", DataTypes.StringType, nullable = true),
      StructField.apply("ago_lev", DataTypes.StringType, nullable = true),
      StructField.apply("after_lev", DataTypes.StringType, nullable = true),
      StructField.apply("date", DataTypes.StringType, nullable = true)
    )
    val schema = StructType.apply(fields) // 表的定义

    // 3. 创建DataFrame
    val dataFrame = spark.createDataFrame(rowRDD, schema)

    dataFrame.show(10,truncate = true) // 慎用，数据大会爆 使用num约束
    dataFrame.printSchema() // 打印表头
    dataFrame.createTempView("temp_change_log") // 通过session 向catalog中注册表名
    spark.sql("select * from temp_change_log limit 10").show()
    spark.sql("use jxc ")
    spark.sql(
      """
        |CREATE EXTERNAL TABLE IF NOT EXISTS `ods_change_log_chauncy` (
        |  `mall_user_id` string,
        |  `ago_lead_id` string,
        |  `after_lead_id` string,
        |  `create_date` string
        |)
        |ROW FORMAT DELIMITED  FIELDS TERMINATED BY '\001'
        |LOCATION 'hdfs://mycluster/project/jxc.db/jxc/ods/ods_change_log_chauncy';
        |""".stripMargin)
    spark.sql(
      """
        |insert overwrite table jxc.ods_change_log_chauncy select * from temp_change_log
        |""".stripMargin)
  }
}

动态封装DataFrame

person.txt
chauncy 18 0
lisa 22 1
yiyun 99 1

// 创建DataFrame动态封装
val rdd = sc.textFile("file:///Users/jinxingguang/java_project/bigdata-chauncy/spark-demo/data/person.txt")

// 每一列的类型约定
val userSchema = Array(
  "name string",
  "age int",
  "sex int"
)

// 转换类型
def toDataType(col: (String, Int)) = {
  userSchema(col._2).split("[ ]")(1) match {
    case "int" => col._1.toInt
    case "date" =>  java.sql.Date.valueOf(col._1)
    case _ => col._1.toString
  }
}

// 1 row rdd
// rdd.map(_.split(" ")).map(line => Row.apply(line(0),line(1).toInt)) // 写死了
val rddRow: RDD[Row] = rdd.map(_.split(" "))
.map(x => x.zipWithIndex) // [(chauncy,0), (18,1)]
.map(x => x.map(toDataType(_)))
.map(line => Row.fromSeq(line)) // row 表示了很多的列，每个列要标识出准确的类型

// 2 structType
// 函数，获取每一列的类型
def getDataType(v: String) = {
  v match {
    case "int" => DataTypes.IntegerType // 24
    case "binary" => DataTypes.BinaryType
    case "boolean" => DataTypes.BooleanType // true false
    case "byte" => DataTypes.ByteType
    case "date" => DataTypes.DateType // 2022-12-31
    case "long" => DataTypes.LongType
    case "timestamp" => DataTypes.TimestampType
    case _ => DataTypes.StringType // 字符串
  }
}
// 列的属性
val fields: Array[StructField] = userSchema.map(_.split(" ")).map(x => StructField.apply(x(0), getDataType(x(1))))
val schema: StructType = StructType.apply(fields)
// schema = schema01等价
val schema01: StructType = StructType.fromDDL("name string,age int,sex int")

val dataFrame = session.createDataFrame(rddRow, schema01)
dataFrame.show()
dataFrame.printSchema()
// 通过session向catalog注册
dataFrame.createTempView("user")

session.sql("select * from user").show()

• 方式三 – bean类型的rdd + javabean

// Bean类型实例
class Person extends Serializable {
    @BeanProperty
    var name: String = ""
    @BeanProperty
    var age: Int = 0
    @BeanProperty
    var sex: Int = 0
}

