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code/spark/spark-core/file/emp.json
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14
code/spark/spark-core/file/emp.json
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@ -0,0 +1,14 @@
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{"EMPNO": 7369,"ENAME": "SMITH","JOB": "CLERK","MGR": 7902,"HIREDATE": "1980-12-17 00:00:00","SAL": 800.00,"COMM": null,"DEPTNO": 20}
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{"EMPNO": 7499,"ENAME": "ALLEN","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-02-20 00:00:00","SAL": 1600.00,"COMM": 300.00,"DEPTNO": 30}
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{"EMPNO": 7521,"ENAME": "WARD","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-02-22 00:00:00","SAL": 1250.00,"COMM": 500.00,"DEPTNO": 30}
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{"EMPNO": 7566,"ENAME": "JONES","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-04-02 00:00:00","SAL": 2975.00,"COMM": null,"DEPTNO": 20}
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{"EMPNO": 7654,"ENAME": "MARTIN","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-09-28 00:00:00","SAL": 1250.00,"COMM": 1400.00,"DEPTNO": 30}
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{"EMPNO": 7698,"ENAME": "BLAKE","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-05-01 00:00:00","SAL": 2850.00,"COMM": null,"DEPTNO": 30}
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{"EMPNO": 7782,"ENAME": "CLARK","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-06-09 00:00:00","SAL": 2450.00,"COMM": null,"DEPTNO": 10}
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{"EMPNO": 7788,"ENAME": "SCOTT","JOB": "ANALYST","MGR": 7566,"HIREDATE": "1987-04-19 00:00:00","SAL": 1500.00,"COMM": null,"DEPTNO": 20}
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{"EMPNO": 7839,"ENAME": "KING","JOB": "PRESIDENT","MGR": null,"HIREDATE": "1981-11-17 00:00:00","SAL": 5000.00,"COMM": null,"DEPTNO": 10}
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{"EMPNO": 7844,"ENAME": "TURNER","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-09-08 00:00:00","SAL": 1500.00,"COMM": 0.00,"DEPTNO": 30}
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{"EMPNO": 7876,"ENAME": "ADAMS","JOB": "CLERK","MGR": 7788,"HIREDATE": "1987-05-23 00:00:00","SAL": 1100.00,"COMM": null,"DEPTNO": 20}
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{"EMPNO": 7900,"ENAME": "JAMES","JOB": "CLERK","MGR": 7698,"HIREDATE": "1981-12-03 00:00:00","SAL": 950.00,"COMM": null,"DEPTNO": 30}
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{"EMPNO": 7902,"ENAME": "FORD","JOB": "ANALYST","MGR": 7566,"HIREDATE": "1981-12-03 00:00:00","SAL": 3000.00,"COMM": null,"DEPTNO": 20}
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{"EMPNO": 7934,"ENAME": "MILLER","JOB": "CLERK","MGR": 7782,"HIREDATE": "1982-01-23 00:00:00","SAL": 1300.00,"COMM": null,"DEPTNO": 10}
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@ -0,0 +1,65 @@
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package rdd.scala
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import org.apache.spark.sql.expressions.Aggregator
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import org.apache.spark.sql.{Encoder, Encoders, SparkSession, functions}
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// 1.定义员工类,对于可能存在null值的字段需要使用Option进行包装
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case class Emp(ename: String, comm: scala.Option[Double], deptno: Long, empno: Long,
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hiredate: String, job: String, mgr: scala.Option[Long], sal: Double)
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// 2.定义聚合操作的中间输出类型
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case class SumAndCount(var sum: Double, var count: Long)
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/* 3.自定义聚合函数
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* @IN 聚合操作的输入类型
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* @BUF reduction操作输出值的类型
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* @OUT 聚合操作的输出类型
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*/
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object MyAverage extends Aggregator[Emp, SumAndCount, Double] {
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// 4.用于聚合操作的的初始零值
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override def zero: SumAndCount = SumAndCount(0, 0)
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// 5.同一分区中的reduce操作
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override def reduce(avg: SumAndCount, emp: Emp): SumAndCount = {
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avg.sum += emp.sal
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avg.count += 1
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avg
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}
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// 6.不同分区中的merge操作
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override def merge(avg1: SumAndCount, avg2: SumAndCount): SumAndCount = {
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avg1.sum += avg2.sum
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avg1.count += avg2.count
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avg1
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}
