Spark的Dataset操作(四)-其他单表操作
还有些杂七杂八的小用法没有提到，比如添加列，删除列，NA值处理之类的，就在这里大概列一下吧。
数据集还是之前的那个吧：
scala> val df = spark.createDataset(Seq(
("aaa",1,2),("bbb",3,4),("ccc",3,5),("bbb",4, 6)) ).toDF("key1","key2","key3")
df: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field]
scala> df.printSchema
root
|-- key1: string (nullable = true)
|-- key2: integer (nullable = false)
|-- key3: integer (nullable = false)
scala> df.show +----+----+----+
|key1|key2|key3| +----+----+----+
| aaa| 1| 2|
| bbb| 3| 4|
| ccc| 3| 5|
| bbb| 4| 6| +----+----+----+下面来添加一列，可以是字符串类型，整型；可以是常量或者是对当前已有的某列的变换，都行：
/*
新增字符串类型的列key_4，都初始化为new_str_col，注意这里的lit()函数 */ scala> val df_1 = df.withColumn("key4", lit("new_str_col"))
df_1: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 2 more fields] scala> df_1.printSchema root |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) scala> df_1.show +----+----+----+-----------+
|key1|key2|key3| key4| +----+----+----+-----------+
| aaa| 1| 2|new_str_col|
| bbb| 3| 4|new_str_col|
| ccc| 3| 5|new_str_col|
| bbb| 4| 6|new_str_col| +----+----+----+-----------+
/*
同样的，新增Int类型的列key5，都初始化为1024
*/ scala> val df_2 = df_1.withColumn("key5", lit(1024)) df_2: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 3 more fields] scala> df_2.printSchema root |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key5: integer (nullable = false) scala> df_2.show +----+----+----+-----------+-----+
|key1|key2|key3| key4|key5| +----+----+----+-----------+-----+
| aaa| 1| 2|new_str_col| 1024|
| bbb| 3| 4|new_str_col| 1024|
| ccc| 3| 5|new_str_col| 1024|
| bbb| 4| 6|new_str_col| 1024| +----+----+----+-----------+-----+
/*
再来个不是常量的新增列key6 = key5 * 2
*/ scala> val df_3 = df_2.withColumn("key6", $"key5"*2)
df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields] scala> df_3.show +----+----+----+-----------+----+----+
|key1|key2|key3| key4|key5|key6| +----+----+----+-----------+----+----+
| aaa| 1| 2|new_str_col|1024|2048|
| bbb| 3| 4|new_str_col|1024|2048|
| ccc| 3| 5|new_str_col|1024|2048|
| bbb| 4| 6|new_str_col|1024|2048| +----+----+----+-----------+----+----+
/*
这次是用的expr()函数
*/ scala> val df_4 = df_2.withColumn("key6", expr("key5 * 4"))
df_4: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 4 more fields] scala> df_4.show +----+----+----+-----------+----+----+
|key1|key2|key3| key4|key5|key6| +----+----+----+-----------+----+----+
| aaa| 1| 2|new_str_col|1024|4096|
| bbb| 3| 4|new_str_col|1024|4096|
| ccc| 3| 5|new_str_col|1024|4096|
| bbb| 4| 6|new_str_col|1024|4096| +----+----+----+-----------+----+----+删除列就比较简单了，指定列名就好了
/*
删除列key5
*/ scala> val df_5 = df_4.drop("key5") df_5: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 3 more fields] scala> df_4.printSchema root |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key5: integer (nullable = false) |-- key6: integer (nullable = false) scala> df_5.printSchema root |-- key1: string (nullable = true) |-- key2: integer (nullable = false) |-- key3: integer (nullable = false) |-- key4: string (nullable = false) |-- key6: integer (nullable = false) scala> df_5.show +----+----+----+-----------+----+