-------
//第二种方式： bean类型的rdd + javabean
//第二种方式： bean类型的rdd + javabean
val rdd = sc.textFile("file:///Users/jinxingguang/java_project/bigdata-chauncy/spark-demo/data/person.txt")
val person = new Person // 放到外部需要 extends Serializable
// 1,mr,spark  pipeline  iter  一次内存飞过一条数据：：-> 这一条记录完成读取/计算/序列化
// 2，分布式计算，计算逻辑由 Driver  序列化，发送给其他jvm的Executor中执行
val rddBean: RDD[Person] = rdd.map(_.split(" ")).map(arr => {
  //      val person = new Person
  person.setName(arr(0))
  person.setAge(arr(1).toInt)
  person.setSex(arr(2).toInt)
  person
})
val dataFrame = session.createDataFrame(rddBean, classOf[Person])
dataFrame.show()
dataFrame.printSchema()
// 通过session向catalog注册
dataFrame.createTempView("user")

session.sql("select * from user").show()
 

创建DataSet

读取文本文件并生成dataset实例

  case class User(name:String,age:BigInt) extends Serializable
  def main(args: Array[String]): Unit = {
    val spark = SparkSession.builder().config(new SparkConf()).master("local").appName("hello").getOrCreate()
    import spark.implicits._
    // {"name":"zhangsan","age":20}...
    val data = spark.read.json("file:///Users/jinxingguang/java_project/bigdata3/spark-demo/data/json").as[User]
    data.show()
    data.filter($"age" >=20).show()
  }

自定义属性生成dataset

/*
    文本文件要转*结构化*数据再进行计算

    文本文件 -> 中间态数据: ETL过程  文件格式，分区和分桶

    分区可以让计算加载的数据减少，分桶可以让计算过程中的shuffle移动量减少
     */
    // Spark 的DataSet 既可以按collection类似于rdd的方式操作，也可以按SQL的方式操作
val rddData: Dataset[String] = session.read.textFile("file:///Users/jinxingguang/java_project/bigdata-chauncy/spark-demo/data/person.txt")
import  session.implicits._
val person: Dataset[(String, Int)] = rddData.map(
  line => {
    val strs = line.split(" ")
    (strs(0), strs(1).toInt)
  }
)/*(Encoders.tuple(Encoders.STRING,Encoders.scalaInt)) //自己写编码器，也可以直接导入隐式类*/

// 附加表的列描述
val cPerson = person.toDF("name", "age")
cPerson.show()
cPerson.printSchema()

写入数据到ES

package com.chauncy



import org.apache.spark.SparkConf
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.elasticsearch.spark.sql.EsSparkSQL


/**
 * @author Chauncy Jin
 *         Tool:
 *         add jar hdfs://emr-header-1.cluster-246415:9000/jars/es-7.6.2/elasticsearch-hadoop-hive-7.6.2.jar;
 * @date 2022/1/20
 */
object HiveToEs {
  def main(args: Array[String]): Unit = {
    val sparkConf = new SparkConf().setAppName("ES_Hive").setMaster("local")
    sparkConf.set("es.nodes", "172.20.5.11,172.20.5.12,172.20.5.13")
    //sparkConf.set("es.nodes","172.20.5.11")
    sparkConf.set("es.port", "9200")
    sparkConf.set("es.index.auto.create", "true")
    sparkConf.set("es.write.operation", "index")
    sparkConf.set("spark.es.batch.size.entries", "10000")
    sparkConf.set("spark.es.batch.write.refresh", "false")
    sparkConf.set("spark.es.scroll.size", "10000")
    sparkConf.set("spark.es.input.use.sliced.partitions", "false")
    sparkConf.set("hive.metastore.uris", "thrift://172.20.1.232:9083")
    val sparkSession: SparkSession = SparkSession.builder().config(sparkConf).enableHiveSupport().getOrCreate()
    sparkSession.sparkContext.setLogLevel("ERROR")

    //val url: String = "jdbc:mysql://rm-uf638jr947ng36h26co.mysql.rds.aliyuncs.com:3306/data_bi?useSSL=false&useUnicode=true&characterEncoding=UTF-8&rewriteBatchedStatements=true"
    //val table: String = "temp_zz"
    //val properties: Properties = new Properties()
    //properties.put("user", "dev_client_001")
    //properties.put("password", "meifute@123")
    //properties.put("driver", "com.mysql.jdbc.Driver")
    //properties.setProperty("batchsize", "10000")
    //properties.setProperty("fetchsize", "10000")
    //val course: DataFrame = sparkSession.read.jdbc(url, table, properties)


    sparkSession.sql("use data_prod ")
    val course: DataFrame = sparkSession.sql(
      """
        |select
        |a.user_id as user_id,
        |cast(a.metric_value as string) as metric_value,
        |a.load_date as load_date,
        |a.dt as dt,
        |a.metric_name as metric_name
        |from ads_fact_metric_agent a
        |""".stripMargin)

