> Actually, we don't have that many partitions - there are lot of gaps both in > days and time events as well.
Your partition description sounded a lot like one of the FAQs from Mithun's talks, which is why I asked http://www.slideshare.net/Hadoop_Summit/hive-at-yahoo-letters-from-the-trenches/24 > But, I would like to understand when you say " time spent might partly be > query planning with million partitions"? I presume, this is in producing the > physical plan? -- does it spend time in allocating group of partition > directories to each map task Yes, the physical planner is significant overhead, since all map-tasks get a list of all partitions and match each read against that list (partition schema can evolve, this is closer to O(n^2)) & the split-generation is bottlenecked by the total # of files involved in the operation (a slow O(n) operation is still slow). The overhead in maintaining partitions is fairly high & the entire query planning will try to do something like a du -sh on each partition, if all the basic file statistics are missing etc. Also, if you have >100k partitions, disabling the metadata optimizer (hive.optimize.metadataonly=false) would be a good thing, since that codepath is single threaded, while a compute-heavy full-table scan is much faster due to parallelism - it might burn more CPU, but it would come back in less than an hour. You might want to opt for daily partitions & also run the stats gathering ops with "analyze table <table> compute statistics partialscan;" & "analyze table <table> compute statistics for columns" to speed up further queries. At least in my experience with ORC + valid stats, a query like "select count(*) from table" should takes <500 milliseconds. Cheers, Gopal