Do you mean two partitions per server? In my case, that would correspond to 30 total rows which would make each row very large ... >1G/row. Should I increase the table.split.threshold in a corresponding way?
On Fri, Nov 9, 2012 at 1:09 PM, John Vines <[email protected]> wrote: > Glad to hear. I typically advice a minimum of 2 shards per tserver. I > would say the maximum is actually based on the tablet size. Others in the > country may disagree/provide better reasoning. > > Sent from my phone, pardon the typos and brevity. > On Nov 9, 2012 1:03 PM, "Anthony Fox" <[email protected]> wrote: > >> Ok, I reingested with 1000 rows and performance for both single record >> scans and index scans is much better. I'm going to experiment a bit with >> the optimal number of rows. Thanks for the help, everyone. >> >> >> On Fri, Nov 9, 2012 at 12:41 PM, John Vines <[email protected]> wrote: >> >>> The bloom filter checks only occur on a seek, and the way the column >>> family filter works it's it seeks and then does a few scans to see if the >>> appropriate families pop up in the short term. Bloom filter on the column >>> family would be better if you had larger rows to encourage more >>> seeks/minimize the number of rows to do bloom checks. >>> >>> The issue is that you are ultimately checking every single row for a >>> column, which is sparse. It's not that different than doing a full table >>> regex. If you had locality groups set up it would be more performant, until >>> you create locality groups for everything. >>> >>> The intersecting iterators get their performance by being able to >>> operate on large rows to avoid the penalty of checking each row. Minimize >>> the number of partitions you have and it should clear up your issues. >>> >>> John >>> >>> Sent from my phone, pardon the typos and brevity. >>> On Nov 9, 2012 12:24 PM, "William Slacum" < >>> [email protected]> wrote: >>> >>>> I'll ask for someone to verify this comment for me (look @ u John W >>>> Vines), but the bloom filter helps when you have a discrete number of >>>> column families that will appear across many rows. >>>> >>>> On Fri, Nov 9, 2012 at 12:18 PM, Anthony Fox <[email protected]>wrote: >>>> >>>>> Ah, ok, I was under the impression that this would be really fast >>>>> since I have a column family bloom filter turned on. Is this not correct? >>>>> >>>>> >>>>> On Fri, Nov 9, 2012 at 12:15 PM, William Slacum < >>>>> [email protected]> wrote: >>>>> >>>>>> When I said smaller of tablets, I really mean smaller number of rows >>>>>> :) My apologies. >>>>>> >>>>>> So if you're searching for a random column family in a table, like >>>>>> with a `scan -c <cf>` in the shell, it will start at row 0 and work >>>>>> sequentially up to row 10000000 until it finds the cf. >>>>>> >>>>>> >>>>>> On Fri, Nov 9, 2012 at 12:11 PM, Anthony Fox <[email protected]>wrote: >>>>>> >>>>>>> This scan is without the intersecting iterator. I'm just trying to >>>>>>> pull back a single data record at the moment which corresponds to >>>>>>> scanning >>>>>>> for one column family. I'll try with a smaller number of tablets, but >>>>>>> is >>>>>>> the computation effort the same for the scan I am doing? >>>>>>> >>>>>>> >>>>>>> On Fri, Nov 9, 2012 at 12:02 PM, William Slacum < >>>>>>> [email protected]> wrote: >>>>>>> >>>>>>>> So that means you have roughly 312.5k rows per tablet, which means >>>>>>>> about 725k column families in any given tablet. The intersecting >>>>>>>> iterator >>>>>>>> will work at a row per time, so I think at any given moment, it will be >>>>>>>> working through 32 at a time and doing a linear scan through the RFile >>>>>>>> blocks. With RFile indices, that check is usually pretty fast, but >>>>>>>> you're >>>>>>>> having go through 4 orders of magnitude more data sequentially than >>>>>>>> you can >>>>>>>> work on. If you can experiment and re-ingest with a smaller number of >>>>>>>> tablets, anywhere between 15 and 45, I think you will see better >>>>>>>> performance. >>>>>>>> >>>>>>>> On Fri, Nov 9, 2012 at 11:53 AM, Anthony Fox >>>>>>>> <[email protected]>wrote: >>>>>>>> >>>>>>>>> Failed to answer the original question - 15 tablet servers, 32 >>>>>>>>> tablets/splits. >>>>>>>>> >>>>>>>>> >>>>>>>>> On