    //course.show()
    EsSparkSQL.saveToEs(course, "ads_fact_metric_agent")

    sparkSession.stop()

    /*
    Warning:scalac: While parsing annotations in /Users/jinxingguang/.m2/repository/org/apache/spark/spark-core_2.12/3.1.2/spark-core_2.12-3.1.2.jar(org/apache/spark/rdd/RDDOperationScope.class), could not find NON_ABSENT in enum object JsonInclude$Include.
This is likely due to an implementation restriction: an annotation argument cannot refer to a member of the annotated class (SI-7014).
     */
  }
}

DataSet的使用 - 读写文件

val conf = new SparkConf().setMaster("local").setAppName("sql hive")
val session = SparkSession
.builder()
.config(conf)
//      .enableHiveSupport()
.getOrCreate()
val sc = session.sparkContext
sc.setLogLevel("Error")

import  session.implicits._
val dataDF: DataFrame = List(
  "hello world",
  "hello world",
  "hello msb",
  "hello world",
  "hello world",
  "hello spark",
  "hello world",
  "hello spark"
).toDF("line") // 列名为line


dataDF.createTempView("ooxx") // 注册到catalog

val df: DataFrame = session.sql("select * from ooxx")
df.show()
df.printSchema()

// 计算词频 使用SQL的方式
//    session.sql(" select word,count(1) from  (select explode(split(line,' ')) word from ooxx) as tt group by word").show()

// 计算词频 使用api的方式 df 相当于 from table
val subTab = dataDF.selectExpr("explode(split(line,' ')) word")
val dataset: RelationalGroupedDataset = subTab.groupBy("word")
val res = dataset.count()


// 将结果保存到parquet文本
res.write.mode(SaveMode.Append).parquet("file:///Users/jinxingguang/java_project/bigdata-chauncy/spark-demo/data/out/ooxx")

// 读取parquet文本
val frame: DataFrame = session.read.parquet("file:///Users/jinxingguang/java_project/bigdata-chauncy/spark-demo/data/out/ooxx")
frame.show()
frame.printSchema()

/*
        基于文件的行式：
        session.read.parquet()
        session.read.textFile()
        session.read.json()
        session.read.csv()
        读取任何格式的数据源都要转换成DF
        res.write.parquet()
        res.write.orc()
        res.write.text()
        */

DataSet的使用 - 读写MySQL

package com.chauncy.spark_sql.file_read_write

import java.util.Properties

import org.apache.spark.SparkConf
import org.apache.spark.sql.{DataFrame, SaveMode, SparkSession}

/**
 * @author Chauncy Jin
 *         Tool: 
 * @date 2021/10/13
 */
object MySparkMysql {
  def main(args: Array[String]): Unit = {
    // val conf = new SparkConf().setMaster("local").setAppName("mysql")
    val session = SparkSession
      .builder()
      .master("local")
      .appName("mysql")
      .config(new SparkConf()) // 可有可无
      .config("spark.sql.shuffle.partitions", "1") // 默认会有100并行度的参数
      .config("hive.metastore.uris", "thrift://192.168.7.11:9083")
      .enableHiveSupport()
      .getOrCreate()
    val sc = session.sparkContext
    // sc.setLogLevel("ERROR")
    sc.setLogLevel("INFO")

    val properties = new Properties()
    properties.setProperty("user", "用户")
    properties.setProperty("password", "密码")
    properties.setProperty("driver", "com.mysql.jdbc.Driver")
    properties.setProperty("batchsize", "10000") // 批处理的大小
    properties.setProperty("fetchsize", "10000") // 一次拿多少数据
    val url_ex = s"jdbc:mysql://ip:port/tablename?useSSL=false&useUnicode=true&characterEncoding=UTF-8&rewriteBatchedStatements=true"  // rewriteBatchedStatements=true 打开批处理开关
    sparkSession.sql("use jxc")
    sparkSession.sql("select * from ODS_AGENT_UPGRADE_LOG")
      .write.mode(SaveMode.Overwrite).jdbc(url_ex, "chauncy_agent_upgrade_log", properties)

