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JavaUDF在Sql SQL中复制相同的值[复制]

apache-spark-sql pyspark apache-spark

marjun · 技术社区 · 6 年前

我有一个带拼花文件的数据框,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,当前版本的Spark是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:

scala> myNewDF.select("myNewColumn").show(10,false)
+-----------+
|myNewColumn|
+-----------+
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
|889488717D |
+-----------+

看起来udf mynextpositivenumber只调用一次,不是吗?

更新确认,只有一个不同的值:

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 |
+-----------+

我做错什么了?

更新2:最后,在@user6910411的帮助下,我得到了以下代码:

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

实际代码生成如下数据:

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函数只调用一次,不是吗?我需要一个新的随机元素。

更新4@user6910411

我有这个实际的代码,增加了id,但它没有连接最后的字符,这很奇怪。这是我的代码:

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          |
+-----------+

我需要这样的东西:

+-----------+
|myNewColumn|
+-----------+
|1D         |
|2D         |
|3D         |
|4D         |
+-----------+

0 回复 | 直到 6 年前

user6910411 7 年前

火花=2.3

可以使用 asNondeterministic 方法:

import org.apache.spark.sql.expressions.UserDefinedFunction

val f: UserDefinedFunction = ???
val fNonDeterministic: UserDefinedFunction = f.asNondeterministic

在使用此选项之前,请确保您理解这些保证。

火花<2.3

传递给UDF的函数应该是确定性的(可能除了 SPARK-20586 可以用常量替换零函数调用。如果要生成在内置函数上使用的随机数:

rand - 从u[0.0,1.0]生成一个具有独立且相同分布(i.i.d.)样本的随机列。
randn - 从标准正态分布中生成具有独立且相同分布(I.I.D.)样本的列。

并转换输出以获得所需的分布,例如:

(rand * Integer.MAX_VALUE).cast("bigint").cast("string")

Avik Aggarwal 7 年前

你可以利用 monotonically_increasing_id 生成随机值。

然后,您可以定义一个UDF,在将字符串转换为字符串后将其附加到字符串中。 单调递增 默认情况下返回long。

scala> var df = Seq(("Ron"), ("John"), ("Steve"), ("Brawn"), ("Rock"), ("Rick")).toDF("names")
+-----+
|names|
+-----+
|  Ron|
| John|
|Steve|
|Brawn|
| Rock|
| Rick|
+-----+

scala> val appendD = spark.sqlContext.udf.register("appendD", (s: String) => s.concat("D"))

scala> df = df.withColumn("ID",monotonically_increasing_id).selectExpr("names","cast(ID as String) ID").withColumn("ID",appendD($"ID"))
+-----+---+
|names| ID|
+-----+---+
|  Ron| 0D|
| John| 1D|
|Steve| 2D|
|Brawn| 3D|
| Rock| 4D|
| Rick| 5D|
+-----+---+