diff --git a/code/spark/spark-core/file/emp.json b/code/spark/spark-core/file/emp.json
new file mode 100644
index 0000000..03af1f5
--- /dev/null
+++ b/code/spark/spark-core/file/emp.json
@@ -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}
\ No newline at end of file
diff --git a/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlApp.scala b/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlApp.scala
new file mode 100644
index 0000000..c70af1f
--- /dev/null
+++ b/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlApp.scala
@@ -0,0 +1,65 @@
+package rdd.scala
+
+import org.apache.spark.sql.expressions.Aggregator
+import org.apache.spark.sql.{Encoder, Encoders, SparkSession, functions}
+
+// 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
+case class Emp(ename: String, comm: scala.Option[Double], deptno: Long, empno: Long,
+               hiredate: String, job: String, mgr: scala.Option[Long], sal: Double)
+
+// 2.定义聚合操作的中间输出类型
+case class SumAndCount(var sum: Double, var count: Long)
+
+/* 3.自定义聚合函数
+ * @IN  聚合操作的输入类型
+ * @BUF reduction操作输出值的类型
+ * @OUT 聚合操作的输出类型
+ */
+object MyAverage extends Aggregator[Emp, SumAndCount, Double] {
+
+
+  // 4.用于聚合操作的的初始零值
+  override def zero: SumAndCount = SumAndCount(0, 0)
+
+
+  // 5.同一分区中的reduce操作
+  override def reduce(avg: SumAndCount, emp: Emp): SumAndCount = {
+    avg.sum += emp.sal
+    avg.count += 1
+    avg
+  }
+
+  // 6.不同分区中的merge操作
+  override def merge(avg1: SumAndCount, avg2: SumAndCount): SumAndCount = {
+    avg1.sum += avg2.sum
+    avg1.count += avg2.count
+    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)
+  }
+}
diff --git a/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlTest.scala b/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlTest.scala
deleted file mode 100644
index 1907cf2..0000000
--- a/code/spark/spark-core/src/main/java/rdd/scala/SparkSqlTest.scala
+++ /dev/null
@@ -1,57 +0,0 @@
-package rdd.scala
-
-import org.apache.spark.sql.{Dataset, SparkSession}
-
-
-object SparkSqlTest extends App {
-
-
-
-  val spark = SparkSession.builder().appName("Spark SQL basic example").config("spark.some.config.option", "some-value").getOrCreate()
-
-  val dataFrames = spark.read.json("/usr/file/people.json")
-
-  df.select("name").show()
-
-  df.printSchema()
-
-
-
-
-  import spark.implicits._
-
-  val primitiveDS = Seq(1, 2, 3).toDS()
-  primitiveDS.printSchema()
-  primitiveDS.map(_ + 1).collect()
-
-  peopleDS.select("name").show()  //失败
-  peopleDS.dtypes
-  peopleDS.printSchema()
-  peopleDS.toDF()
-  // Encoders are created for case classes
-
-  /* 1.此时把selected写成为selected ,编译器没有任何提示 */
-  spark.sql("selected name from emp")
-
-  /* 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*/
-  val dataFrames = spark.read.json("people.json")
-  dataFrames.selected("nameEd").show()
-  dataFrames.map(line=>line.name)
-
-  case class Person(name: String, age: Long)
-
-  /* 3.此时最为严格,语法和字段名称错误都被检测出来*/
-  val dataSet: Dataset[Person] = spark.read.json("people.json").as[Person]
-  dataSet.selected("name")
-  dataSet.map(line=>line.name)
-  dataSet.map(line=>line.nameEd)
-
-  /* 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*/
-  val peopleDF = spark.sparkContext
-    .textFile("people.json")
-    .map(_.split(","))
-    .map(attributes => Person(attributes(0), attributes(1).trim.toInt))
-    .toDF()
-  peopleDF.map(line=>line.name)
-
-}
diff --git a/code/spark/spark-core/src/main/java/rdd/scala/TransformationTest.scala b/code/spark/spark-core/src/main/java/rdd/scala/TransformationTest.scala
index 1229dae..1b3f03e 100644
--- a/code/spark/spark-core/src/main/java/rdd/scala/TransformationTest.scala
+++ b/code/spark/spark-core/src/main/java/rdd/scala/TransformationTest.scala