Fri, Nov 9, 2012 at 11:52 AM, Anthony Fox <[email protected] >>>>>>>>> > wrote: >>>>>>>>> >>>>>>>>>> I've tried a number of different settings of >>>>>>>>>> table.split.threshold. I started at 1G and bumped it down to 128M >>>>>>>>>> and the >>>>>>>>>> cf scan is still ~30 seconds for both. I've also used less rows - >>>>>>>>>> 00000 to >>>>>>>>>> 99999 and still see similar performance numbers. I thought the >>>>>>>>>> column >>>>>>>>>> family bloom filter would help deal with large row space but sparsely >>>>>>>>>> populated column space. Is that correct? >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> On Fri, Nov 9, 2012 at 11:49 AM, William Slacum < >>>>>>>>>> [email protected]> wrote: >>>>>>>>>> >>>>>>>>>>> I'm more inclined to believe it's because you have to search >>>>>>>>>>> across 10M different rows to find any given column family, since >>>>>>>>>>> they're >>>>>>>>>>> randomly, and possibly uniformly, distributed. How many tablets are >>>>>>>>>>> you >>>>>>>>>>> searching across? >>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>>> On Fri, Nov 9, 2012 at 11:45 AM, Anthony Fox < >>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>> >>>>>>>>>>>> Yes, there are 10M possible partitions. I do not have a hash >>>>>>>>>>>> from value to partition, the data is essentially randomly balanced >>>>>>>>>>>> across >>>>>>>>>>>> all the tablets. Unlike the bloom filter and intersecting iterator >>>>>>>>>>>> examples, I do not have locality groups turned on and I have data >>>>>>>>>>>> in the cq >>>>>>>>>>>> and the value for both index entries and record entries. Could >>>>>>>>>>>> this be the >>>>>>>>>>>> issue? Each record entry has approximately 30 column qualifiers >>>>>>>>>>>> with data >>>>>>>>>>>> in the value for each. >>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:41 AM, William Slacum < >>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>> >>>>>>>>>>>>> I guess assuming you have 10M possible partitions, if you're >>>>>>>>>>>>> using a relatively uniform hash to generate your IDs, you'll >>>>>>>>>>>>> average about >>>>>>>>>>>>> 2 per partition. Do you have any index for term/value to >>>>>>>>>>>>> partition? This >>>>>>>>>>>>> will help you narrow down your search space to a subset of your >>>>>>>>>>>>> partitions. >>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:39 AM, William Slacum < >>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>> >>>>>>>>>>>>>> That shouldn't be a huge issue. How many rows/partitions do >>>>>>>>>>>>>> you have? How many do you have to scan to find the specific >>>>>>>>>>>>>> column >>>>>>>>>>>>>> family/doc id you want? >>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>>> On Fri, Nov 9, 2012 at 11:26 AM, Anthony Fox < >>>>>>>>>>>>>> [email protected]> wrote: >>>>>>>>>>>>>> >>>>>>>>>>>>>>> I have a table set up to use the intersecting iterator pattern. >>>>>>>>>>>>>>> The >>>>>>>>>>>>>>> table has about 20M records which leads to 20M column families >>>>>>>>>>>>>>> for the >>>>>>>>>>>>>>> data section - 1 unique column family per record. The index >>>>>>>>>>>>>>> section of >>>>>>>>>>>>>>> the table is not quite as large as the data section. The >>>>>>>>>>>>>>> rowkey is a >>>>>>>>>>>>>>> random padded integer partition between 0000000 and 9999999. I >>>>>>>>>>>>>>> turned >>>>>>>>>>>>>>> bloom filters on and used the ColumnFamilyFunctor to get >>>>>>>>>>>>>>> performant >>>>>>>>>>>>>>> column family scans without specifying a range like in the >>>>>>>>>>>>>>> bloom filter >>>>>>>>>>>>>>> examples in the README. However, my column family scans >>>>>>>>>>>>>>> (without any >>>>>>>>>>>>>>> custom iterator) are still fairly slow - ~30 seconds for a >>>>>>>>>>>>>>> column family >>>>>>>>>>>>>>> batch scan of one record. I've also tried RowFunctor but I see >>>>>>>>>>>>>>> similar >>>>>>>>>>>>>>> performance. Can anyone shed any light on the performance >>>>>>>>>>>>>>> metrics I'm >>>>>>>>>>>>>>> seeing? >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> Thanks, >>>>>>>>>>>>>>> Anthony >>>>>>>>>>>>>>> >>>>>>>>>>>>>>> >>>>>>>>>>>>>> >>>>>>>>>>>>> >>>>>>>>>>>> >>>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>>> >>>>>>> >>>>>> >>>>> >>>> >>