    /*  */
    // 没有介入hive时，数据源都是DS/DF
    val jdbcDF: DataFrame = session.read.jdbc(properties.getProperty("url"), "student", properties)
    jdbcDF.show(10, truncate = true)
    jdbcDF.createTempView("student_spark")
    session.sql("select * from student_spark").show()

    // 写数据到mysql
    import org.apache.spark.sql.functions._ //导入函数，可以使用 udf、col 、lit方法
    jdbcDF.withColumn("status", lit(1)) // 加入一列
      .write.mode(SaveMode.Overwrite)
      .jdbc(properties.getProperty("url"), "student_copy", properties)
    // jdbcDF.write.jdbc(properties.getProperty("url"),"student_copy",properties) // 数据移动
    

    /**
     * 连表查询
     */
    val usersDF: DataFrame = session.read.jdbc(properties.get("url").toString, "student", properties)
    val scoreDF: DataFrame = session.read.jdbc(properties.get("url").toString, "score", properties)

    usersDF.createTempView("userstab")
    scoreDF.createTempView("scoretab")

    val resDF: DataFrame = session.sql(
      """
        |SELECT
        |	userstab.s_id,
        |	userstab.s_name,
        |	scoretab.s_score
        |FROM
        |	userstab
        |	JOIN scoretab ON userstab.s_id = scoretab.s_id
        |""".stripMargin)
    resDF.show()
    resDF.printSchema()
    // 默认并行度是100
    // 21/10/13 07:47:05 INFO DAGScheduler: Submitting 100 missing tasks from ResultStage 11
  }
}

Spark_On_Hive

standalone模式

val ss: SparkSession = SparkSession
.builder()
.master("local")
.appName("standalone hive")
.config("spark.sql.shuffle.partitions", 1)
.config("spark.sql.warehouse.dir", "file:///Users/jinxingguang/java_project/bigdata-chauncy/spark/warehouse")
.enableHiveSupport()   //开启hive支持   ？   自己会启动hive的metastore
.getOrCreate()
val sc: SparkContext = ss.sparkContext
//    sc.setLogLevel("ERROR")
//    ss.sql("create table xxx(name string,age int)")
//    ss.sql("insert into xxx values ('zhangsan',18),('lisi',22)")
ss.sql("select * from xxx").show()
ss.catalog.listTables().show()

// 有数据库的概念
ss.sql("create database chauncy_db")
ss.sql("use chauncy_db")
ss.sql("create table meifute(name string,age int)")
ss.catalog.listTables().show()

IDEA使用spark程序交互hive

val ss: SparkSession = SparkSession
  .builder()
  .appName("cluster on hive")
  .master("local")
  .config("hive.metastore.uris", "thrift://node01:9083")
  .enableHiveSupport()
  .getOrCreate()
val sc: SparkContext = ss.sparkContext
sc.setLogLevel("ERROR")

ss.sql("create database IF NOT EXISTS spark_hive ")
ss.sql("use spark_hive")
ss.catalog.listTables().show() // 报错了,删除掉hive中的hbase表
// Class org.apache.hadoop.hive.hbase.HBaseSerDe not found

// 读取hive表, sparkSession.table(表名) 产生dataframe
sparkSession.table("TO_YCAK_MAC_LOC_D")

import ss.implicits._

val df01: DataFrame = List(
  "zhangsan",
  "lisi"
).toDF("name")
df01.createTempView("ooxx") // 通过session向catalog注册表

// SQL创建表，并插入数据
ss.sql("create table IF NOT EXISTS hddffs ( id int,age int)") //DDL
// 需要 core-site.xml 和 hdfs-site.xml 的hadoop配置
ss.sql("insert into hddffs values (4,3),(8,4),(9,3)") // DML  数据是通过spark自己和hdfs进行访问

ss.sql("show tables").show() // 临时表，没有保存到hive中
df01.write.saveAsTable("oxox") // 在hive中将数据保存成oxox表,不是临时向catalog注册的表
ss.sql("show tables").show() // 临时表，没有保存到hive中

