Hadoop教程
|NO.Z.00044|——————————|BigDataEnd|——|Hadoop&Spark.V05|------------------------------------------|Spa
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[BigDataHadoop：Hadoop&Spark.V05] [BigDataHadoop.Spark内存级快速计算引擎][|章节四|Hadoop|spark|spark sql：spark sql编程&Transformation操作|]
一、Transformation 操作
### --- select * from tab where ... group by ... having... order by... # --- 1、RDD类似的操作持久化 ~~~ 缓存与checkpoint ~~~ select ~~~ where ~~~ group by / 聚合 ~~~ order by ~~~ join ~~~ 集合操作 ~~~ 空值操作(函数) ~~~ 函数
### --- 2、与RDD类似的操作 map、filter、flatMap、mapPartitions、sample、 randomSplit、 limit、distinct、dropDuplicates、describe
scala> df1.map(row=>row.getAs[Int](0)).show +-----+ |value| +-----+ | 7369| | 7499| | 7521| | 7566| | 7654| | 7698| | 7782| | 7788| | 7839| | 7844| | 7876| | 7900| | 7902| | 7934| +-----+
~~~ # randomSplit(与RDD类似，将DF、DS按给定参数分成多份) scala> val df2 = df1.randomSplit(Array(0.5, 0.6, 0.7)) df2: Array[org.apache.spark.sql.Dataset[org.apache.spark.sql.Row]] = Array([EMPNO: int, ENAME: string ... 6 more fields], [EMPNO: int, ENAME: string ... 6 more fields], [EMPNO: int, ENAME: string ... 6 more fields]) scala> df2(0).count res76: Long = 2 scala> df2(1).count res77: Long = 4 scala> df2(2).count res78: Long = 8
~~~ # 取10行数据生成新的DataSet scala> val df2 = df1.limit(10) df2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [EMPNO: int, ENAME: string ... 6 more fields]
~~~ # distinct，去重 scala> val df2 = df1.union(df1) df2: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [EMPNO: int, ENAME: string ... 6 more fields] scala> df2.distinct.count res79: Long = 14
~~~ # dropDuplicates，按列值去重
scala> df2.dropDuplicates.show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
+-----+------+---------+----+-------------------+----+----+------+
scala> df2.dropDuplicates("mgr", "deptno").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df2.dropDuplicates("mgr").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df2.dropDuplicates("deptno").show
+-----+-----+--------+----+-------------------+----+----+------+
|EMPNO|ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+-----+--------+----+-------------------+----+----+------+
| 7369|SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7782|CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499|ALLEN|SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
+-----+-----+--------+----+-------------------+----+----+------+~~~ # 返回全部列的统计（count、mean、stddev、min、max） scala> ds1.describe().show +-------+----+----------------+------------------+ |summary|name| age| height| +-------+----+----------------+------------------+ | count| 3| 3| 3| | mean|null| 18.0|164.33333333333334| | stddev|null|9.16515138991168|20.008331597945226| | min|Andy| 10| 144| | max| Tom| 28| 184| +-------+----+----------------+------------------+
~~~ # 返回指定列的统计
scala> ds1.describe("*").show
+-------+----+----------------+------------------+
|summary|name| age| height|
+-------+----+----------------+------------------+
| count| 3| 3| 3|
| mean|null| 18.0|164.33333333333334|
| stddev|null|9.16515138991168|20.008331597945226|
| min|Andy| 10| 144|
| max| Tom| 28| 184|
+-------+----+----------------+------------------+### --- 3、存储相关 ~~~ cacheTable、persist、checkpoint、unpersist、cache ~~~ 备注：Dataset 默认的存储级别是 MEMORY_AND_DISK
scala> import org.apache.spark.storage.StorageLevel
import org.apache.spark.storage.StorageLevel
scala> spark.sparkContext.setCheckpointDir("hdfs://hadoop01:9000/checkpoint")scala> df1.show() +-----+------+---------+----+-------------------+----+----+------+ |EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO| +-----+------+---------+----+-------------------+----+----+------+ | 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20| | 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30| | 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30| | 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20| | 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30| | 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30| | 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10| | 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20| | 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10| | 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30| | 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20| | 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30| | 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20| | 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10| +-----+------+---------+----+-------------------+----+----+------+
scala> df1.checkpoint()
res36: org.apache.spark.sql.Dataset[org.apache.spark.sql.Row] = [EMPNO: int, ENAME: string ... 6 more fields]
scala> df1.cache()
res37: df1.type = [EMPNO: int, ENAME: string ... 6 more fields]
scala> df1.persist(StorageLevel.MEMORY_ONLY)
21/10/20 15:45:46 WARN CacheManager: Asked to cache already cached data.
