Apache spark 如何将行压缩为一行？

环境：spark 2.4.5

来源：test.csv 目标：test.csv 如您所见，我希望将具有相同id和日期的行合并为一行

我的解决方案： 我已尝试使用arrays_zip函数来处理它，但失败：

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cols

---

如果您能给我一些建议，我将不胜感激。

使用

展平

和

数组

而不是

数组，然后使用
元素
函数从每个元素获取项目


2.使用groupBy和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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+

SQL:

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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+


动态方式：

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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+

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|
//+---+----+-----+-----+-----+

谢谢你详细的回答！