@@ -67,7 +67,7 @@ class TransformationTest {
   @Test
   def sample(): Unit = {
     val list = List(1, 2, 3, 4, 5, 6)
-    sc.parallelize(list).sample(withReplacement = false, 0.5).foreach(println)
+    sc.parallelize(list).sample(withReplacement = false, fraction = 0.5).foreach(println)
   }
 
 
diff --git a/notes/Spark-Transformation和Action.md b/notes/Spark-Transformation和Action.md
index 5ea245a..eb44935 100644
--- a/notes/Spark-Transformation和Action.md
+++ b/notes/Spark-Transformation和Action.md
@@ -144,7 +144,7 @@ sc.parallelize(list, 3).mapPartitionsWithIndex((index, iterator) => {
 
 ```scala
 val list = List(1, 2, 3, 4, 5, 6)
-sc.parallelize(list).sample(withReplacement = false, 0.5).foreach(println)
+sc.parallelize(list).sample(withReplacement = false, fraction = 0.5).foreach(println)
 ```
 
 ### 1.7 union
diff --git a/notes/SparkSQL-API基本使用.md b/notes/SparkSQL-API基本使用.md
new file mode 100644
index 0000000..dbcd47b
--- /dev/null
+++ b/notes/SparkSQL-API基本使用.md
@@ -0,0 +1,345 @@
+# SparkSQL API基本使用
+
+## 一、创建DataFrames
+
+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：
+
+```scala
+val spark = SparkSession.builder().appName("Spark-SQL").master("local[2]").getOrCreate()
+val df = spark.read.json("/usr/file/emp.json")
+df.show()
+
+// 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
+import spark.implicits._
+```
+
+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：
+
+![spark-sql-shell](D:\BigData-Notes\pictures\spark-sql-shell.png)
+
+## 二、DataFrames基本操作
+
+### 2.1 printSchema
+
+```scala
+// 以树形结构打印dataframe的schema信息 
+df.printSchema()
+```
+
+![spark-scheme](D:\BigData-Notes\pictures\spark-scheme.png)
+
+### 2.2 使用DataFrame API进行基本查询
+
+```scala
+// 查询员工姓名及工作
+df.select($"ename", $"job").show()
+
+// 查询工资大于2000的员工信息
+df.filter($"sal" > 2000).show()
+
+// 分组统计部门人数
+df.groupBy("deptno").count().show()
+```
+
+### 2.3 使用SQL进行基本查询
+
+```scala
+// 首先需要将DataFrame注册为临时视图
+df.createOrReplaceTempView("emp")
+
+// 查询员工姓名及工作
+spark.sql("SELECT ename,job FROM emp").show()
+
+// 查询工资大于2000的员工信息
+spark.sql("SELECT * FROM emp where sal > 2000").show()
+
+// 分组统计部门人数
+spark.sql("SELECT deptno,count(ename) FROM emp group by deptno").show()
+```
+
+### 2.4 全局临时视图
+
+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。
+
+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`。
+
+```scala
+// 注册为全局临时视图
+df.createGlobalTempView("gemp")
+
+// 查询员工姓名及工作，使用限定名称进行引用
+spark.sql("SELECT ename,job FROM global_temp.gemp").show()
+
+// 查询工资大于2000的员工信息，使用限定名称进行引用
+spark.sql("SELECT * FROM global_temp.gemp where sal > 2000").show()
+
+// 分组统计部门人数，使用限定名称进行引用
+spark.sql("SELECT deptno,count(ename) FROM global_temp.gemp  group by deptno").show()
+```
+
+## 三、创建Datasets
+
+### 3.1 由外部数据集创建
+
+```scala
+// 1.需要导入隐式转换
+import spark.implicits._
+
+// 2.创建case class,等价于Java Bean
+case class Emp(ename: String, comm: Double, deptno: Long, empno: Long, 
+               hiredate: String, job: String, mgr: Long, sal: Double)
+
+// 3.由外部数据集创建Datasets
+val ds = spark.read.json("/usr/file/emp.json").as[Emp]
+ds.show()
+```
+
+### 3.2 由内部数据集创建
+
+```scala
+// 1.需要导入隐式转换
+import spark.implicits._
+
+// 2.创建case class,等价于Java Bean
+case class Emp(ename: String, comm: Double, deptno: Long, empno: Long, 
+               hiredate: String, job: String, mgr: Long, sal: Double)
+
+// 3.由内部数据集创建Datasets
+val caseClassDS = Seq(Emp("ALLEN", 300.0, 30, 7499, "1981-02-20 00:00:00", "SALESMAN", 7698, 1600.0),
+                      Emp("JONES", 300.0, 30, 7499, "1981-02-20 00:00:00", "SALESMAN", 7698, 1600.0))
+                    .toDS()
+caseClassDS.show()
+```
+
+
+
+## 四、DataFrames与Datasets互相转换
+
+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：
+
+```shell
+# DataFrames转Datasets
+scala> df.as[Emp]