res38: df1.type = [EMPNO: int, ENAME: string ... 6 more fields]
scala> df1.count()
res39: Long = 14
scala> df1.unpersist(true)
res40: df1.type = [EMPNO: int, ENAME: string ... 6 more fields]
scala> df1.createOrReplaceTempView("t1")
scala> spark.catalog.cacheTable("t1")
scala> spark.catalog.uncacheTable("t1")### --- 4、select相关 ~~~ 列的多种表示、select、selectExpr ~~~ drop、withColumn、withColumnRenamed、cast（内置函数）
~~~ # 列的多种表示方法。使用""、$""、'、col()、ds("")
~~~ # 注意：不要混用；必要时使用spark.implicitis._；并非每个表示在所有的地方都有效
scala> df1.select($"ename", $"hiredate", $"sal").show
+------+-------------------+----+
| ename| hiredate| sal|
+------+-------------------+----+
| SMITH|2001-01-02 22:12:13| 800|
| ALLEN|2002-01-02 22:12:13|1600|
| WARD|2003-01-02 22:12:13|1250|
| JONES|2004-01-02 22:12:13|2975|
|MARTIN|2005-01-02 22:12:13|1250|
| BLAKE|2005-04-02 22:12:13|2850|
| CLARK|2006-03-02 22:12:13|2450|
| SCOTT|2007-03-02 22:12:13|3000|
| KING|2006-03-02 22:12:13|5000|
|TURNER|2009-07-02 22:12:13|1500|
| ADAMS|2010-05-02 22:12:13|1100|
| JAMES|2011-06-02 22:12:13| 950|
| FORD|2011-07-02 22:12:13|3000|
|MILLER|2012-11-02 22:12:13|1300|
+------+-------------------+----+
scala> df1.select("ename", "hiredate", "sal").show
+------+-------------------+----+
| ename| hiredate| sal|
+------+-------------------+----+
| SMITH|2001-01-02 22:12:13| 800|
| ALLEN|2002-01-02 22:12:13|1600|
| WARD|2003-01-02 22:12:13|1250|
| JONES|2004-01-02 22:12:13|2975|
|MARTIN|2005-01-02 22:12:13|1250|
| BLAKE|2005-04-02 22:12:13|2850|
| CLARK|2006-03-02 22:12:13|2450|
| SCOTT|2007-03-02 22:12:13|3000|
| KING|2006-03-02 22:12:13|5000|
|TURNER|2009-07-02 22:12:13|1500|
| ADAMS|2010-05-02 22:12:13|1100|
| JAMES|2011-06-02 22:12:13| 950|
| FORD|2011-07-02 22:12:13|3000|
|MILLER|2012-11-02 22:12:13|1300|
+------+-------------------+----+
scala> df1.select('ename, 'hiredate, 'sal).show
+------+-------------------+----+
| ename| hiredate| sal|
+------+-------------------+----+
| SMITH|2001-01-02 22:12:13| 800|
| ALLEN|2002-01-02 22:12:13|1600|
| WARD|2003-01-02 22:12:13|1250|
| JONES|2004-01-02 22:12:13|2975|
|MARTIN|2005-01-02 22:12:13|1250|
| BLAKE|2005-04-02 22:12:13|2850|
| CLARK|2006-03-02 22:12:13|2450|
| SCOTT|2007-03-02 22:12:13|3000|
| KING|2006-03-02 22:12:13|5000|
|TURNER|2009-07-02 22:12:13|1500|
| ADAMS|2010-05-02 22:12:13|1100|
| JAMES|2011-06-02 22:12:13| 950|
| FORD|2011-07-02 22:12:13|3000|
|MILLER|2012-11-02 22:12:13|1300|
+------+-------------------+----+
scala> df1.select(col("ename"), col("hiredate"), col("sal")).show
+------+-------------------+----+
| ename| hiredate| sal|
+------+-------------------+----+
| SMITH|2001-01-02 22:12:13| 800|
| ALLEN|2002-01-02 22:12:13|1600|
| WARD|2003-01-02 22:12:13|1250|
| JONES|2004-01-02 22:12:13|2975|
|MARTIN|2005-01-02 22:12:13|1250|
| BLAKE|2005-04-02 22:12:13|2850|
| CLARK|2006-03-02 22:12:13|2450|
| SCOTT|2007-03-02 22:12:13|3000|
| KING|2006-03-02 22:12:13|5000|
|TURNER|2009-07-02 22:12:13|1500|
| ADAMS|2010-05-02 22:12:13|1100|
| JAMES|2011-06-02 22:12:13| 950|
| FORD|2011-07-02 22:12:13|3000|
|MILLER|2012-11-02 22:12:13|1300|
+------+-------------------+----+
scala> df1.select(df1("ename"), df1("hiredate"), df1("sal")).show
+------+-------------------+----+
| ename| hiredate| sal|
+------+-------------------+----+
| SMITH|2001-01-02 22:12:13| 800|
| ALLEN|2002-01-02 22:12:13|1600|
| WARD|2003-01-02 22:12:13|1250|
| JONES|2004-01-02 22:12:13|2975|
|MARTIN|2005-01-02 22:12:13|1250|
| BLAKE|2005-04-02 22:12:13|2850|
| CLARK|2006-03-02 22:12:13|2450|
| SCOTT|2007-03-02 22:12:13|3000|
| KING|2006-03-02 22:12:13|5000|
|TURNER|2009-07-02 22:12:13|1500|
| ADAMS|2010-05-02 22:12:13|1100|
| JAMES|2011-06-02 22:12:13| 950|
| FORD|2011-07-02 22:12:13|3000|
|MILLER|2012-11-02 22:12:13|1300|
+------+-------------------+----+~~~ # 下面的写法无效，其他列的表示法有效
scala> df1.select("ename", "hiredate", "sal"+100).show
scala> df1.select("ename", "hiredate", "sal+100").show~~~ # 这样写才符合语法
scala> df1.select($"ename", $"hiredate", $"sal"+100).show
+------+-------------------+-----------+
| ename| hiredate|(sal + 100)|
+------+-------------------+-----------+
| SMITH|2001-01-02 22:12:13| 900|
| ALLEN|2002-01-02 22:12:13| 1700|
| WARD|2003-01-02 22:12:13| 1350|
