关于如何在 Scala 中使用随机值向现有 DataFrame 添加新列 [英] About how to add a new column to an existing DataFrame with random values in Scala
问题描述
我有一个带有镶木地板文件的数据框，我必须添加一个包含一些随机数据的新列，但我需要这些随机数据彼此不同.这是我的实际代码，spark 的当前版本是 1.5.1-cdh-5.5.2:
i have a dataframe with a parquet file and I have to add a new column with some random data, but I need that random data different each other. This is my actual code and the current version of spark is 1.5.1-cdh-5.5.2:
val mydf = sqlContext.read.parquet("some.parquet")
// mydf.count()
// 63385686
mydf.cache
val r = scala.util.Random
import org.apache.spark.sql.functions.udf
def myNextPositiveNumber :String = { (r.nextInt(Integer.MAX_VALUE) + 1 ).toString.concat("D")}
val myFunction = udf(myNextPositiveNumber _)
val myNewDF = mydf.withColumn("myNewColumn",lit(myNextPositiveNumber))
有了这个代码，我就有了这个数据:
with this code, I have this data:
scala> myNewDF.select("myNewColumn").show(10,false)
+-----------+
|myNewColumn|
+-----------+
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
+-----------+
看起来 udf myNextPositiveNumber 只被调用一次，不是吗?
It looks like that the udf myNextPositiveNumber is invoked only once, isn't?
更新确认，只有一个不同的值:
update confirmed, there is only one distinct value:
scala> myNewDF.select("myNewColumn").distinct.show(50,false)
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
17/02/21 13:23:11 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
...
+-----------+
|myNewColumn|
+-----------+
|889488717D |
+-----------+
我做错了什么?
更新 2:最后，在@user6910411 的帮助下，我得到了这个代码:
Update 2: finally, with the help of @user6910411 I have this code:
val mydf = sqlContext.read.parquet("some.parquet")
// mydf.count()
// 63385686
mydf.cache
val r = scala.util.Random
import org.apache.spark.sql.functions.udf
val accum = sc.accumulator(1)
def myNextPositiveNumber():String = {
accum+=1
accum.value.toString.concat("D")
}
val myFunction = udf(myNextPositiveNumber _)
val myNewDF = mydf.withColumn("myNewColumn",lit(myNextPositiveNumber))
myNewDF.select("myNewColumn").count
// 63385686
更新 3
实际代码生成的数据如下:
Actual code generates data like this:
scala> mydf.select("myNewColumn").show(5,false)
17/02/22 11:01:57 WARN ParquetRecordReader: Can not initialize counter due to context is not a instance of TaskInputOutputContext, but is org.apache.hadoop.mapreduce.task.TaskAttemptContextImpl
+-----------+
|myNewColumn|
+-----------+
|2D |
|2D |
|2D |
|2D |
|2D |
+-----------+
only showing top 5 rows
看起来 udf 函数只被调用一次，不是吗?我需要在该列中添加一个新的随机元素.
It looks like the udf function is invoked only once, isn't? I need a new random element in that column.
更新 4 @user6910411
update 4 @user6910411
我有这个增加 id 的实际代码，但它没有连接最终的字符，这很奇怪.这是我的代码:
i have this actual code that increases the id but it is not concatenating the final char, it is weird. This is my code:
import org.apache.spark.sql.functions.udf
val mydf = sqlContext.read.parquet("some.parquet")
mydf.cache
def myNextPositiveNumber():String = monotonically_increasing_id().toString().concat("D")
val myFunction = udf(myNextPositiveNumber _)
val myNewDF = mydf.withColumn("myNewColumn",expr(myNextPositiveNumber))
scala> myNewDF.select("myNewColumn").show(5,false)
17/02/22 12:00:02 WARN Executor: 1 block locks were not released by TID = 1:
[rdd_4_0]
+-----------+
|myNewColumn|
+-----------+
|0 |
|1 |
|2 |
|3 |
|4 |
+-----------+
我需要类似的东西:
+-----------+
|myNewColumn|
+-----------+
|1D |
|2D |
|3D |
|4D |
+-----------+
推荐答案
Spark >= 2.3
可以使用 asNondeterministic 方法禁用一些优化:
It is possible to disable some optimizations using asNondeterministic method:
import org.apache.spark.sql.expressions.UserDefinedFunction
val f: UserDefinedFunction = ???
val fNonDeterministic: UserDefinedFunction = f.asNondeterministic
在使用此选项之前，请确保您了解这些保证.
Please make sure you understand the guarantees before using this option.
火花<2.3
传递给 udf 的函数应该是确定性的(SPARK-20586) 和 nullary 函数调用可以替换为常量.如果要生成随机数，请使用内置函数:
Function which is passed to udf should be deterministic (with possible exception of SPARK-20586) and nullary functions calls can be replaced by constants. If you want to generate random numbers use on of the built-in functions:
并转换输出以获得所需的分布，例如:
and transform the output to obtain required distribution for example:
(rand * Integer.MAX_VALUE).cast("bigint").cast("string")
这篇关于关于如何在 Scala 中使用随机值向现有 DataFrame 添加新列的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