Spark-Sql服务

SPARK整合HIVE

• spark配置

只需要metastore的地址配置就可以了
cat > /opt/bigdata/spark-2.3.4/conf/hive-site.xml <<-EOF



    
        hive.metastore.uris
        thrift://node01:9083
        metastore地址
    

EOF

• spark相关的命令启动服务

启动spark-shell
cd /opt/bigdata/spark-2.3.4/bin
./spark-shell --master yarn
scala> spark.sql("show tables").show


启动spark-sql
cd /opt/bigdata/spark-2.3.4/bin
./spark-sql --master yarn
查看网页 http://node03:8088/cluster 会出现SparkSQL
可以直接执行SQL，跟hive中共享,两边都可以操作
spark-sql> show tables;

Spark-Sql

# 对外暴露JDBC服务，接受SQL执行
cd /opt/bigdata/spark-2.3.4/sbin
./start-thriftserver.sh --master yarn
查看网页 http://node03:8088/cluster  **多了一个 Thrift JDBC/ODBC Server**


# 使用spark的beeline连接
/opt/bigdata/spark-2.3.4/bin/beeline -u jdbc:hive2://node01:10000/default -n god

打印 Connected to: Spark SQL (version 2.3.4)

/usr/lib/spark-current/bin/beeline -u jdbc:hive2://localhost:10000 -n hadoop
show tables;

spark-sql函数

官网文档 2.3.4版本

org/apache/spark/sql/functions.scala scala对应的源码

自定义函数

package com.chauncy.spark_dataframe_dataset

import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
import org.apache.spark.sql.types.{DataType, DoubleType, IntegerType, StructField, StructType}
import org.apache.spark.sql.{DataFrame, Row, SparkSession}

object MySpark_Sql_functions {
  def main(args: Array[String]): Unit = {
    val ss: SparkSession = SparkSession.builder()
    .master("local")
    .appName("ProduceClientLog")
    .config("hive.metastore.uris", "thrift://192.168.7.11:9083") // hive metastore 的地址
    //.config(new SparkConf())
    .enableHiveSupport()
    .getOrCreate()
    ss.sparkContext.setLogLevel("ERROR")


    import ss.implicits._
    // 将List转成DataFrame
    val dataDF: DataFrame = List(
      ("A", 1, 67),
      ("D", 1, 87),
      ("B", 1, 54),
      ("D", 2, 24),
      ("C", 3, 64),
      ("R", 2, 54),
      ("E", 1, 74)
    ).toDF("name", "class", "score")

    dataDF.createTempView("users")

    // 分组，排序统计
    //    ss.sql("select name,sum(score) " +
    //      " from users " +
    //      "group by name" +
    //      " order by name").show()
    ss.sql("select * from users order by name asc,score desc").show()

    //udf 普通的自定义函数
    ss.udf.register("ooxx", (x: Int) => {
      x * 10
    })
    ss.sql("select *,ooxx(score) mut_10 from users ").show()

    // 自定义聚合函数
    //    class MyAggFun extends UserDefinedAggregateFunction
    ss.udf.register("myagg", new MyAvgFun)
    ss.sql("select name," +
      " myagg(score) " +
      " from users " +
      " group by name").show()
  }

  class MyAvgFun extends UserDefinedAggregateFunction {
    // 输入列的类型
    override def inputSchema: StructType = {
      // myagg(score)
      StructType.apply(Array(StructField.apply("score", IntegerType, false)))
    }

    override def bufferSchema: StructType = {
      StructType.apply(
        Array(
          StructField.apply("sum", IntegerType, false),
          StructField.apply("count", IntegerType, false)
        )
      )
    }

    override def dataType: DataType = DoubleType
    // 是否幂等
    override def deterministic: Boolean = true
    // 是否初始化
    override def initialize(buffer: MutableAggregationBuffer): Unit = {
      buffer(0) = 0
      buffer(1) = 0
    }
    // 来一条数据更新一次
    override def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
      // 组内，一条记录调用一次
      buffer(0) = buffer.getInt(0) + input.getInt(0) // sum
      buffer(1) = buffer.getInt(1) + 1
    }
    // 溢写怎么计算
    override def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
      buffer1(0) = buffer1.getInt(0) + buffer2.getInt(0)
      buffer1(1) = buffer1.getInt(1) + buffer2.getInt(1)
    }
    // 最后的结果
    override def evaluate(buffer: Row): Double = {
      buffer.getInt(0) /buffer.getInt(1)
    }
  }
}