| JONES|2004-01-02 22:12:13| 3075|
|MARTIN|2005-01-02 22:12:13| 1350|
| BLAKE|2005-04-02 22:12:13| 2950|
| CLARK|2006-03-02 22:12:13| 2550|
| SCOTT|2007-03-02 22:12:13| 3100|
| KING|2006-03-02 22:12:13| 5100|
|TURNER|2009-07-02 22:12:13| 1600|
| ADAMS|2010-05-02 22:12:13| 1200|
| JAMES|2011-06-02 22:12:13| 1050|
| FORD|2011-07-02 22:12:13| 3100|
|MILLER|2012-11-02 22:12:13| 1400|
+------+-------------------+-----------+
scala> df1.select('ename, 'hiredate, 'sal+100).show
+------+-------------------+-----------+
| ename| hiredate|(sal + 100)|
+------+-------------------+-----------+
| SMITH|2001-01-02 22:12:13| 900|
| ALLEN|2002-01-02 22:12:13| 1700|
| WARD|2003-01-02 22:12:13| 1350|
| JONES|2004-01-02 22:12:13| 3075|
|MARTIN|2005-01-02 22:12:13| 1350|
| BLAKE|2005-04-02 22:12:13| 2950|
| CLARK|2006-03-02 22:12:13| 2550|
| SCOTT|2007-03-02 22:12:13| 3100|
| KING|2006-03-02 22:12:13| 5100|
|TURNER|2009-07-02 22:12:13| 1600|
| ADAMS|2010-05-02 22:12:13| 1200|
| JAMES|2011-06-02 22:12:13| 1050|
| FORD|2011-07-02 22:12:13| 3100|
|MILLER|2012-11-02 22:12:13| 1400|
+------+-------------------+-----------+~~~ # 可使用expr表达式(expr里面只能使用引号)
scala> df1.select(expr("comm+100"), expr("sal+100"), expr("ename")).show
+------------+-----------+------+
|(comm + 100)|(sal + 100)| ename|
+------------+-----------+------+
| null| 900| SMITH|
| 400| 1700| ALLEN|
| 600| 1350| WARD|
| null| 3075| JONES|
| 1500| 1350|MARTIN|
| null| 2950| BLAKE|
| null| 2550| CLARK|
| null| 3100| SCOTT|
| null| 5100| KING|
| 100| 1600|TURNER|
| null| 1200| ADAMS|
| null| 1050| JAMES|
| null| 3100| FORD|
| null| 1400|MILLER|
+------------+-----------+------+
scala> df1.selectExpr("ename as name").show
+------+
| name|
+------+
| SMITH|
| ALLEN|
| WARD|
| JONES|
|MARTIN|
| BLAKE|
| CLARK|
| SCOTT|
| KING|
|TURNER|
| ADAMS|
| JAMES|
| FORD|
|MILLER|
+------+
scala> df1.selectExpr("power(sal, 2)", "sal").show
+---------------------------------------------+----+
|POWER(CAST(sal AS DOUBLE), CAST(2 AS DOUBLE))| sal|
+---------------------------------------------+----+
| 640000.0| 800|
| 2560000.0|1600|
| 1562500.0|1250|
| 8850625.0|2975|
| 1562500.0|1250|
| 8122500.0|2850|
| 6002500.0|2450|
| 9000000.0|3000|
| 2.5E7|5000|
| 2250000.0|1500|
| 1210000.0|1100|
| 902500.0| 950|
| 9000000.0|3000|
| 1690000.0|1300|
+---------------------------------------------+----+
scala> df1.selectExpr("round(sal, -3) as newsal", "sal", "ename").show
+------+----+------+
|newsal| sal| ename|
+------+----+------+
| 1000| 800| SMITH|
| 2000|1600| ALLEN|
| 1000|1250| WARD|
| 3000|2975| JONES|
| 1000|1250|MARTIN|
| 3000|2850| BLAKE|
| 2000|2450| CLARK|
| 3000|3000| SCOTT|
| 5000|5000| KING|
| 2000|1500|TURNER|
| 1000|1100| ADAMS|
| 1000| 950| JAMES|
| 3000|3000| FORD|
| 1000|1300|MILLER|
+------+----+------+~~~ # drop、withColumn、 withColumnRenamed、casting
~~~ # drop 删除一个或多个列，得到新的DF
scala> df1.drop("mgr")
res42: org.apache.spark.sql.DataFrame = [EMPNO: int, ENAME: string ... 5 more fields]
scala> df1.drop("empno", "mgr")
res43: org.apache.spark.sql.DataFrame = [ENAME: string, JOB: string ... 4 more fields]~~~ # withColumn，修改列值
scala> val df2 = df1.withColumn("sal", $"sal"+1000)
df2: org.apache.spark.sql.DataFrame = [EMPNO: int, ENAME: string ... 6 more fields]
scala> df2.show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| sal|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13|1800|null| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|2600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|2250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|3975|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|2250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|3850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|3450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|4000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|6000|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|2500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|2100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13|1950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|4000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|2300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+~~~ # withColumnRenamed，更改列名
