Question

我使用HiveContext创建了DataFrame，其中的一列保存着像这样的记录：

text1        text2

我们希望将2个文本之间的空格替换为一个文本，并最终输出为：

text1 text2

我们如何在Spark SQL中实现这一目标？请注意，我们正在使用Hive Context，注册临时表并在其上编写SQL查询。

Answer 1

import org.apache.spark.sql.functions._

val myUDf = udf((s:String) => Array(s.trim.replaceAll(" +", " ")))
//error: object java.lang.String is not a value --> use Array

val data = List("i  like    cheese", "  the dog runs   ", "text111111   text2222222")
val df = data.toDF("val")
df.show()

val new_df = df
  .withColumn("udfResult",myUDf(col("val")))
  .withColumn("new_val", col("udfResult")(0))
  .drop("udfResult")
new_df.show

数据块的输出

+--------------------+
|                 val|
+--------------------+
|   i  like    cheese|
|     the dog runs   |
|text111111   text...|
+--------------------+

+--------------------+--------------------+
|                 val|             new_val|
+--------------------+--------------------+
|   i  like    cheese|       i like cheese|
|     the dog runs   |        the dog runs|
|text111111   text...|text111111 text22...|
+--------------------+--------------------+

Answer 2

更好的是，我现在已经被一位真正的专家所启发。实际上更简单：

   import org.apache.spark.sql.functions._
// val myUDf = udf((s:String) => Array(s.trim.replaceAll(" +", " ")))
   val myUDf = udf((s:String) => s.trim.replaceAll("\\s+", " ")) // <-- no Array(...)
// Then there is no need to play with columns excessively:

   val data = List("i  like    cheese", "  the dog runs   ", "text111111   text2222222")
   val df = data.toDF("val")
   df.show()

   val new_df = df.withColumn("new_val", myUDf(col("val")))
   new_df.show

Answer 3

只需在 spark.sql 中执行

regexp_replace( COLUMN, ' +', ' ')

https://spark.apache.org/docs/latest/api/sql/index.html#regexp_replace

检查一下：

spark.sql("""
    select regexp_replace(col1, ' +', ' ') as col2
    from (
        select 'text1        text2     text3' as col1
    )
""").show(20,False)

输出

+-----------------+
|col2             |
+-----------------+
|text1 text2 text3|
+-----------------+

在Spark SQL中使用单个空格删除多个空格

3 个答案: