Can you give some outline as to what you mean? Should I broadcast a dataframe, and register the broadcasted df as a temp table? And then use a lookup UDF in a SELECT query? I've managed to get it working by loading the 1.5GB dataset into an embedded redis instance on the driver, and used a mapPartitions on the big dataframe to map it to the required triples by doing the lookup from redis. It took around 13 minutes to load the data into redis using 4 cores, and the subsequent map on the main dataset was quite fast.
From: gourav.sengu...@gmail.com Date: Tue, 9 Aug 2016 21:13:51 +0100 Subject: Re: Spark join and large temp files To: as...@live.com CC: mich.talebza...@gmail.com; samkiller....@gmail.com; deepakmc...@gmail.com; user@spark.apache.org In case of skewed data the joins will mess things up. Try to write a UDF with the lookup on broadcast variable and then let me know the results. It should not take more than 40 mins in a 32 GB RAM system with 6 core processors. Gourav On Tue, Aug 9, 2016 at 6:02 PM, Ashic Mahtab <as...@live.com> wrote: Hi Mich,Hardware: AWS EMR cluster with 15 nodes with Rx3.2xlarge (CPU, RAM fine, disk a couple of hundred gig). When we do: onPointFiveTB.join(onePointFiveGig.cache(), "id") we're seing that the temp directory is filling up fast, until a node gets killed. And then everything dies. -Ashic. From: mich.talebza...@gmail.com Date: Tue, 9 Aug 2016 17:25:23 +0100 Subject: Re: Spark join and large temp files To: as...@live.com CC: samkiller....@gmail.com; deepakmc...@gmail.com; user@spark.apache.org Hi Sam, What is your spark Hardware spec, No of nodes, RAM per node and disks please? I don't understand this should not really be an issue. Underneath the bonnet it is a hash join. The small table I gather can be cached and the big table will do multiple passes using the temp space. HTH Dr Mich Talebzadeh LinkedIn https://www.linkedin.com/profile/view?id=AAEAAAAWh2gBxianrbJd6zP6AcPCCdOABUrV8Pw http://talebzadehmich.wordpress.com Disclaimer: Use it at your own risk. Any and all responsibility for any loss, damage or destruction of data or any other property which may arise from relying on this email's technical content is explicitly disclaimed. The author will in no case be liable for any monetary damages arising from such loss, damage or destruction. On 9 August 2016 at 15:46, Ashic Mahtab <as...@live.com> wrote: Hi Sam,Yup. It seems it stalls when broadcasting. CPU goes to 100%, but there's no progress. The spark UI doesn't even show up. -Ashic. From: samkiller....@gmail.com Date: Tue, 9 Aug 2016 16:21:27 +0200 Subject: Re: Spark join and large temp files To: as...@live.com CC: deepakmc...@gmail.com; user@spark.apache.org Have you tried to broadcast your small table table in order to perform your join ? joined = bigDF.join(broadcast(smallDF, ....) On Tue, Aug 9, 2016 at 3:29 PM, Ashic Mahtab <as...@live.com> wrote: Hi Deepak,No...not really. Upping the disk size is a solution, but more expensive as you can't attach EBS volumes to EMR clusters configured with data pipelines easily (which is what we're doing). I've tried collecting the 1.5G dataset in a hashmap, and broadcasting. Timeouts seems to prevent that (even after upping the max driver result size). Increasing partition counts didn't help (the shuffle used up the temp space). I'm now looking at some form of clever broadcasting, or maybe falling back to chunking up the input, producing interim output, and unioning them for the final output. Might even try using Spark Streaming pointing to the parquet and seeing if that helps. -Ashic. From: deepakmc...@gmail.com Date: Tue, 9 Aug 2016 17:31:19 +0530 Subject: Re: Spark join and large temp files To: as...@live.com Hi AshicDid you find the resolution to this issue?Just curious to know like what helped in this scenario. ThanksDeepak On Tue, Aug 9, 2016 at 12:23 AM, Ashic Mahtab <as...@live.com> wrote: Hi Deepak,Thanks for the response. Registering the temp tables didn't help. Here's what I have: val a = sqlContext..read.parquet(...).select("eid.id", "name").withColumnRenamed("eid.id", "id")val b = sqlContext.read.parquet(...).select("id", "number") a.registerTempTable("a")b.registerTempTable("b") val results = sqlContext.sql("SELECT x.id, x.name, y.number FROM a x join b y on x.id=y.id) results.write.parquet(...) Is there something I'm missing? Cheers,Ashic. From: deepakmc...@gmail.com Date: Tue, 9 Aug 2016 00:01:32 +0530 Subject: Re: Spark join and large temp files To: as...@live.com CC: user@spark.apache.org Register you dataframes as temp tables and then try the join on the temp table.This should resolve your issue. ThanksDeepak On Mon, Aug 8, 2016 at 11:47 PM, Ashic Mahtab <as...@live.com> wrote: Hello,We have two parquet inputs of the following form: a: id:String, Name:String (1.5TB)b: id:String, Number:Int (1.3GB) We need to join these two to get (id, Number, Name). We've tried two approaches: a.join(b, Seq("id"), "right_outer") where a and b are dataframes. We also tried taking the rdds, mapping them to pair rdds with id as the key, and then joining. What we're seeing is that temp file usage is increasing on the join stage, and filling up our disks, causing the job to crash. Is there a way to join these two data sets without well...crashing? Note, the ids are unique, and there's a one to one mapping between the two datasets. Any help would be appreciated. -Ashic. -- Thanks Deepak www.bigdatabig.com www.keosha.net -- Thanks Deepak www.bigdatabig.com www.keosha.net