~~~ 备注：drop、withColumn、withColumnRenamed返回的是DF
scala> df1.withColumnRenamed("sal", "newsal")
res45: org.apache.spark.sql.DataFrame = [EMPNO: int, ENAME: string ... 6 more fields]~~~ # cast，类型转换
scala> df1.selectExpr("cast(empno as string)").printSchema
root
|-- empno: string (nullable = true)
scala> import org.apache.spark.sql.types._
import org.apache.spark.sql.types._
scala> df1.select('empno.cast(StringType)).printSchema
root
|-- empno: string (nullable = true)### --- 5、where相关 ~~~ where == filter
~~~ # where操作
scala> df1.filter("sal>1000").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.filter("sal>1000 and job=='MANAGER'").show
+-----+-----+-------+----+-------------------+----+----+------+
|EMPNO|ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+-----+-------+----+-------------------+----+----+------+
| 7566|JONES|MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698|BLAKE|MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782|CLARK|MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
+-----+-----+-------+----+-------------------+----+----+------+~~~ # filter操作
scala> df1.where("sal>1000").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.where("sal>1000 and job=='MANAGER'").show
+-----+-----+-------+----+-------------------+----+----+------+
|EMPNO|ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+-----+-------+----+-------------------+----+----+------+
| 7566|JONES|MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698|BLAKE|MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782|CLARK|MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
+-----+-----+-------+----+-------------------+----+----+------+### --- 6、groupBy相关 ~~~ groupBy、agg、max、min、avg、sum、count（后面5个为内置函数）
~~~ # groupBy、max、min、mean、sum1 、count（与df1.count不同）
scala> df1.groupBy("Job").sum("sal").show
+---------+--------+
| Job|sum(sal)|
+---------+--------+
| ANALYST| 6000|
| SALESMAN| 5600|
| CLERK| 4150|
| MANAGER| 8275|
|PRESIDENT| 5000|
+---------+--------+
scala> df1.groupBy("Job").max("sal").show
+---------+--------+
| Job|max(sal)|
+---------+--------+
| ANALYST| 3000|
| SALESMAN| 1600|
| CLERK| 1300|
| MANAGER| 2975|
|PRESIDENT| 5000|
+---------+--------+
scala> df1.groupBy("Job").min("sal").show
+---------+--------+
| Job|min(sal)|
+---------+--------+
| ANALYST| 3000|
| SALESMAN| 1250|
| CLERK| 800|
| MANAGER| 2450|
|PRESIDENT| 5000|
+---------+--------+
scala> df1.groupBy("Job").avg("sal").show
+---------+------------------+
| Job| avg(sal)|
+---------+------------------+
| ANALYST| 3000.0|
| SALESMAN| 1400.0|
| CLERK| 1037.5|
| MANAGER|2758.3333333333335|
|PRESIDENT| 5000.0|
+---------+------------------+
scala> df1.groupBy("Job").count.show
+---------+-----+
| Job|count|
+---------+-----+
| ANALYST| 2|
| SALESMAN| 4|
| CLERK| 4|
| MANAGER| 3|
|PRESIDENT| 1|
+---------+-----+~~~ # 类似having子句
scala> df1.groupBy("Job").avg("sal").where("avg(sal) > 2000").show
+---------+------------------+
| Job| avg(sal)|
+---------+------------------+
| ANALYST| 3000.0|
| MANAGER|2758.3333333333335|
|PRESIDENT| 5000.0|
+---------+------------------+
scala> df1.groupBy("Job").avg("sal").where($"avg(sal)" > 2000).show
+---------+------------------+
| Job| avg(sal)|
+---------+------------------+
| ANALYST| 3000.0|
| MANAGER|2758.3333333333335|
|PRESIDENT| 5000.0|
+---------+------------------+~~~ # agg
scala> df1.groupBy("Job").agg("sal"->"max", "sal"->"min", "sal"->"avg", "sal"->"sum", "sal"->"count").show
+---------+--------+--------+------------------+--------+----------+
| Job|max(sal)|min(sal)| avg(sal)|sum(sal)|count(sal)|
+---------+--------+--------+------------------+--------+----------+
| ANALYST| 3000| 3000| 3000.0| 6000| 2|
| SALESMAN| 1600| 1250| 1400.0| 5600| 4|
| CLERK| 1300| 800| 1037.5| 4150| 4|
| MANAGER| 2975| 2450|2758.3333333333335| 8275| 3|
|PRESIDENT| 5000| 5000| 5000.0| 5000| 1|
+---------+--------+--------+------------------+--------+----------+
scala> df1.groupBy("deptno").agg("sal"->"max", "sal"->"min", "sal"->"avg", "sal"->"sum", "sal"->"count").show
+------+--------+--------+------------------+--------+----------+
|deptno|max(sal)|min(sal)| avg(sal)|sum(sal)|count(sal)|
