Already tried that. The CPU hits 100% on the collectAsMap (even tried foreaching to a java ConcurrentHashmap), and eventually finishes, but while broadcasting, it takes a while, and at some point there's some timeout, and the worker is killed. The driver (and workers) have more than enough RAM (1.5GB of parquet expands to about 4.5GB, and the nodes have 64GB RAM). Filtering is also not an option, as every entry of the "smaller" dataset exists in the large one. As mentioned in another reply, I managed to get it working by using embedded Redis on the driver, loading the smaller dataset into it, and then doing a straight map on the larger dataset via a foreachPartition, and doing lookups to the dirver's Redis. Since there's no network shuffle, the temp folder is barely touched, and it seems to work quite well. -Ashic.
From: zouz...@gmail.com Date: Wed, 10 Aug 2016 08:22:24 +0200 Subject: Re: Spark join and large temp files To: as...@live.com Hi Ashic, I think this approach should solve your problem, i.e., by broadcasting the small RDD. However you should do it propertly. IMO, you should try val smallRDDBroadcasted: Map[Int, YouTypeValue] = sc.broadcast(smallRDD.collectAsMap()) bigRDD.mapPartitoin{ case elems => // Here manually join using the map elems.flatMap{ case (key, value) => smallRDDBroadcasted.value.get(key).map(x => (key, (value,x)) }} Ensure that your driver has enough memory to store the above Map. If you get out of memory on the driver, increase your memory. Speaking of which, a filtering step might also help on the above, i.e., filter the bigRDD with the keys of the Map before joining. Hope this helps,Anastasios On Tue, Aug 9, 2016 at 4:46 PM, 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 -- -- Anastasios Zouzias