I was aiming to show the operations with pseudo-code, but I apparently failed, so Java it is ☺ Assume the following 3 datasets on HDFS.
1. RDD1: User (1 Million rows – 2GB ) Columns: uid, locationId, (extra stuff) 2. RDD2: Actions (1 Billion rows – 500GB) Columns: uid_1, uid_2 (extra stuff) 3. RDD3: Locations (10 Millions rows – 10GB) Columns: locationId, (extra stuff) So, what I am doing is shown below. I assume that it is clear of why using a “join” would be really inefficient, but if not have a look of some of the reasons here<http://stackoverflow.com/questions/30412325/hoes-does-spark-schedule-a-join/30490619#30490619> . //1 Filter actions > JavaRDD actionRdd = RDD2.filter(f->….); //300GB //2 Filter users & Broadcast > JavaRDD userRdd = RDD1.filter(f->…); //1GB > Map m = userRdd.keyBy(f->g.getUid()).collectAsMap(); > Broadcast<Map> userMap = sparkCtx.broadcast(m);//1GB //3 Filter locations & Broadcast > JavaPairRDD locations = RDD3.filter(f->….).keyBy(f->g.getLocationId()); > Broadcast<Map> locationMap = > sparkCtx.broadcast(locations.collectAsMap());//1GB //4 Process using data from all 3 data sets > actionRdd.map(f-> { User u1 = userMap.value.get( f.getUid1()); User u2 = userMap.value.get( f.getUid2()); Location l= locationMap.value.get(u.getLocationId(); Object result = method(f,u1,u2,l);//method implementation not important, but requires all 3 objects return result; }); From: Marcin Tustin [mailto:mtus...@handybook.com] Sent: 28 April 2016 12:27 To: Deligiannis, Ioannis (UK) Cc: dev@spark.apache.org Subject: Re: RDD.broadcast I don't know what your notation really means. I'm very much unclear on why you can't use the filter method for 1. If you're talking about splitting/bucketing rather filtering as such I think that is a specific lacuna in spark's Api. I've generally found the join api to be entirely adequate for my needs, so I don't really have a comment on 2. On Thursday, April 28, 2016, <ioannis.deligian...@nomura.com<mailto:ioannis.deligian...@nomura.com>> wrote: One example pattern we have it doing joins or filters based on two datasets. E.g. 1 Filter –multiple- RddB for a given set extracted from RddA (keyword here is multiple times) a. RddA -> keyBy -> distinct -> collect() to Set A; b. RddB -> Filter using Set A; 2 “Join” using composition on executor (again multiple times) a. RddA -> filter by XYZ -> keyBy join attribute -> collectAsMap ->Broadcast MapA; b. RddB -> map (Broadcast<Map<K,V>> MapA; The first use case might not be that common, but joining a large RDD with a small (reference) RDD is quite common and much faster than using “join” method. From: Marcin Tustin [mailto:mtus...@handybook.com<javascript:_e(%7B%7D,'cvml','mtus...@handybook.com');>] Sent: 28 April 2016 12:08 To: Deligiannis, Ioannis (UK) Cc: dev@spark.apache.org<javascript:_e(%7B%7D,'cvml','dev@spark.apache.org');> Subject: Re: RDD.broadcast Why would you ever need to do this? I'm genuinely curious. I view collects as being solely for interactive work. On Thursday, April 28, 2016, <ioannis.deligian...@nomura.com<javascript:_e(%7B%7D,'cvml','ioannis.deligian...@nomura.com');>> wrote: Hi, It is a common pattern to process an RDD, collect (typically a subset) to the driver and then broadcast back. Adding an RDD method that can do that using the torrent broadcast mechanics would be much more efficient. In addition, it would not require the Driver to also utilize its Heap holding this broadcast. I guess this can become complicated if the resulting broadcast is required to keep lineage information, but assuming a torrent distribution, once the broadcast is synced then lineage would not be required. I’d also expect the call to rdd.brodcast to be an action that eagerly distributes the broadcast and returns when the operation has succeeded. Is this something that could be implemented or are there any reasons that prohibits this? Thanks Ioannis This e-mail (including any attachments) is private and confidential, may contain proprietary or privileged information and is intended for the named recipient(s) only. Unintended recipients are strictly prohibited from taking action on the basis of information in this e-mail and must contact the sender immediately, delete this e-mail (and all attachments) and destroy any hard copies. 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