I think this is the same issue I found for ACCUMULO-3710 <https://issues.apache.org/jira/browse/ACCUMULO-3710>, only in my case the tserver ran out of memory. Accumulo doesn't handle large numbers of small, disjoint ranges well. I bet there's room for improvement on both the client and tablet server. ~Dylan
On Wed, May 13, 2015 at 3:13 PM, Eric Newton <eric.new...@gmail.com> wrote: > Yes, hot-spotting does affect accumulo because you have fewer servers and > caches handling your request. > > Let's say your data is spread out, in a normal distribution from "0".."9". > > What if you have only 1 split? You would want it at "5", to divide the > data in half, and you could host the halves on different servers. But if > you split at 1, now 10% of your queries go to one tablet, and 90% go to the > other. > > -Eric > > > On Wed, May 13, 2015 at 1:56 PM, vaibhav thapliyal < > vaibhav.thapliyal...@gmail.com> wrote: > >> Thank you Eric. I will surely do the same. Should uneven distribution >> across the tablets affect querying in accumulo? If this case, it is. Is >> this behaviour normal? >> On 13-May-2015 10:58 pm, "Eric Newton" <eric.new...@gmail.com> wrote: >> >>> Yes, that's a great way to split the data evenly. >>> >>> Also, since the data set is so small, turn on data caching for your >>> table: >>> >>> shell> config -t mytable -s table.cache.block.enable=true >>> >>> You may want to increase the size of your tserver JVM, and increase the >>> size of the cache: >>> >>> shell> config -s tserver.cache.data.size=1G >>> >>> This will help with repeated random look-ups. >>> >>> -Eric >>> >>> On Wed, May 13, 2015 at 11:31 AM, vaibhav thapliyal < >>> vaibhav.thapliyal...@gmail.com> wrote: >>> >>>> Thank you Eric. >>>> >>>> One thing I would like to know. Does pre-splitting the data play a part >>>> in querying accumulo? >>>> >>>> Because I managed to somewhat decrease the querying time. >>>> I did the following steps: >>>> My table was around 1.47gb so I explicity set the split parameter to >>>> 256mb instead of the default 1gb. >>>> >>>> So I had just 8 tablets. Now when I carried out the same query, it >>>> finished in 15s. >>>> >>>> Is it because of the split points are more evenly distributed? >>>> >>>> The previous table on which the query took 50s had entries unevenly >>>> distributed across the tablets. >>>> Thanks >>>> Vaibhav >>>> On 13-May-2015 7:43 pm, "Eric Newton" <eric.new...@gmail.com> wrote: >>>> >>>>> This use case is one of the things Accumulo was designed to handle >>>>> well. It's the reason there is a BatchScanner. >>>>> >>>>> I've created: >>>>> >>>>> https://issues.apache.org/jira/browse/ACCUMULO-3813 >>>>> >>>>> so we can investigate and track down any problems or improvements. >>>>> >>>>> Feel free to add any other details to the JIRA ticket. >>>>> >>>>> -Eric >>>>> >>>>> >>>>> On Wed, May 13, 2015 at 10:03 AM, Emilio Lahr-Vivaz < >>>>> elahrvi...@ccri.com> wrote: >>>>> >>>>>> It sounds like each of your ranges is an ID, e.g. a single row. I've >>>>>> found that scanning lots of non-sequential single-row ranges is pretty >>>>>> slow >>>>>> in accumulo. Your best approach is probably to create an index table on >>>>>> whatever you are originally trying to query (assuming those 10000 ids >>>>>> came >>>>>> from some other query). >>>>>> >>>>>> Thanks, >>>>>> >>>>>> Emilio >>>>>> >>>>>> >>>>>> On 05/13/2015 09:14 AM, vaibhav thapliyal wrote: >>>>>> >>>>>> The rf files per tablet vary between 2 to 5 per tablet. The entries >>>>>> returned to me by the batchScanner is 460000. The approx. average data >>>>>> rate >>>>>> is 0.5 MB/s as seen on the accumulo monitor page. >>>>>> >>>>>> A simple scan on the table has an average data rate of about 7-8 >>>>>> MB/s. >>>>>> >>>>>> All the ids exist in the accumulo table. >>>>>> >>>>>> On 12 May 2015 at 23:39, Keith Turner <ke...@deenlo.com> wrote: >>>>>> >>>>>>> Do you know how much data is being brought back (i.e. 100 >>>>>>> megabytes)? I am wondering what the data rate is in MB/s. Do you know >>>>>>> how >>>>>>> many files per tablet you have? Do most of the 10,000 ids you are >>>>>>> querying >>>>>>> for exist? >>>>>>> >>>>>>> On Tue, May 12, 2015 at 1:58 PM, vaibhav thapliyal < >>>>>>> vaibhav.thapliyal...@gmail.com> wrote: >>>>>>> >>>>>>>> I have 194 tablets. Currently I am using 20 threads to create the >>>>>>>> batchscanner inside the createBatchScanner method. >>>>>>>> On 12-May-2015 11:19 pm, "Keith Turner" <ke...@deenlo.com> wrote: >>>>>>>> >>>>>>>>> How many tablets do you have? The batch scanner does not >>>>>>>>> parallelize operations within a tablet. >>>>>>>>> >>>>>>>>> If you give the batch scanner more threads than there are >>>>>>>>> tservers, it will make multilple parallel rpc calls to each tserver >>>>>>>>> if the >>>>>>>>> tserver has multiple tablets. Each rpc may include multiple tablets >>>>>>>>> and >>>>>>>>> ranges for each tablet. >>>>>>>>> >>>>>>>>> If the batch scanner has less threads than tservers, it will make >>>>>>>>> one rpc per tserver per thread. Each rpc call will include all >>>>>>>>> tablets and >>>>>>>>> associated ranges for that tserver. >>>>>>>>> >>>>>>>>> Keith >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> On Tue, May 12, 2015 at 1:39 PM, vaibhav thapliyal < >>>>>>>>> vaibhav.thapliyal...@gmail.com> wrote: >>>>>>>>> >>>>>>>>>> Hi, >>>>>>>>>> >>>>>>>>>> I am using BatchScanner to scan rows from a accumulo table. The >>>>>>>>>> table has around 187m entries and I am using a 3 node cluster which >>>>>>>>>> has >>>>>>>>>> accumulo 1.6.1. >>>>>>>>>> >>>>>>>>>> I have passed 10000 ids which are stored as row id in my table >>>>>>>>>> as a list in the setRanges() method. >>>>>>>>>> >>>>>>>>>> This whole process takes around 50 secs(from adding the ids in >>>>>>>>>> the list to scanning the whole table using the BatchScanner). >>>>>>>>>> >>>>>>>>>> I tried switching on bloom filters but that didn't work. >>>>>>>>>> >>>>>>>>>> Also if anyone could briefly explain how a BatchScanner works, >>>>>>>>>> how it does parallel scanning it would help me understand what I am >>>>>>>>>> doing >>>>>>>>>> better. >>>>>>>>>> >>>>>>>>>> Thanks >>>>>>>>>> Vaibhav >>>>>>>>>> >>>>>>>>>> >>>>>>>>>> >>>>>>>>> >>>>>>> >>>>>> >>>>>> >>>>> >>> >