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// 7.定义最终的输出类型
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override def finish(reduction: SumAndCount): Double = reduction.sum / reduction.count
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// 8.中间类型的编码转换
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override def bufferEncoder: Encoder[SumAndCount] = Encoders.product
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// 9.输出类型的编码转换
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override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
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}
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object SparkSqlApp {
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// 测试方法
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def main(args: Array[String]): Unit = {
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val spark = SparkSession.builder().appName("Spark-SQL").master("local[2]").getOrCreate()
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import spark.implicits._
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val ds = spark.read.json("file/emp.json").as[Emp]
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// 10.使用内置avg()函数和自定义函数分别进行计算，验证自定义函数是否正确
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val myAvg = ds.select(MyAverage.toColumn.name("average_sal")).first()
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val avg = ds.select(functions.avg(ds.col("sal"))).first().get(0)
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println("自定义average函数 : " + myAvg)
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println("内置的average函数 : " + avg)
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}
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}
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@ -1,57 +0,0 @@
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package rdd.scala
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import org.apache.spark.sql.{Dataset, SparkSession}
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object SparkSqlTest extends App {
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val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate()
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val dataFrames = spark.read.json("/usr/file/people.json")
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df.select("name").show()
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df.printSchema()
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import spark.implicits._
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val primitiveDS = Seq(1, 2, 3).toDS()
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primitiveDS.printSchema()
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primitiveDS.map(_ + 1).collect()
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peopleDS.select("name").show() //失败
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peopleDS.dtypes
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peopleDS.printSchema()
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peopleDS.toDF()
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// Encoders are created for case classes
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/* 1.此时把selected写成为selected ,编译器没有任何提示 */
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spark.sql("selected name from emp")
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/* 2.此时把selected写成为selected ,编译器有提示; 但是把字段名称name写成了nameEd ,编译器没有任何提示*/
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val dataFrames = spark.read.json("people.json")
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dataFrames.selected("nameEd").show()
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dataFrames.map(line=>line.name)
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case class Person(name: String, age: Long)
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/* 3.此时最为严格,语法和字段名称错误都被检测出来*/
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val dataSet: Dataset[Person] = spark.read.json("people.json").as[Person]
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dataSet.selected("name")
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dataSet.map(line=>line.name)
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dataSet.map(line=>line.nameEd)
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/* 4.即使在由RDD转换为dataFrame时候指定了类型Person,依然无法提示字段名称*/
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val peopleDF = spark.sparkContext
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.textFile("people.json")
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.map(_.split(","))
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.map(attributes => Person(attributes(0), attributes(1).trim.toInt))
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.toDF()
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peopleDF.map(line=>line.name)
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}
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@ -67,7 +67,7 @@ class TransformationTest {
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@Test
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def sample(): Unit = {
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val list = List(1, 2, 3, 4, 5, 6)