+------+--------+--------+------------------+--------+----------+
| 20| 3000| 800| 2175.0| 10875| 5|
| 10| 5000| 1300|2916.6666666666665| 8750| 3|
| 30| 2850| 950|1566.6666666666667| 9400| 6|
+------+--------+--------+------------------+--------+----------+~~~ # 这种方式更好理解
scala> df1.groupBy("Job").agg(max("sal"), min("sal"), avg("sal"), sum("sal"), count("sal")).show
+---------+--------+--------+------------------+--------+----------+
| Job|max(sal)|min(sal)| avg(sal)|sum(sal)|count(sal)|
+---------+--------+--------+------------------+--------+----------+
| ANALYST| 3000| 3000| 3000.0| 6000| 2|
| SALESMAN| 1600| 1250| 1400.0| 5600| 4|
| CLERK| 1300| 800| 1037.5| 4150| 4|
| MANAGER| 2975| 2450|2758.3333333333335| 8275| 3|
|PRESIDENT| 5000| 5000| 5000.0| 5000| 1|
+---------+--------+--------+------------------+--------+----------+~~~ # 给列取别名
scala> df1.groupBy("Job").agg(max("sal"), min("sal"), avg("sal"),
| sum("sal"), count("sal")).withColumnRenamed("min(sal)",
| "min1").show
+---------+--------+----+------------------+--------+----------+
| Job|max(sal)|min1| avg(sal)|sum(sal)|count(sal)|
+---------+--------+----+------------------+--------+----------+
| ANALYST| 3000|3000| 3000.0| 6000| 2|
| SALESMAN| 1600|1250| 1400.0| 5600| 4|
| CLERK| 1300| 800| 1037.5| 4150| 4|
| MANAGER| 2975|2450|2758.3333333333335| 8275| 3|
|PRESIDENT| 5000|5000| 5000.0| 5000| 1|
+---------+--------+----+------------------+--------+----------+~~~ # 给列取别名，最简便
scala> df1.groupBy("Job").agg(max("sal").as("max1"),
| min("sal").as("min2"), avg("sal").as("avg3"),
| sum("sal").as("sum4"), count("sal").as("count5")).show
+---------+----+----+------------------+----+------+
| Job|max1|min2| avg3|sum4|count5|
+---------+----+----+------------------+----+------+
| ANALYST|3000|3000| 3000.0|6000| 2|
| SALESMAN|1600|1250| 1400.0|5600| 4|
| CLERK|1300| 800| 1037.5|4150| 4|
| MANAGER|2975|2450|2758.3333333333335|8275| 3|
|PRESIDENT|5000|5000| 5000.0|5000| 1|
+---------+----+----+------------------+----+------+### --- 7、orderBy相关 ~~~ orderBy == sort
~~~ # orderBy
scala> df1.orderBy("sal").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy($"sal").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy($"sal".asc).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+~~~ # 降序
scala> df1.orderBy(-$"sal").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy('sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy(col("sal")).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy(df1("sal")).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy($"sal".desc).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy(-'sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.orderBy(-'deptno, -'sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+~~~ # sort，以下语句等价
scala> df1.sort("sal").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort($"sal").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort($"sal".asc).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort('sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort(col("sal")).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort(df1("sal")).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort($"sal".desc).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort(-'sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.sort(-'deptno, -'sal).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+### --- 8、join相关 ~~~ # 1、笛卡尔积 df1.crossJoin(df1).count
~~~ # 2、等值连接（单字段）（连接字段empno，仅显示了一次） df1.join(df1, "empno").count
~~~ # 3、等值连接（多字段）（连接字段empno、ename，仅显示了一次）
df1.join(df1, Seq("empno", "ename")).show~~~ # 定义第一个数据集 case class StudentAge(sno: Int, name: String, age: Int) val lst = List(StudentAge(1,"Alice", 18), StudentAge(2,"Andy", 19), StudentAge(3,"Bob", 17), StudentAge(4,"Justin", 21), StudentAge(5,"Cindy", 20)) val ds1 = spark.createDataset(lst) ds1.show()
~~~ # 定义第二个数据集
case class StudentHeight(sname: String, height: Int)
val rdd = sc.makeRDD(List(StudentHeight("Alice", 160),
StudentHeight("Andy", 159), StudentHeight("Bob", 170),
StudentHeight("Cindy", 165), StudentHeight("Rose", 160)))
val ds2 = rdd.toDS~~~ # 备注：不能使用双引号，而且这里是 ===
ds1.join(ds2, $"name"===$"sname").show
ds1.join(ds2, 'name==='sname).show
