Hi Everett, That's pretty much what I'd do. Can't think of a way to beat your solution. Why do you "feel vaguely uneasy about it"?
I'd also check out the execution plan (with explain) to see how it's gonna work at runtime. I may have seen groupBy + join be better than window (there were more exchanges in play for windows I reckon). Pozdrawiam, Jacek Laskowski ---- https://medium.com/@jaceklaskowski/ Mastering Apache Spark 2.0 https://bit.ly/mastering-apache-spark Follow me at https://twitter.com/jaceklaskowski On Tue, Feb 7, 2017 at 10:54 PM, Everett Anderson <ever...@nuna.com> wrote: > > > On Tue, Feb 7, 2017 at 12:50 PM, Jacek Laskowski <ja...@japila.pl> wrote: >> >> Hi, >> >> Could groupBy and withColumn or UDAF work perhaps? I think window could >> help here too. > > > This seems to work, but I do feel vaguely uneasy about it. :) > > // First add a 'rank' column which is priority order just in case priorities > aren't > // from 1 with no gaps. > val temp1 = data.withColumn("rank", functions.dense_rank() > .over(Window.partitionBy("id", "name").orderBy("priority"))) > > +---+----+-----+------+--------+----+ > | id|name|extra| data|priority|rank| > +---+----+-----+------+--------+----+ > | 1|Fred| 8|value1| 1| 1| > | 1|Fred| 8|value8| 2| 2| > | 1|Fred| 8|value5| 3| 3| > | 2| Amy| 9|value3| 1| 1| > | 2| Amy| 9|value5| 2| 2| > +---+----+-----+------+--------+----+ > > // Now move all the columns we want to denormalize into a struct column to > keep them together. > val temp2 = temp1.withColumn("temp_struct", struct(temp1("extra"), > temp1("data"), temp1("priority"))) > .drop("extra", "data", "priority") > > +---+----+----+------------+ > | id|name|rank| temp_struct| > +---+----+----+------------+ > | 1|Fred| 1|[8,value1,1]| > | 1|Fred| 2|[8,value8,2]| > | 1|Fred| 3|[8,value5,3]| > | 2| Amy| 1|[9,value3,1]| > | 2| Amy| 2|[9,value5,2]| > +---+----+----+------------+ > > // groupBy, again, but now pivot the rank column. We need an aggregate > function after pivot, > // so use first -- there will only ever be one element. > val temp3 = temp2.groupBy("id", "name") > .pivot("rank", Seq("1", "2", "3")) > .agg(functions.first("temp_struct")) > > +---+----+------------+------------+------------+ > | id|name| 1| 2| 3| > +---+----+------------+------------+------------+ > | 1|Fred|[8,value1,1]|[8,value8,2]|[8,value5,3]| > | 2| Amy|[9,value3,1]|[9,value5,2]| null| > +---+----+------------+------------+------------+ > > // Now just moving things out of the structs and clean up. > val output = temp3.withColumn("extra1", temp3("1").getField("extra")) > .withColumn("data1", temp3("1").getField("data")) > .withColumn("priority1", temp3("1").getField("priority")) > .withColumn("extra2", temp3("2").getField("extra")) > .withColumn("data2", temp3("2").getField("data")) > .withColumn("priority2", temp3("2").getField("priority")) > .withColumn("extra3", temp3("3").getField("extra")) > .withColumn("data3", temp3("3").getField("data")) > .withColumn("priority3", temp3("3").getField("priority")) > .drop("1", "2", "3") > > +---+----+------+------+---------+------+------+---------+------+------+---------+ > | id|name|extra1| data1|priority1|extra2| data2|priority2|extra3| > data3|priority3| > +---+----+------+------+---------+------+------+---------+------+------+---------+ > | 1|Fred| 8|value1| 1| 8|value8| 2| 8|value5| > 3| > | 2| Amy| 9|value3| 1| 9|value5| 2| null| null| > null| > +---+----+------+------+---------+------+------+---------+------+------+---------+ > > > > > > > >> >> >> Jacek >> >> On 7 Feb 2017 8:02 p.m., "Everett Anderson" <ever...@nuna.com.invalid> >> wrote: >>> >>> Hi, >>> >>> I'm trying to un-explode or denormalize a table like >>> >>> +---+----+-----+------+--------+ >>> |id |name|extra|data |priority| >>> +---+----+-----+------+--------+ >>> |1 |Fred|8 |value1|1 | >>> |1 |Fred|8 |value8|2 | >>> |1 |Fred|8 |value5|3 | >>> |2 |Amy |9 |value3|1 | >>> |2 |Amy |9 |value5|2 | >>> +---+----+-----+------+--------+ >>> >>> into something that looks like >>> >>> >>> +---+----+------+------+---------+------+------+---------+------+------+---------+ >>> |id |name|extra1|data1 |priority1|extra2|data2 |priority2|extra3|data3 >>> |priority3| >>> >>> +---+----+------+------+---------+------+------+---------+------+------+---------+ >>> |1 |Fred|8 |value1|1 |8 |value8|2 |8 |value5|3 >>> | >>> |2 |Amy |9 |value3|1 |9 |value5|2 |null |null >>> |null | >>> >>> +---+----+------+------+---------+------+------+---------+------+------+---------+ >>> >>> If I were going the other direction, I'd create a new column with an >>> array of structs, each with 'extra', 'data', and 'priority' fields and then >>> explode it. >>> >>> Going from the more normalized view, though, I'm having a harder time. >>> >>> I want to group or partition by (id, name) and order by priority, but >>> after that I can't figure out how to get multiple rows rotated into one. >>> >>> Any ideas? >>> >>> Here's the code to create the input table above: >>> >>> import org.apache.spark.sql.Row >>> import org.apache.spark.sql.Dataset >>> import org.apache.spark.sql.types._ >>> >>> val rowsRDD = sc.parallelize(Seq( >>> Row(1, "Fred", 8, "value1", 1), >>> Row(1, "Fred", 8, "value8", 2), >>> Row(1, "Fred", 8, "value5", 3), >>> Row(2, "Amy", 9, "value3", 1), >>> Row(2, "Amy", 9, "value5", 2))) >>> >>> val schema = StructType(Seq( >>> StructField("id", IntegerType, nullable = true), >>> StructField("name", StringType, nullable = true), >>> StructField("extra", IntegerType, nullable = true), >>> StructField("data", StringType, nullable = true), >>> StructField("priority", IntegerType, nullable = true))) >>> >>> val data = sqlContext.createDataFrame(rowsRDD, schema) >>> >>> >>> > --------------------------------------------------------------------- To unsubscribe e-mail: user-unsubscr...@spark.apache.org