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sc.parallelize(list).sample(withReplacement = false, 0.5).foreach(println)
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sc.parallelize(list).sample(withReplacement = false, fraction = 0.5).foreach(println)
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}
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@ -144,7 +144,7 @@ sc.parallelize(list, 3).mapPartitionsWithIndex((index, iterator) => {
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```scala
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val list = List(1, 2, 3, 4, 5, 6)
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sc.parallelize(list).sample(withReplacement = false, 0.5).foreach(println)
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sc.parallelize(list).sample(withReplacement = false, fraction = 0.5).foreach(println)
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```
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### 1.7 union
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345
notes/SparkSQL-API基本使用.md
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345
notes/SparkSQL-API基本使用.md
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# SparkSQL API基本使用
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## 一、创建DataFrames
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Spark中所有功能的入口点是`SparkSession`，可以使用`SparkSession.builder()`创建。创建后应用程序就可以从现有RDD，Hive表或Spark数据源创建DataFrame。如下所示：
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```scala
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val spark = SparkSession.builder().appName("Spark-SQL").master("local[2]").getOrCreate()
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val df = spark.read.json("/usr/file/emp.json")
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df.show()
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// 建议在进行spark SQL编程前导入下面的隐式转换，因为DataFrames和dataSets中很多操作都依赖了隐式转换
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import spark.implicits._
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```
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这里可以启动`spark-shell`进行测试，需要注意的是`spark-shell`启动后会自动创建一个名为`spark`的`SparkSession`，在命令行中可以直接引用即可：
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## 二、DataFrames基本操作
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### 2.1 printSchema
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```scala
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// 以树形结构打印dataframe的schema信息
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df.printSchema()
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```
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### 2.2 使用DataFrame API进行基本查询
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```scala
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// 查询员工姓名及工作
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df.select($"ename", $"job").show()
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// 查询工资大于2000的员工信息
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df.filter($"sal" > 2000).show()
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// 分组统计部门人数
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df.groupBy("deptno").count().show()
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```
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### 2.3 使用SQL进行基本查询
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```scala
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// 首先需要将DataFrame注册为临时视图
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df.createOrReplaceTempView("emp")
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// 查询员工姓名及工作
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spark.sql("SELECT ename,job FROM emp").show()
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// 查询工资大于2000的员工信息
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spark.sql("SELECT * FROM emp where sal > 2000").show()
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// 分组统计部门人数
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spark.sql("SELECT deptno,count(ename) FROM emp group by deptno").show()
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```
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### 2.4 全局临时视图
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上面使用`createOrReplaceTempView`创建的是会话临时视图，它的生命周期仅限于会话范围，会随会话的结束而结束。
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你也可以使用`createGlobalTempView`创建全局临时视图，全局临时视图可以在所有会话之间共享，并直到整个Spark应用程序终止才会消失。全局临时视图被定义在内置的`global_temp`数据库下，需要使用限定名称进行引用，如`SELECT * FROM global_temp.view1`。
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```scala
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// 注册为全局临时视图
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df.createGlobalTempView("gemp")
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// 查询员工姓名及工作，使用限定名称进行引用
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spark.sql("SELECT ename,job FROM global_temp.gemp").show()
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// 查询工资大于2000的员工信息，使用限定名称进行引用
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spark.sql("SELECT * FROM global_temp.gemp where sal > 2000").show()
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// 分组统计部门人数，使用限定名称进行引用
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spark.sql("SELECT deptno,count(ename) FROM global_temp.gemp group by deptno").show()