|key1|key2|key3| key4|key6| +----+----+----+-----------+----+
| aaa| 1| 2|new_str_col|4096|
| bbb| 3| 4|new_str_col|4096|
| ccc| 3| 5|new_str_col|4096|
| bbb| 4| 6|new_str_col|4096| +----+----+----+-----------+----+
/*
可以一次删除多列key4和key6
*/ scala> val df_6 = df_5.drop("key4", "key6") df_6: org.apache.spark.sql.DataFrame = [key1: string, key2: int ... 1 more field] /* 这里的columns函数以数组形式返回所有列名 */ scala> df_6.columns res23: Array[String] = Array(key1, key2, key3) scala> df_6.show +----+----+----+
|key1|key2|key3| +----+----+----+
| aaa| 1| 2|
| bbb| 3| 4|
| ccc| 3| 5|
| bbb| 4| 6| +----+----+----+再写几个null值等无效数据的一些处理吧
这次得换个数据集，null值的表用个csv文件导入，代码如下：
/*
csv文件内容如下：
key1,key2,key3,key4,key5
aaa,1,2,t1,4
bbb,5,3,t2,8
ccc,2,2,,7
,7,3,t1,
bbb,1,5,t3,0
,4,,t1,8
*/
scala> val df = spark.read.option("header","true").csv("natest.csv")
df: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
|null| 7| 3| t1|null|
| bbb| 1| 5| t3| 0|
| null| 4|null| t1| 8|
+----+----+----+----+----+
/*
把key1列中所有的null值替换成'xxx'
*/
scala> val df_2 = df.na.fill("xxx",Seq("key1"))
df_2: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df_2.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
| xxx| 7| 3| t1|null|
| bbb| 1| 5| t3| 0|
| xxx| 4|null| t1| 8|
+----+----+----+----+----+
/*
一次修改相同类型的多个列的示例。
这里是把key3,key5列中所有的null值替换成1024。
csv导入时默认是string，如果是整型，写法是一样的，有各个类型的重载。
*/
scala> val df_3 = df.na.fill("1024",Seq("key3","key5"))
df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df_3.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
|null| 7| 3| t1|1024|
| bbb| 1| 5| t3| 0|
|null| 4|1024| t1| 8|
+----+----+----+----+----+
/*
一次修改不同类型的多个列的示例。
csv导入时默认是string，如果是整型，写法是一样的，有各个类型的重载。
*/
scala> val df_3 = df.na.fill(Map(("key1"->"yyy"),("key3","1024"),("key4","t88"),("key5","4096")))
df_3: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df_3.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2| t88| 7|
| yyy| 7| 3| t1|4096|
| bbb| 1| 5| t3| 0|
| yyy| 4|1024| t1| 8|
+----+----+----+----+----+
/*
不修改，只是过滤掉含有null值的行。
这里是过滤掉key3,key5列中含有null的行
*/
scala> val df_4 = df.na.drop(Seq("key3","key5"))
df_4: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df_4.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
| bbb| 1| 5| t3| 0|
+----+----+----+----+----+
/*
过滤掉指定的若干列中，有效值少于n列的行
这里是过滤掉key1,key2,key3这3列中有效值小于2列的行。最后一行中，这3列有2列都是null，所以被过滤掉了。
*/
scala> val df_5 = df.na.drop(2,Seq("key1","key2","key3"))
df_5: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
|null| 7| 3| t1|null|
| bbb| 1| 5| t3| 0|
|null| 4|null| t1| 8|
+----+----+----+----+----+
scala> df_5.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
|null| 7| 3| t1|null|
| bbb| 1| 5| t3| 0|
+----+----+----+----+----+
/*
同上，如果不指定列名列表，则默认列名列表就是所有列
*/
scala> val df_6 = df.na.drop(4)
df_6: org.apache.spark.sql.DataFrame = [key1: string, key2: string ... 3 more fields]
scala> df_6.show
+----+----+----+----+----+
|key1|key2|key3|key4|key5|
+----+----+----+----+----+
| aaa| 1| 2| t1| 4|
| bbb| 5| 3| t2| 8|
| ccc| 2| 2|null| 7|
| bbb| 1| 5| t3| 0|
+----+----+----+----+----+ok，就到这吧，下次再写多表的部分了~~