ds1.join(ds2, ds1("name")===ds2("sname")).show
ds1.join(ds2, ds1("sname")===ds2("sname"), "inner").show~~~ # 多种连接方式 ds1.join(ds2, $"name"===$"sname").show ds1.join(ds2, $"name"===$"sname", "inner").show ds1.join(ds2, $"name"===$"sname", "left").show ds1.join(ds2, $"name"===$"sname", "left_outer").show ds1.join(ds2, $"name"===$"sname", "right").show ds1.join(ds2, $"name"===$"sname", "right_outer").show ds1.join(ds2, $"name"===$"sname", "outer").show ds1.join(ds2, $"name"===$"sname", "full").show ds1.join(ds2, $"name"===$"sname", "full_outer").show ~~~ # 备注：DS在join操作之后变成了DF
### --- 9、集合相关 ~~~ union==unionAll（过期）、intersect、except
~~~ # union、unionAll、intersect、except。集合的交、并、差
scala> val ds3 = ds1.select("name")
scala> val ds4 = ds2.select("sname")~~~ # union 求并集，不去重 scala> ds3.union(ds4).show
~~~ # unionAll、union 等价；unionAll过期方法，不建议使用 scala> ds3.unionAll(ds4).show
~~~ # intersect 求交 scala> ds3.intersect(ds4).show
~~~ # except 求差 scala> ds3.except(ds4).show
### --- 10、空值处理 ~~~ na.fill、na.drop
~~~ # NaN (Not a Number) scala> math.sqrt(-1.0) res90: Double = NaN scala> math.sqrt(-1.0).isNaN() res91: Boolean = true scala> df1.show +-----+------+---------+----+-------------------+----+----+------+ |EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO| +-----+------+---------+----+-------------------+----+----+------+ | 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20| | 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30| | 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30| | 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20| | 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30| | 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30| | 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10| | 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20| | 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10| | 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30| | 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20| | 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30| | 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20| | 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10| +-----+------+---------+----+-------------------+----+----+------+
~~~ # 删除所有列的空值和NaN scala> df1.na.drop.show +-----+------+--------+----+-------------------+----+----+------+ |EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO| +-----+------+--------+----+-------------------+----+----+------+ | 7499| ALLEN|SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30| | 7521| WARD|SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30| | 7654|MARTIN|SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30| | 7844|TURNER|SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30| +-----+------+--------+----+-------------------+----+----+------+
~~~ # 删除某列的空值和NaN
scala> df1.na.drop(Array("mgr")).show
+-----+------+--------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+--------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7499| ALLEN|SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD|SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7654|MARTIN|SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7844|TURNER|SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+--------+----+-------------------+----+----+------+~~~ # 对全部列填充；对指定单列填充；对指定多列填充
scala> df1.na.fill(1000).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|1000| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|1000| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|1000| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|1000| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|1000| 20|
