You could also try pivot. On 7 February 2017 at 16:13, Everett Anderson <ever...@nuna.com.invalid> wrote:
> > > On Tue, Feb 7, 2017 at 2:21 PM, Michael Armbrust <mich...@databricks.com> > wrote: > >> I think the fastest way is likely to use a combination of conditionals >> (when / otherwise), first (ignoring nulls), while grouping by the id. >> This should get the answer with only a single shuffle. >> >> Here is an example >> <https://databricks-prod-cloudfront.cloud.databricks.com/public/4027ec902e239c93eaaa8714f173bcfc/1023043053387187/3908422850525880/2840265927289860/latest.html> >> . >> > > Very cool! Using the simpler aggregates feels cleaner. > > >> >> On Tue, Feb 7, 2017 at 5:07 PM, Jacek Laskowski <ja...@japila.pl> wrote: >> >>> 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"? >>> >> > Maybe it felt like I was unnecessarily grouping-by twice, but probably > mostly that I hadn't used pivot before. > > Interestingly, the physical plans are not especially different between > these two solutions after the rank column is added. They both have two > SortAggregates that seem to be figuring out where to put results based on > the rank: > > My original one: > > == Physical Plan == > *Project [id#279, name#280, 1#372.extra AS extra1#409, 1#372.data AS > data1#435, 1#372.priority AS priority1#462, 2#374.extra AS extra2#490, > 2#374.data AS data2#519, 2#374.priority AS priority2#549, 3#376.extra AS > extra3#580, 3#376.data AS data3#612, 3#376.priority AS priority3#645] > +- SortAggregate(key=[id#279,name#280], functions=[first(if > ((cast(rank#292 as double) = 1.0)) temp_struct#312 else null, > true),first(if ((cast(rank#292 as double) = 2.0)) temp_struct#312 else > null, true),first(if ((cast(rank#292 as double) = 3.0)) temp_struct#312 > else null, true)]) > +- SortAggregate(key=[id#279,name#280], functions=[partial_first(if > ((cast(rank#292 as double) = 1.0)) temp_struct#312 else null, > true),partial_first(if ((cast(rank#292 as double) = 2.0)) temp_struct#312 > else null, true),partial_first(if ((cast(rank#292 as double) = 3.0)) > temp_struct#312 else null, true)]) > +- *Project [id#279, name#280, rank#292, struct(extra#281, data#282, > priority#283) AS temp_struct#312] > +- Window [denserank(priority#283) windowspecdefinition(id#279, > name#280, priority#283 ASC, ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT > ROW) AS rank#292], [id#279, name#280], [priority#283 ASC] > +- *Sort [id#279 ASC, name#280 ASC, priority#283 ASC], false, 0 > +- Exchange hashpartitioning(id#279, name#280, 200) > +- Scan ExistingRDD[id#279,name#280, > extra#281,data#282,priority#283] > > > And modifying Michael's slightly to use a rank: > > import org.apache.spark.sql.functions._ > > def getColumnWithRank(column: String, rank: Int) = { > first(when(col("rank") === lit(rank), col(column)).otherwise(null), > ignoreNulls = true) > } > > val withRankColumn = data.withColumn("rank", > functions.dense_rank().over(Window.partitionBy("id", > "name").orderBy("priority"))) > > val modCollapsed = withRankColumn > .groupBy($"id", $"name") > .agg( > getColumnWithRank("data", 1) as 'data1, > getColumnWithRank("data", 2) as 'data2, > getColumnWithRank("data", 3) as 'data3, > getColumnWithRank("extra", 1) as 'extra1, > getColumnWithRank("extra", 2) as 'extra2, > getColumnWithRank("extra", 3) as 'extra3) > > > modCollapsed.explain > > == Physical Plan == > SortAggregate(key=[id#279,name#280], functions=[first(CASE WHEN (rank#965 > = 1) THEN data#282 ELSE null END, true),first(CASE WHEN (rank#965 = 2) THEN > data#282 ELSE null END, true),first(CASE WHEN (rank#965 = 3) THEN data#282 > ELSE null END, true),first(CASE WHEN (rank#965 = 1) THEN extra#281 ELSE > null END, true),first(CASE WHEN (rank#965 = 2) THEN extra#281 ELSE null > END, true),first(CASE WHEN (rank#965 = 3) THEN extra#281 ELSE null END, > true)]) > +- SortAggregate(key=[id#279,name#280], functions=[partial_first(CASE > WHEN (rank#965 = 1) THEN data#282 ELSE null END, true),partial_first(CASE > WHEN (rank#965 = 2) THEN data#282 ELSE null END, true),partial_first(CASE > WHEN (rank#965 = 3) THEN data#282 ELSE null END, true),partial_first(CASE > WHEN (rank#965 = 1) THEN extra#281 ELSE null END, true),partial_first(CASE > WHEN (rank#965 = 2) THEN extra#281 ELSE null END, true),partial_first(CASE > WHEN (rank#965 = 3) THEN extra#281 ELSE null END, true)]) > +- *Project [id#279, name#280, extra#281, data#282, rank#965] > +- Window [denserank(priority#283) windowspecdefinition(id#279, > name#280, priority#283 ASC, ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT > ROW) AS rank#965], [id#279, name#280], [priority#283 ASC] > +- *Sort [id#279 ASC, name#280 ASC, priority#283 ASC], false, 0 > +- Exchange hashpartitioning(id#279, name#280, 200) > +- Scan ExistingRDD[id#279,name#280, > extra#281,data#282,priority#283] > > > >> >>> 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 >>> >>> >> >