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```
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## 三、创建Datasets
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### 3.1 由外部数据集创建
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```scala
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// 1.需要导入隐式转换
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import spark.implicits._
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// 2.创建case class,等价于Java Bean
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case class Emp(ename: String, comm: Double, deptno: Long, empno: Long,
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hiredate: String, job: String, mgr: Long, sal: Double)
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// 3.由外部数据集创建Datasets
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val ds = spark.read.json("/usr/file/emp.json").as[Emp]
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ds.show()
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```
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### 3.2 由内部数据集创建
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```scala
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// 1.需要导入隐式转换
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import spark.implicits._
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// 2.创建case class,等价于Java Bean
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case class Emp(ename: String, comm: Double, deptno: Long, empno: Long,
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hiredate: String, job: String, mgr: Long, sal: Double)
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// 3.由内部数据集创建Datasets
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val caseClassDS = Seq(Emp("ALLEN", 300.0, 30, 7499, "1981-02-20 00:00:00", "SALESMAN", 7698, 1600.0),
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Emp("JONES", 300.0, 30, 7499, "1981-02-20 00:00:00", "SALESMAN", 7698, 1600.0))
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.toDS()
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caseClassDS.show()
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```
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## 四、DataFrames与Datasets互相转换
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Spark提供了非常简单的转换方法用于DataFrames与Datasets互相转换，示例如下：
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```shell
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# DataFrames转Datasets
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scala> df.as[Emp]
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res1: org.apache.spark.sql.Dataset[Emp] = [COMM: double, DEPTNO: bigint ... 6 more fields]
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# Datasets转DataFrames
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scala> ds.toDF()
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res2: org.apache.spark.sql.DataFrame = [COMM: double, DEPTNO: bigint ... 6 more fields]
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```
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## 五、RDDs转换为DataFrames\Datasets
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Spark支持两种方式把RDD转换为DataFrames，分别是使用反射推断和指定schema转换。
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### 5.1 使用反射推断
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```scala
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// 1.导入隐式转换
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import spark.implicits._
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// 2.创建部门类
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case class Dept(deptno: Long, dname: String, loc: String)
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// 3.创建RDD并转换为dataSet
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val rddToDS = spark.sparkContext
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.textFile("/usr/file/dept.txt")
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.map(_.split("\t"))
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.map(line => Dept(line(0).trim.toLong, line(1), line(2)))
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.toDS() // 如果调用toDF()则转换为dataFrame
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```
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### 5.2 以编程方式指定Schema
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```scala
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import org.apache.spark.sql.Row
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import org.apache.spark.sql.types._
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// 1.定义每个列的列类型
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val fields = Array(StructField("deptno", LongType, nullable = true),
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StructField("dname", StringType, nullable = true),
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StructField("loc", StringType, nullable = true))
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// 2.创建schema
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val schema = StructType(fields)
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// 3.创建RDD
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val deptRDD = spark.sparkContext.textFile("/usr/file/dept.txt")
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val rowRDD = deptRDD.map(_.split("\t")).map(line => Row(line(0).toLong, line(1), line(2)))