| 7839| KING|PRESIDENT|1000|2006-03-02 22:12:13|5000|1000| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|1000| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|1000| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|1000| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|1000| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.na.fill(1000, Array("comm")).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|1000| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|1000| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|1000| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|1000| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|1000| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|1000| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|1000| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|1000| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|1000| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|1000| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.na.fill(Map("mgr"->2000, "comm"->1000)).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|1000| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|1000| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|1000| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|1000| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|1000| 20|
| 7839| KING|PRESIDENT|2000|2006-03-02 22:12:13|5000|1000| 10|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|1000| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|1000| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|1000| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|1000| 10|
+-----+------+---------+----+-------------------+----+----+------+~~~ # 对指定的值进行替换
scala> df1.na.replace("comm" :: "deptno" :: Nil, Map(0 -> 100, 10 -> 100)).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7499| ALLEN| SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD| SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7654|MARTIN| SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 100|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 100|
| 7844|TURNER| SALESMAN|7698|2009-07-02 22:12:13|1500| 100| 30|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 100|
+-----+------+---------+----+-------------------+----+----+------+~~~ # 查询空值列或非空值列。isNull、isNotNull为内置函数
scala> df1.filter("comm is null").show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.filter($"comm".isNull).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.filter(col("comm").isNull).show
+-----+------+---------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+---------+----+-------------------+----+----+------+
| 7369| SMITH| CLERK|7902|2001-01-02 22:12:13| 800|null| 20|
| 7566| JONES| MANAGER|7839|2004-01-02 22:12:13|2975|null| 20|
| 7698| BLAKE| MANAGER|7839|2005-04-02 22:12:13|2850|null| 30|
| 7782| CLARK| MANAGER|7839|2006-03-02 22:12:13|2450|null| 10|
| 7788| SCOTT| ANALYST|7566|2007-03-02 22:12:13|3000|null| 20|
| 7839| KING|PRESIDENT|null|2006-03-02 22:12:13|5000|null| 10|
| 7876| ADAMS| CLERK|7788|2010-05-02 22:12:13|1100|null| 20|
| 7900| JAMES| CLERK|7698|2011-06-02 22:12:13| 950|null| 30|
| 7902| FORD| ANALYST|7566|2011-07-02 22:12:13|3000|null| 20|
| 7934|MILLER| CLERK|7782|2012-11-02 22:12:13|1300|null| 10|
+-----+------+---------+----+-------------------+----+----+------+
scala> df1.filter("comm is not null").show
+-----+------+--------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+--------+----+-------------------+----+----+------+
| 7499| ALLEN|SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD|SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7654|MARTIN|SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7844|TURNER|SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
+-----+------+--------+----+-------------------+----+----+------+
scala> df1.filter(col("comm").isNotNull).show
+-----+------+--------+----+-------------------+----+----+------+
|EMPNO| ENAME| JOB| MGR| HIREDATE| SAL|COMM|DEPTNO|
+-----+------+--------+----+-------------------+----+----+------+
| 7499| ALLEN|SALESMAN|7698|2002-01-02 22:12:13|1600| 300| 30|
| 7521| WARD|SALESMAN|7698|2003-01-02 22:12:13|1250| 500| 30|
| 7654|MARTIN|SALESMAN|7698|2005-01-02 22:12:13|1250|1400| 30|
| 7844|TURNER|SALESMAN|7698|2009-07-02 22:12:13|1500| 0| 30|
+-----+------+--------+----+-------------------+----+----+------+### --- 11、窗口函数 ~~~ 一般情况下窗口函数不用 DSL 处理，直接用SQL更方便 ~~~ 参考源码Window.scala、WindowSpec.scala（主要）
scala> import org.apache.spark.sql.expressions.Window
import org.apache.spark.sql.expressions.Window
scala> val w1 = Window.partitionBy("cookieid").orderBy("createtime")
w1: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@32a97ef8
