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From Dylan Hutchison <dhutc...@cs.washington.edu>
Subject Re: Accumulo Seek performance
Date Wed, 14 Sep 2016 13:52:42 GMT
Do we have a (hopefully reproducible) conclusion from this thread,
regarding Scanners and BatchScanners?

On Sep 13, 2016 11:17 PM, "Josh Elser" <josh.elser@gmail.com> wrote:

> Yeah, this seems to have been osx causing me grief.
>
> Spun up a 3tserver cluster (on openstack, even) and reran the same
> experiment. I could not reproduce the issues, even without substantial
> config tweaking.
>
> Josh Elser wrote:
>
>> I'm playing around with this a little more today and something is
>> definitely weird on my local machine. I'm seeing insane spikes in
>> performance using Scanners too.
>>
>> Coupled with Keith's inability to repro this, I am starting to think
>> that these are not worthwhile numbers to put weight behind. Something I
>> haven't been able to figure out is quite screwy for me.
>>
>> Josh Elser wrote:
>>
>>> Sven, et al:
>>>
>>> So, it would appear that I have been able to reproduce this one (better
>>> late than never, I guess...). tl;dr Serially using Scanners to do point
>>> lookups instead of a BatchScanner is ~20x faster. This sounds like a
>>> pretty serious performance issue to me.
>>>
>>> Here's a general outline for what I did.
>>>
>>> * Accumulo 1.8.0
>>> * Created a table with 1M rows, each row with 10 columns using YCSB
>>> (workloada)
>>> * Split the table into 9 tablets
>>> * Computed the set of all rows in the table
>>>
>>> For a number of iterations:
>>> * Shuffle this set of rows
>>> * Choose the first N rows
>>> * Construct an equivalent set of Ranges from the set of Rows, choosing a
>>> random column (0-9)
>>> * Partition the N rows into X collections
>>> * Submit X tasks to query one partition of the N rows (to a thread pool
>>> with X fixed threads)
>>>
>>> I have two implementations of these tasks. One, where all ranges in a
>>> partition are executed via one BatchWriter. A second where each range is
>>> executed in serial using a Scanner. The numbers speak for themselves.
>>>
>>> ** BatchScanners **
>>> 2016-09-10 17:51:38,811 [joshelser.YcsbBatchScanner] INFO : Shuffled all
>>> rows
>>> 2016-09-10 17:51:38,843 [joshelser.YcsbBatchScanner] INFO : All ranges
>>> calculated: 3000 ranges found
>>> 2016-09-10 17:51:38,846 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:52:19,025 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 40178 ms
>>> 2016-09-10 17:52:19,025 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:53:01,321 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 42296 ms
>>> 2016-09-10 17:53:01,321 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:53:47,414 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 46094 ms
>>> 2016-09-10 17:53:47,415 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:54:35,118 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 47704 ms
>>> 2016-09-10 17:54:35,119 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:55:24,339 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 49221 ms
>>>
>>> ** Scanners **
>>> 2016-09-10 17:57:23,867 [joshelser.YcsbBatchScanner] INFO : Shuffled all
>>> rows
>>> 2016-09-10 17:57:23,898 [joshelser.YcsbBatchScanner] INFO : All ranges
>>> calculated: 3000 ranges found
>>> 2016-09-10 17:57:23,903 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:57:26,738 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 2833 ms
>>> 2016-09-10 17:57:26,738 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:57:29,275 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 2536 ms
>>> 2016-09-10 17:57:29,275 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:57:31,425 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 2150 ms
>>> 2016-09-10 17:57:31,425 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:57:33,487 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 2061 ms
>>> 2016-09-10 17:57:33,487 [joshelser.YcsbBatchScanner] INFO : Executing 6
>>> range partitions using a pool of 6 threads
>>> 2016-09-10 17:57:35,628 [joshelser.YcsbBatchScanner] INFO : Queries
>>> executed in 2140 ms
>>>
>>> Query code is available
>>> https://github.com/joshelser/accumulo-range-binning
>>>
>>> Sven Hodapp wrote:
>>>
>>>> Hi Keith,
>>>>
>>>> I've tried it with 1, 2 or 10 threads. Unfortunately there where no
>>>> amazing differences.
>>>> Maybe it's a problem with the table structure? For example it may
>>>> happen that one row id (e.g. a sentence) has several thousand column
>>>> families. Can this affect the seek performance?
>>>>
>>>> So for my initial example it has about 3000 row ids to seek, which
>>>> will return about 500k entries. If I filter for specific column
>>>> families (e.g. a document without annotations) it will return about 5k
>>>> entries, but the seek time will only be halved.
>>>> Are there to much column families to seek it fast?
>>>>
>>>> Thanks!
>>>>
>>>> Regards,
>>>> Sven
>>>>
>>>>

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