If it is really a Cartesian product, there is no better way other than increasing the timeout for the reducers. You can do a back-of-the-envelope calculation on how long it takes (e.g., in your log it shows it takes 19 sec. to get 6 million rows out of the join). This calculation can also be done if it is not a Cartesian product, and you have an good estimate of how many rows will be produced.
In general, joining two huge tables can be avoided by partitioning the fact tables, so that you don't need to join the whole table. BTW, are you joining two fact tables or one dimension table is just huge? Thanks, Ning On Oct 26, 2009, at 5:16 AM, Ryan LeCompte wrote: So it turns out that the JOIN key of my query basically results in a match/join on all rows of each table! There really is no extra filtering that I can do to exclude invalid rows, etc. The mappers fly by and complete, but the reducers are just moving extremely slowly (my guess due to what Zheng said about the Cartesian product of all rows getting matched). Is there some other way that I could re-write the JOIN or is my only option to increase the timeout on the task trackers so that they don't timeout/kill the reducers? I've already upped their timeouts to 30 minutes (as opposed to the default of 10), and it doesn't seem to be sufficient... Again, this is joining a 33GB table with a 13GB table where join key is shared by virtually all rows in both tables. Thanks, Ryan On Mon, Oct 26, 2009 at 7:35 AM, Ryan LeCompte <[email protected]<mailto:[email protected]>> wrote: Thanks guys, very useful information. I will modify my query a bit and get back to you guys on whether it worked or not. Thanks, Ryan On Mon, Oct 26, 2009 at 4:34 AM, Chris Bates <[email protected]<mailto:[email protected]>> wrote: Ryan, I asked this question a couple days ago but in a slightly different form. What you have to do is make sure the table you're joining is smaller than the leftmost table. As an example, SELECT COUNT(DISTINCT UT.UserID) FROM usertracking UT JOIN streamtransfers ST ON (ST.usertrackingid = UT.usertrackingid) WHERE UT.UserID IS NOT NULL AND UT.UserID <> 0; In this query, usertracking is a table that is about 8 or 9 gigs. Streamtransfers is a table that is about 4 gigs. As per Zheng's comment, I omitted UserID's of Null or Zero as there are many rows with this key and the join worked as intended. Chris PS. As an aside, Hive is proving to be quite useful to all of our database hackers here at Grooveshark. Thanks to everyone who has committed...I hope to contribute soon. On Mon, Oct 26, 2009 at 2:08 AM, Zheng Shao <[email protected]<mailto:[email protected]>> wrote: It's probably caused by the Cartesian product of many rows from the two tables with the same key. Zheng On Sun, Oct 25, 2009 at 7:22 PM, Ryan LeCompte <[email protected]<mailto:[email protected]>> wrote: It also looks like the reducers just never stop outputting things likethe (following -- see below), causing them to ultimately time out and get killed by the system. 2009-10-25 22:21:18,879 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 100000000 rows 2009-10-25 22:21:22,009 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 101000000 rows 2009-10-25 22:21:22,010 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 101000000 rows 2009-10-25 22:21:25,141 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 102000000 rows 2009-10-25 22:21:25,142 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 102000000 rows 2009-10-25 22:21:28,263 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 103000000 rows 2009-10-25 22:21:28,263 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 103000000 rows 2009-10-25 22:21:31,387 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 104000000 rows 2009-10-25 22:21:31,387 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 104000000 rows 2009-10-25 22:21:34,510 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 105000000 rows 2009-10-25 22:21:34,510 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 105000000 rows 2009-10-25 22:21:37,633 INFO org.apache.hadoop.hive.ql.exec.JoinOperator: 4 forwarding 106000000 rows 2009-10-25 22:21:37,633 INFO org.apache.hadoop.hive.ql.exec.SelectOperator: 5 forwarding 106000000 rows On Sun, Oct 25, 2009 at 9:39 PM, Ryan LeCompte <[email protected]<mailto:[email protected]>> wrote: Hello all, Should I expect to be able to do a Hive JOIN between two tables that have about 10 or 15GB of data each? What I'm noticing (for a simple JOIN) is that all the map tasks complete, but the reducers just hang at around 87% or so (for the first set of 4 reducers), and then they eventually just get killed due to inability to respond by the cluster. I can do a JOIN between a large table and a very small table of 10 or so records just fine. Any thoughts? Thanks, Ryan -- Yours, Zheng
