The random_read_test used to score 4k qps for comparable benchmark (vs
1.2k qps in the bigtable paper, note the 10k qps number is for
memtable, which is very different from regular table. In hypertable
you can use the IN MEMORY access group option to get memtable) The
regular table scanner needs to merge scan cell cache, cell stores, so
it's much more expensive than memtable scanner which just scan the
cellcache, regardless the size of the table.

Doug pushed out .11, which contains major changes to the way cellcache
and compaction works before he went on a vacation. There might be a
performance regression in recent releases. Thanks for the note, we'll
look into it.

__Luke

On Oct 16, 9:59 pm, Alex <[EMAIL PROTECTED]> wrote:
> Hi All,
>
> Could somebody explain/describe CellCache allocation/eviction policy?
>
> After running random read/write tests, I came to the conclusion that
> CellCache operation is very different from what is described in Google
> BigTable paper, i.e. CellCache works as a write buffer rather than a
> cache. CellCache seems to help a lot for optimizing writes but it
> doesn't help reads.
>
> Here are the results:
>
> random_write_test 100000000
>
>   Elapsed time:  8.16 s
>  Total inserts:  100000
>     Throughput:  12403559.87 bytes/s
>     Throughput:  12256.48 inserts/s
>
> random_read_test 100000000
>
>   Elapsed time:  1038.47 s
>  Total scanned:  100000
>     Throughput:  97451.43 bytes/s
>     Throughput:  96.30 scanned cells/s
>
> Random read speed is ~100x slower than the result in Google BigTable
> for random read test which fits in the memory. In this case the data
> set size should be ~100MB and should comfortably fit in the DRAM
> (8GB).
>
> Also, tcmalloc heap profiling shows that the usage memory actually
> decreases to ~50MB during random read test while it is >700MB during
> random write test (although top instead shows increase).
>
> I apologize if I am missing something very basic, I have very little
> experience in this area.
>
> Thanks,
> Alex
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