+res1: org.apache.spark.sql.Dataset[Emp] = [COMM: double, DEPTNO: bigint ... 6 more fields]
+
+# Datasets转DataFrames
+scala> ds.toDF()
+res2: org.apache.spark.sql.DataFrame = [COMM: double, DEPTNO: bigint ... 6 more fields]
+```
+
+
+
+## 五、RDDs转换为DataFrames\Datasets
+
+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。
+
+### 5.1 使用反射推断
+
+```scala
+// 1.导入隐式转换
+import spark.implicits._
+
+// 2.创建部门类
+case class Dept(deptno: Long, dname: String, loc: String)
+
+// 3.创建RDD并转换为dataSet
+val rddToDS = spark.sparkContext
+  .textFile("/usr/file/dept.txt")
+  .map(_.split("\t"))
+  .map(line => Dept(line(0).trim.toLong, line(1), line(2)))
+  .toDS()  // 如果调用toDF()则转换为dataFrame 
+```
+
+### 5.2 以编程方式指定Schema
+
+```scala
+import org.apache.spark.sql.Row
+import org.apache.spark.sql.types._
+
+
+// 1.定义每个列的列类型
+val fields = Array(StructField("deptno", LongType, nullable = true),
+                   StructField("dname", StringType, nullable = true),
+                   StructField("loc", StringType, nullable = true))
+
+// 2.创建schema
+val schema = StructType(fields)
+
+// 3.创建RDD
+val deptRDD = spark.sparkContext.textFile("/usr/file/dept.txt")
+val rowRDD = deptRDD.map(_.split("\t")).map(line => Row(line(0).toLong, line(1), line(2)))
+
+
+// 4.将RDD转换为dataFrame
+val deptDF = spark.createDataFrame(rowRDD, schema)
+deptDF.show()
+```
+
+## 六、使用自定义聚合函数
+
+Scala提供了两种自定义聚合函数的方法，分别如下：
+
++ 有类型的自定义聚合函数，主要适用于DataSets；
++ 无类型的自定义聚合函数，主要适用于DataFrames。
+
+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：
+
+### 6.1 有类型的自定义函数
+
+```scala
+import org.apache.spark.sql.expressions.Aggregator
+import org.apache.spark.sql.{Encoder, Encoders, SparkSession, functions}
+
+// 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
+case class Emp(ename: String, comm: scala.Option[Double], deptno: Long, empno: Long,
+               hiredate: String, job: String, mgr: scala.Option[Long], sal: Double)
+
+// 2.定义聚合操作的中间输出类型
+case class SumAndCount(var sum: Double, var count: Long)
+
+/* 3.自定义聚合函数
+ * @IN  聚合操作的输入类型
+ * @BUF reduction操作输出值的类型
+ * @OUT 聚合操作的输出类型
+ */
+object MyAverage extends Aggregator[Emp, SumAndCount, Double] {
+    
+    // 4.用于聚合操作的的初始零值
+    override def zero: SumAndCount = SumAndCount(0, 0)
+    
+    // 5.同一分区中的reduce操作
+    override def reduce(avg: SumAndCount, emp: Emp): SumAndCount = {
+        avg.sum += emp.sal
+        avg.count += 1
+        avg
+    }
+
+    // 6.不同分区中的merge操作
+    override def merge(avg1: SumAndCount, avg2: SumAndCount): SumAndCount = {
+        avg1.sum += avg2.sum
+        avg1.count += avg2.count
+        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：
+
+![spark-sql-自定义函数](D:\BigData-Notes\pictures\spark-sql-自定义函数.png)
+
+
+
+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)
\ No newline at end of file
diff --git a/pictures/spark-scheme.png b/pictures/spark-scheme.png
new file mode 100644
index 0000000..a4b8dbd
Binary files /dev/null and b/pictures/spark-scheme.png differ
diff --git a/pictures/spark-sql-shell.png b/pictures/spark-sql-shell.png
new file mode 100644
index 0000000..dc31c06
Binary files /dev/null and b/pictures/spark-sql-shell.png differ
diff --git a/pictures/spark-sql-自定义函数.png b/pictures/spark-sql-自定义函数.png
new file mode 100644
index 0000000..abb1775
Binary files /dev/null and b/pictures/spark-sql-自定义函数.png differ
diff --git a/resources/dept.json b/resources/dept.json
new file mode 100644
index 0000000..7a50976
--- /dev/null
+++ b/resources/dept.json
@@ -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"}
\ No newline at end of file
diff --git a/resources/emp.json b/resources/emp.json
new file mode 100644
index 0000000..03af1f5
--- /dev/null
+++ b/resources/emp.json
@@ -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}
\ No newline at end of file