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apache-spark - 如何将行压缩为一个？

问题描述

环境：火花 2.4.5

来源：test.csv

id,date,item1,item2,item3
0,1,111,,
0,1,,222,
0,1,,,333
1,1,111,,
1,1,,222,
1,1,,,333

目标：test.csv

id,date,item1,item2,item3
0,1,111,222,333
1,1,111,222,333

如您所见，我想将具有相同id和date的行合并为一个。

我的解决方案：

我试过 arrays_zip 函数来处理它但失败了：

val soruce = spark.read("/home/user/test.csv").csv.options("header", "true")
spark.sql("SELECT id , date, arrays_zip( collect_list(item1), collect_list(item2), collect_list(item3)) FROM source GROUP BY id,date").show(false)

+---+----+-------------------------------------------------------------------------+
|id |date|arrays_zip(collect_list(item1), collect_list(item2), collect_list(item3))|
+---+----+-------------------------------------------------------------------------+
|0  |1   |[[111, 222, 333]]                                                        |
|1  |1   |[[111, 222, 333]]                                                        |
+---+----+-------------------------------------------------------------------------+

也许我应该把这个数组分解成列？

如果您能给我一些建议，我将不胜感激。

标签： apache-sparkpysparkapache-spark-sql

解决方案

使用flattenandarray而不是arrays_zipthen 使用element_at函数从每个元素中获取项目。

val df = spark.read("/home/user/test.csv").csv.options("header", "true")

df.groupBy(col("id"),col("date")).
agg(flatten(array(collect_list(col("item1")),collect_list(col("item2")),collect_list(col("item3")))).alias("it")).
withColumn("item1",element_at(col("it"),1)).
withColumn("item2",element_at(col("it"),2)).
withColumn("item3",element_at(col("it"),3)).
drop("it").
show()
//+---+----+-----+-----+-----+
//| id|date|item1|item2|item3|
//+---+----+-----+-----+-----+
//|  0|   1|  111|  222|  333|
//|  1|   1|  111|  222|  333|
//+---+----+-----+-----+-----+

2.Using groupBy and first(col,ignoreNulls=true)

df.groupBy(col("id"),col("date")).
agg(first(col("item1")).alias("item1"),first(col("item2"),true).alias("item2"),first(col("item3"),true).alias("item3")).
show()
//+---+----+-----+-----+-----+
//| id|date|item1|item2|item3|
//+---+----+-----+-----+-----+
//|  0|   1|  111|  222|  333|
//|  1|   1|  111|  222|  333|
//+---+----+-----+-----+-----+

SQL:

df.createOrReplaceTempView("tmp")

//using first
spark.sql("select id,date,first(item1,true) as item1,first(item2,true) as item2,first(item3,true) as item3 from tmp group by id,date").show()

//using max
spark.sql("select id,date,max(item1) as item1,max(item2) as item2,max(item3) as item3 from tmp group by id,date").show()

//using flatten array
spark.sql("select id,date, element_at(tmp,1)item1, element_at(tmp,2)item2, element_at(tmp,3)item3 from (select id,date,flatten(array(collect_list(item1),collect_list(item2),collect_list(item3))) as tmp from tmp group by id,date)t").show()
//+---+----+-----+-----+-----+
//| id|date|item1|item2|item3|
//+---+----+-----+-----+-----+
//|  0|   1|  111|  222|  333|
//|  1|   1|  111|  222|  333|
//+---+----+-----+-----+-----+

Dynamic way:

val df = spark.read("/home/user/test.csv").csv.options("header", "true")

val df1=df.groupBy(col("id"),col("date")).agg(flatten(array(collect_list(col("item1")),collect_list(col("item2")),collect_list(col("item3")))).alias("it"))

val len=df1.agg(max(size(col("it")))).collect()(0)(0).toString.toInt

spark.range(len).collect().foldLeft(df1)((df,len) => df.withColumn(s"item${len+1}",col("it")(len))).
drop("it").
show()
//+---+----+-----+-----+-----+
//| id|date|item1|item2|item3|
//+---+----+-----+-----+-----+
//|  0|   1|  111|  222|  333|
//|  1|   1|  111|  222|  333|
//+---+----+-----+-----+-----+

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