blog/BigData-Notes
2
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

modify

Browse Source
This commit is contained in:
罗祥
2019-05-21 13:33:50 +08:00
parent 388711f147
commit 656090f384
11 changed files with 444 additions and 59 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
code/spark/spark-core/file/emp.json Normal file
View 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}

65
code/spark/spark-core/src/main/java/rdd/scala/SparkSqlApp.scala Normal file
View File

@@ -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)
}
}

57
code/spark/spark-core/src/main/java/rdd/scala/SparkSqlTest.scala
View File

@@ -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)
}

2
code/spark/spark-core/src/main/java/rdd/scala/TransformationTest.scala
View File

@@ -67,7 +67,7 @@ class TransformationTest {
@Test @Test
def sample(): Unit = { def sample(): Unit = {
val list = List(1, 2, 3, 4, 5, 6) 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)
} }

2
notes/Spark-Transformation和Action.md
View File

@@ -144,7 +144,7 @@ sc.parallelize(list, 3).mapPartitionsWithIndex((index, iterator) => {
```scala ```scala
val list = List(1, 2, 3, 4, 5, 6) 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 ### 1.7 union

345
notes/SparkSQL-API基本使用.md Normal file
View File

@@ -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)

BIN
pictures/spark-scheme.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 13 KiB

BIN
pictures/spark-sql-shell.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 49 KiB

BIN
pictures/spark-sql-自定义函数.png Normal file
View File

Binary file not shown.
Side by Side

After

Width:  |  Height:  |  Size: 70 KiB

4
resources/dept.json Normal file
View 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
View 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}