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// 4.将RDD转换为dataFrame
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val deptDF = spark.createDataFrame(rowRDD, schema)
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deptDF.show()
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```
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## 六、使用自定义聚合函数
|
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|
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Scala提供了两种自定义聚合函数的方法，分别如下：
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+ 有类型的自定义聚合函数，主要适用于DataSets；
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+ 无类型的自定义聚合函数，主要适用于DataFrames。
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|
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以下分别使用两种方式来自定义一个求平均值的聚合函数，这里以计算员工平均工资为例。两种自定义方式分别如下：
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|
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### 6.1 有类型的自定义函数
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|
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```scala
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import org.apache.spark.sql.expressions.Aggregator
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import org.apache.spark.sql.{Encoder, Encoders, SparkSession, functions}
|
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|
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// 1.定义员工类,对于可能存在null值的字段需要使用Option进行包装
|
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case class Emp(ename: String, comm: scala.Option[Double], deptno: Long, empno: Long,
|
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hiredate: String, job: String, mgr: scala.Option[Long], sal: Double)
|
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|
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// 2.定义聚合操作的中间输出类型
|
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case class SumAndCount(var sum: Double, var count: Long)
|
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|
||||
/* 3.自定义聚合函数
|
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* @IN 聚合操作的输入类型
|
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* @BUF reduction操作输出值的类型
|
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* @OUT 聚合操作的输出类型
|
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*/
|
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object MyAverage extends Aggregator[Emp, SumAndCount, Double] {
|
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|
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// 4.用于聚合操作的的初始零值
|
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override def zero: SumAndCount = SumAndCount(0, 0)
|
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|
||||
// 5.同一分区中的reduce操作
|
||||
override def reduce(avg: SumAndCount, emp: Emp): SumAndCount = {
|
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avg.sum += emp.sal
|
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avg.count += 1
|
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avg
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}
|
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|
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// 6.不同分区中的merge操作
|
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override def merge(avg1: SumAndCount, avg2: SumAndCount): SumAndCount = {
|
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avg1.sum += avg2.sum
|
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avg1.count += avg2.count
|
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avg1
|
||||
}
|
||||
|
||||
// 7.定义最终的输出类型
|
||||
override def finish(reduction: SumAndCount): Double = reduction.sum / reduction.count
|
||||
|
||||
// 8.中间类型的编码转换
|
||||
override def bufferEncoder: Encoder[SumAndCount] = Encoders.product
|
||||
|
||||
// 9.输出类型的编码转换
|
||||
override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
|
||||
}
|
||||
|
||||
object SparkSqlApp {
|
||||
|
||||
// 测试方法
|
||||
def main(args: Array[String]): Unit = {
|
||||
|
||||
val spark = SparkSession.builder().appName("Spark-SQL").master("local[2]").getOrCreate()
|
||||
import spark.implicits._
|
||||
val ds = spark.read.json("file/emp.json").as[Emp]
|
||||
|
||||
// 10.使用内置avg()函数和自定义函数分别进行计算，验证自定义函数是否正确
|
||||
val myAvg = ds.select(MyAverage.toColumn.name("average_sal")).first()
|
||||
val avg = ds.select(functions.avg(ds.col("sal"))).first().get(0)
|
||||
|
||||
println("自定义average函数 : " + myAvg)
|
||||
println("内置的average函数 : " + avg)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
自定义聚合函数需要实现的方法比较多，这里以绘图的方式来演示其执行流程，以及每个方法的作用：
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
关于`zero`,`reduce`,`merge`,`finish`方法的作用在上图都有说明，这里解释一下中间类型和输出类型的编码转换，这个写法比较固定，基本上就是两种情况：
|
||||
|
||||
+ 自定义类型case class或者元组就使用`Encoders.product`方法；
|
||||
+ 基本类型就使用其对应名称的方法，如`scalaByte `，`scalaFloat`，`scalaShort`等。
|
||||
|
||||
```scala
|
||||
override def bufferEncoder: Encoder[SumAndCount] = Encoders.product
|
||||
override def outputEncoder: Encoder[Double] = Encoders.scalaDouble
|
||||
```
|
||||
|
||||
|
||||
|
||||
### 6.2 无类型的自定义聚合函数
|
||||
|
||||
理解了有类型的自定义聚合函数后，无类型的定义方式也基本相同，代码如下：
|
||||
|
||||
```scala
|
||||
import org.apache.spark.sql.expressions.{MutableAggregationBuffer, UserDefinedAggregateFunction}
|
||||
import org.apache.spark.sql.types._
|
||||
