Sorry i sent the wrong join code snippet, the actual snippet is
ggImpsDf.join(
aggRevenueDf,
aggImpsDf("id_1") <=> aggRevenueDf("id_1")
&& aggImpsDf("id_2") <=> aggRevenueDf("id_2")
&& aggImpsDf("day_hour") <=> aggRevenueDf("day_hour")
&& aggImpsDf("day_hour_2") <=> aggRevenueDf("day_hour_2"),
"inner")
.select(
aggImpsDf("id_1"), aggImpsDf("id_2"), aggImpsDf("day_hour"),
aggImpsDf("day_hour_2"), aggImpsDf("metric1"), aggRevenueDf("metric2"))
.coalesce(200)
On Fri, Oct 23, 2015 at 11:16 AM pratik khadloya <tispra...@gmail.com>
wrote:
> Hello,
>
> Data about my spark job is below. My source data is only 916MB (stage 0)
> and 231MB (stage 1), but when i join the two data sets (stage 2) it takes a
> very long time and as i see the shuffled data is 614GB. Is this something
> expected? Both the data sets produce 200 partitions.
>
> Stage IdDescriptionSubmittedDurationTasks: Succeeded/TotalInputOutputShuffle
> ReadShuffle Write2saveAsTable at Driver.scala:269
> <http://sparkhs.rfiserve.net:18080/history/application_1437606252645_1034031/stages/stage?id=2&attempt=0>
> +details
>
> 2015/10/22 18:48:122.3 h
> 200/200
> 614.6 GB1saveAsTable at Driver.scala:269
> <http://sparkhs.rfiserve.net:18080/history/application_1437606252645_1034031/stages/stage?id=1&attempt=0>
> +details
>
> 2015/10/22 18:46:022.1 min
> 8/8
> 916.2 MB3.9 MB0saveAsTable at Driver.scala:269
> <http://sparkhs.rfiserve.net:18080/history/application_1437606252645_1034031/stages/stage?id=0&attempt=0>
> +details
>
> 2015/10/22 18:46:0235 s
> 3/3
> 231.2 MB4.8 MBAm running Spark 1.4.1 and my code snippet which joins the
> two data sets is:
>
> hc.sql(query).
> mapPartitions(iter => {
> iter.map {
> case Row(
> ...
> ...
> ...
> )
> }
> }
> ).toDF()
> .groupBy("id_1", "id_2", "day_hour", "day_hour_2")
> .agg($"id_1", $"id_2", $"day_hour", $"day_hour_2",
> sum("attr1").alias("attr1"), sum("attr2").alias("attr2"))
>
>
> Please advise on how to reduce the shuffle and speed this up.
>
>
> ~Pratik
>
>
