So was the point of breaking into 36 parts to bring each row to the 64 or 128mb threshold?
On Tue, Jul 9, 2013 at 3:18 AM, Theo Hultberg <t...@iconara.net> wrote: > We store objects that are a couple of tens of K, sometimes 100K, and we > store quite a few of these per row, sometimes hundreds of thousands. > > One problem we encountered early was that these rows would become so big > that C* couldn't compact the rows in-memory and had to revert to slow > two-pass compactions where it spills partially compacted rows to disk. we > solved that in two ways, first by > increasing in_memory_compaction_limit_in_mb from 64 to 128, and although it > helped a little bit we quickly realized didn't have much effect because > most of the time was taken up by really huge rows many times larger than > that. > > We ended up implementing a simple sharding scheme where each row is > actually 36 rows that each contain 1/36 of the range (we take the first > letter in the column key and stick that on the row key on writes, and on > reads we read all 36 rows -- 36 because there are 36 letters and numbers in > the ascii alphabet and our column keys happen to distribute over that quite > nicely). > > Cassandra works well with semi-large objects, and it works well with wide > rows, but you have to be careful about the combination where rows get > larger than 64 Mb. > > T# > > > On Mon, Jul 8, 2013 at 8:13 PM, S Ahmed <sahmed1...@gmail.com> wrote: > >> Hi Peter, >> >> Can you describe your environment, # of documents and what kind of usage >> pattern you have? >> >> >> >> >> On Mon, Jul 8, 2013 at 2:06 PM, Peter Lin <wool...@gmail.com> wrote: >> >>> I regularly store word and pdf docs in cassandra without any issues. >>> >>> >>> >>> >>> On Mon, Jul 8, 2013 at 1:46 PM, S Ahmed <sahmed1...@gmail.com> wrote: >>> >>>> I'm guessing that most people use cassandra to store relatively smaller >>>> payloads like 1-5kb in size. >>>> >>>> Is there anyone using it to store say 100kb (1/10 of a megabyte) and if >>>> so, was there any tweaking or gotchas that you ran into? >>>> >>> >>> >> >