import org.apache.spark.sql.{Row, SparkSession}
|
||||
|
||||
object MyAverage extends UserDefinedAggregateFunction {
|
||||
// 1.聚合操作输入参数的类型,字段名称可以自定义
|
||||
def inputSchema: StructType = StructType(StructField("MyInputColumn", LongType) :: Nil)
|
||||
|
||||
// 2.聚合操作中间值的类型,字段名称可以自定义
|
||||
def bufferSchema: StructType = {
|
||||
StructType(StructField("sum", LongType) :: StructField("MyCount", LongType) :: Nil)
|
||||
}
|
||||
|
||||
// 3.聚合操作输出参数的类型
|
||||
def dataType: DataType = DoubleType
|
||||
|
||||
// 4.此函数是否始终在相同输入上返回相同的输出,通常为true
|
||||
def deterministic: Boolean = true
|
||||
|
||||
// 5.定义零值
|
||||
def initialize(buffer: MutableAggregationBuffer): Unit = {
|
||||
buffer(0) = 0L
|
||||
buffer(1) = 0L
|
||||
}
|
||||
|
||||
// 6.同一分区中的reduce操作
|
||||
def update(buffer: MutableAggregationBuffer, input: Row): Unit = {
|
||||
if (!input.isNullAt(0)) {
|
||||
buffer(0) = buffer.getLong(0) + input.getLong(0)
|
||||
buffer(1) = buffer.getLong(1) + 1
|
||||
}
|
||||
}
|
||||
|
||||
// 7.不同分区中的merge操作
|
||||
def merge(buffer1: MutableAggregationBuffer, buffer2: Row): Unit = {
|
||||
buffer1(0) = buffer1.getLong(0) + buffer2.getLong(0)
|
||||
buffer1(1) = buffer1.getLong(1) + buffer2.getLong(1)
|
||||
}
|
||||
|
||||
// 8.计算最终的输出值
|
||||
def evaluate(buffer: Row): Double = buffer.getLong(0).toDouble / buffer.getLong(1)
|
||||
}
|
||||
|
||||
object SparkSqlApp {
|
||||
|
||||
// 测试方法
|
||||
def main(args: Array[String]): Unit = {
|
||||
|
||||
val spark = SparkSession.builder().appName("Spark-SQL").master("local[2]").getOrCreate()
|
||||
// 9.注册自定义的聚合函数
|
||||
spark.udf.register("myAverage", MyAverage)
|
||||
|
||||
val df = spark.read.json("file/emp.json")
|
||||
df.createOrReplaceTempView("emp")
|
||||
|
||||
// 10.使用自定义函数和内置函数分别进行计算
|
||||
val myAvg = spark.sql("SELECT myAverage(sal) as avg_sal FROM emp").first()
|
||||
val avg = spark.sql("SELECT avg(sal) as avg_sal FROM emp").first()
|
||||
|
||||
println("自定义average函数 : " + myAvg)
|
||||
println("内置的average函数 : " + avg)
|
||||
}
|
||||
}
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 参考资料
|
||||
|
||||
[Spark SQL, DataFrames and Datasets Guide > Getting Started](https://spark.apache.org/docs/latest/sql-getting-started.html)
|
||||
BIN
pictures/spark-scheme.png
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pictures/spark-scheme.png
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|
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BIN
pictures/spark-sql-shell.png
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pictures/spark-sql-shell.png
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|
After Width: | Height: | Size: 49 KiB |
BIN
pictures/spark-sql-自定义函数.png
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pictures/spark-sql-自定义函数.png
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|
After Width: | Height: | Size: 70 KiB |
4
resources/dept.json
Normal file
4
resources/dept.json
Normal file
@ -0,0 +1,4 @@
|
||||
{"DEPTNO": 10,"DNAME": "ACCOUNTING","LOC": "NEW YORK"}
|
||||
{"DEPTNO": 20,"DNAME": "RESEARCH","LOC": "DALLAS"}
|
||||
{"DEPTNO": 30,"DNAME": "SALES","LOC": "CHICAGO"}
|
||||
{"DEPTNO": 40,"DNAME": "OPERATIONS","LOC": "BOSTON"}
|
||||
14
resources/emp.json
Normal file
14
resources/emp.json
Normal file
@ -0,0 +1,14 @@
|
||||
{"EMPNO": 7369,"ENAME": "SMITH","JOB": "CLERK","MGR": 7902,"HIREDATE": "1980-12-17 00:00:00","SAL": 800.00,"COMM": null,"DEPTNO": 20}
|
||||
{"EMPNO": 7499,"ENAME": "ALLEN","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-02-20 00:00:00","SAL": 1600.00,"COMM": 300.00,"DEPTNO": 30}
|
||||
{"EMPNO": 7521,"ENAME": "WARD","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-02-22 00:00:00","SAL": 1250.00,"COMM": 500.00,"DEPTNO": 30}
|
||||
{"EMPNO": 7566,"ENAME": "JONES","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-04-02 00:00:00","SAL": 2975.00,"COMM": null,"DEPTNO": 20}
|
||||
{"EMPNO": 7654,"ENAME": "MARTIN","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-09-28 00:00:00","SAL": 1250.00,"COMM": 1400.00,"DEPTNO": 30}
|
||||
{"EMPNO": 7698,"ENAME": "BLAKE","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-05-01 00:00:00","SAL": 2850.00,"COMM": null,"DEPTNO": 30}
|
||||
{"EMPNO": 7782,"ENAME": "CLARK","JOB": "MANAGER","MGR": 7839,"HIREDATE": "1981-06-09 00:00:00","SAL": 2450.00,"COMM": null,"DEPTNO": 10}
|
||||
{"EMPNO": 7788,"ENAME": "SCOTT","JOB": "ANALYST","MGR": 7566,"HIREDATE": "1987-04-19 00:00:00","SAL": 1500.00,"COMM": null,"DEPTNO": 20}
|
||||
{"EMPNO": 7839,"ENAME": "KING","JOB": "PRESIDENT","MGR": null,"HIREDATE": "1981-11-17 00:00:00","SAL": 5000.00,"COMM": null,"DEPTNO": 10}
|
||||
{"EMPNO": 7844,"ENAME": "TURNER","JOB": "SALESMAN","MGR": 7698,"HIREDATE": "1981-09-08 00:00:00","SAL": 1500.00,"COMM": 0.00,"DEPTNO": 30}
|
||||
{"EMPNO": 7876,"ENAME": "ADAMS","JOB": "CLERK","MGR": 7788,"HIREDATE": "1987-05-23 00:00:00","SAL": 1100.00,"COMM": null,"DEPTNO": 20}
|
||||
{"EMPNO": 7900,"ENAME": "JAMES","JOB": "CLERK","MGR": 7698,"HIREDATE": "1981-12-03 00:00:00","SAL": 950.00,"COMM": null,"DEPTNO": 30}
|
||||
{"EMPNO": 7902,"ENAME": "FORD","JOB": "ANALYST","MGR": 7566,"HIREDATE": "1981-12-03 00:00:00","SAL": 3000.00,"COMM": null,"DEPTNO": 20}
|
||||
{"EMPNO": 7934,"ENAME": "MILLER","JOB": "CLERK","MGR": 7782,"HIREDATE": "1982-01-23 00:00:00","SAL": 1300.00,"COMM": null,"DEPTNO": 10}
|
||||
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Block a user