Also, how big are the files in each directory? Are they roughly the size of one HDFS block or a multiple. Lots of small files will mean lots of mapper tasks will little to do.
You can also compare the job tracker console output for each job. I bet the slow one has a lot of very short map and reduce tasks, while the faster one has fewer tasks that run longer. A rule of thumb is that any one task should take 20 seconds or more to amortize over the few seconds spent in start up per task. In other words, if you think about what's happening at the HDFS and MR level, you can learn to predict how fast or slow things will run. Learning to read the output of EXPLAIN or EXPLAIN EXTENDED helps with this. dean On Thu, Apr 4, 2013 at 6:25 PM, Owen O'Malley <omal...@apache.org> wrote: > See slide #9 from my Optimizing Hive Queries talk > http://www.slideshare.net/oom65/optimize-hivequeriespptx . Certainly, we > will improve it, but for now you are much better off with 1,000 partitions > than 10,000. > > -- Owen > > > On Thu, Apr 4, 2013 at 4:21 PM, Ramki Palle <ramki.pa...@gmail.com> wrote: > >> Is it possible for you to send the explain plan of these two queries? >> >> Regards, >> Ramki. >> >> >> On Thu, Apr 4, 2013 at 4:06 PM, Sanjay Subramanian < >> sanjay.subraman...@wizecommerce.com> wrote: >> >>> The slow down is most possibly due to large number of partitions. >>> I believe the Hive book authors tell us to be cautious with large number >>> of partitions :-) and I abide by that. >>> >>> Users >>> Please add your points of view and experiences >>> >>> Thanks >>> sanjay >>> >>> From: Ian <liu...@yahoo.com> >>> Reply-To: "user@hive.apache.org" <user@hive.apache.org>, Ian < >>> liu...@yahoo.com> >>> Date: Thursday, April 4, 2013 4:01 PM >>> To: "user@hive.apache.org" <user@hive.apache.org> >>> Subject: Partition performance >>> >>> Hi, >>> >>> I created 3 years of hourly log files (totally 26280 files), and use >>> External Table with partition to query. I tried two partition methods. >>> >>> 1). Log files are stored as /test1/2013/04/02/16/000000_0 (A directory >>> per hour). Use date and hour as partition keys. Add 3 years of directories >>> to the table partitions. So there are 26280 partitions. >>> CREATE EXTERNAL TABLE test1 (logline string) PARTITIONED BY (dt >>> string, hr int); >>> ALTER TABLE test1 ADD PARTITION (dt='2013-04-02', hr=16) >>> LOCATION '/test1/2013/04/02/16'; >>> >>> 2). Log files are stored as /test2/2013/04/02/16_000000_0 (A directory >>> per day, 24 files in each directory). Use date as partition key. Add 3 >>> years of directories to the table partitions. So there are 1095 partitions. >>> CREATE EXTERNAL TABLE test2 (logline string) PARTITIONED BY >>> (dt string); >>> ALTER TABLE test2 ADD PARTITION (dt='2013-04-02') LOCATION >>> '/test2/2013/04/02'; >>> >>> When doing a simple query like >>> SELECT * FROM test1/test2 WHERE dt >= '2013-02-01' and dt <= >>> '2013-02-14' >>> Using approach #1 takes 320 seconds, but #2 only takes 70 seconds. >>> >>> I'm wondering why there is a big performance difference between these >>> two? These two approaches have the same number of files, only the directory >>> structure is different. So Hive is going to load the same amount of files. >>> Why does the number of partitions have such big impact? Does that mean #2 >>> is a better partition strategy? >>> >>> Thanks. >>> >>> >>> >>> CONFIDENTIALITY NOTICE >>> ====================== >>> This email message and any attachments are for the exclusive use of the >>> intended recipient(s) and may contain confidential and privileged >>> information. Any unauthorized review, use, disclosure or distribution is >>> prohibited. If you are not the intended recipient, please contact the >>> sender by reply email and destroy all copies of the original message along >>> with any attachments, from your computer system. If you are the intended >>> recipient, please be advised that the content of this message is subject to >>> access, review and disclosure by the sender's Email System Administrator. >>> >> >> > -- *Dean Wampler, Ph.D.* thinkbiganalytics.com +1-312-339-1330