scala> val w2 = Window.partitionBy("cookieid").orderBy("pv")
w2: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@dac633
scala> val w3 = w1.rowsBetween(Window.unboundedPreceding,
| Window.currentRow)
w3: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@2b1e2939
scala> val w4 = w1.rowsBetween(-1, 1)
w4: org.apache.spark.sql.expressions.WindowSpec = org.apache.spark.sql.expressions.WindowSpec@217d9e78~~~ # 聚组函数【用分析函数的数据集】
scala> df.select($"cookieid", $"pv", sum("pv").over(w1).alias("pv1")).show
scala> df.select($"cookieid", $"pv", sum("pv").over(w3).alias("pv1")).show
scala> df.select($"cookieid", $"pv", sum("pv").over(w4).as("pv1")).show~~~ # 排名
scala> df.select($"cookieid", $"pv", rank().over(w2).alias("rank")).show
scala> df.select($"cookieid", $"pv", dense_rank().over(w2).alias("denserank")).show
scala> df.select($"cookieid", $"pv", row_number().over(w2).alias("rownumber")).show~~~ # lag、lead
scala> df.select($"cookieid", $"pv", lag("pv", 2).over(w2).alias("rownumber")).show
scala> df.select($"cookieid", $"pv", lag("pv", -2).over(w2).alias("rownumber")).show### --- 12、内建函数 ~~~ http://spark.apache.org/docs/latest/api/sql/index.html
二、编程代码实现### --- 编程代码实现
package cn.yanqi.sparksql
import org.apache.spark.sql.{DataFrame, SparkSession}
import org.apache.spark.sql.functions._
object TransformationDemo {
def main(args: Array[String]): Unit = {
val spark = SparkSession
.builder()
.appName("Demo1")
.master("local[*]")
.getOrCreate()
val sc = spark.sparkContext
sc.setLogLevel("warn")
import spark.implicits._
val df1: DataFrame = spark.read
.option("header", "true")
.option("inferschema", "true")
.csv("data/emp.dat")
// df1.printSchema()
// df1.map(row=>row.getAs[Int](0)).show
// // randomSplit(与RDD类似，将DF、DS按给定参数分成多份)
// val Array(dfx, dfy, dfz) = df1.randomSplit(Array(0.5, 0.6, 0.7))
// dfx.count
// dfy.count
// dfz.count
//
// // 取10行数据生成新的DataSet
// val df2 = df1.limit(10)
//
// // distinct，去重
// val df3 = df1.union(df1)
// df3.distinct.count
//
// // dropDuplicates，按列值去重
// df2.dropDuplicates.show
// df2.dropDuplicates("mgr", "deptno").show
// df2.dropDuplicates("mgr").show
// df2.dropDuplicates("deptno").show
//
// // 返回全部列的统计（count、mean、stddev、min、max）
// df1.describe().show
//
// // 返回指定列的统计
// df1.describe("sal").show
// df1.describe("sal", "comm").show
//
// df1.createOrReplaceTempView("t1")
// spark.sql("select * from t1").show
// spark.catalog.cacheTable("t1")
// spark.catalog.uncacheTable("t1")
import org.apache.spark.sql.functions._
df1.groupBy("Job").agg(min("sal").as("minsal"), max("sal").as("maxsal")).where($"minsal" > 2000).show
val lst = List(StudentAge(1,"Alice", 18), StudentAge(2,"Andy", 19), StudentAge(3,"Bob", 17), StudentAge(4,"Justin", 21), StudentAge(5,"Cindy", 20))
val ds1 = spark.createDataset(lst)
ds1.show()
// 定义第二个数据集
val rdd = sc.makeRDD(List(StudentHeight("Alice", 160), StudentHeight("Andy", 159), StudentHeight("Bob", 170), StudentHeight("Cindy", 165), StudentHeight("Rose", 160)))
val ds2 = rdd.toDS
spark.close()
}
}
case class StudentAge(sno: Int, sname: String, age: Int)
case class StudentHeight(sname: String, height: Int)### --- 编译打印 ~~~ # 准备数据文件：data/emp.dat
~~~ # 编译打印 D:\JAVA\jdk1.8.0_231\bin\java.exe "-javaagent:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\lib\idea_rt.jar=56671:D:\IntelliJIDEA\IntelliJ IDEA 2019.3.3\bin" -Dfile.encoding=UTF-8 -classpath 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cn.yanqi.sparksql.TransformationDemo Using Spark's default log4j profile: org/apache/spark/log4j-defaults.properties +---------+------+------+ | Job|minsal|maxsal| +---------+------+------+ | ANALYST| 3000| 3000| | MANAGER| 2450| 2975| |PRESIDENT| 5000| 5000| +---------+------+------+ +---+------+---+ |sno| sname|age| +---+------+---+ | 1| Alice| 18| | 2| Andy| 19| | 3| Bob| 17| | 4|Justin| 21| | 5| Cindy| 20| +---+------+---+ Process finished with exit code 0
===============================END===============================
Walter Savage Landor:strove with none,for none was worth my strife.Nature I loved and, next to Nature, Art:I warm'd both hands before the fire of life.It sinks, and I am ready to depart ——W.S.Landor
来自为知笔记(Wiz)
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