Thanks for clearing that out. I'm using your message to ping anyone who assist as to it appears the use case should happen to a lot of people?
Thanks! On Wednesday, November 20, 2013, Himanshu Vashishtha wrote: > Re: "The 32 limit makes HBase go into > stress mode, and dump all involving regions contains in those 32 WAL > Files." > > Pardon, I haven't read all your data points/details thoroughly, but the > above statement is not true. Rather, it looks at the oldest WAL file, and > flushes those regions which would free that WAL file. > > But I agree that in general with this kind of workload, we should handle > WAL files more intelligently and free up those WAL files which don't have > any dependency (that is, all their entries are already flushed) when > archiving. We do that in trunk but not in any released version, though. > > > > On Sat, Nov 16, 2013 at 11:16 AM, Asaf Mesika <asaf.mes...@gmail.com> > wrote: > > > First I forgot to mention that <customerId> in our case is > > MD5(<customerId>). > > In our case, we have so much data flowing in, that we end up having a > > region per <customerId><bucket> pretty quickly and even that, is splitted > > into different regions by specific date duration (timestamp). > > > > We're not witnessing a hotspot issue. I built some scripts in java and > awk, > > and saw that 66% of our customers use more than 1Rs. > > > > We have two main serious issues: primary and secondary. > > > > Our primary issue being the slow-region vs fast-region. First let's be > > reminded that a region represents as I detailed before a specific > > <customerId><bucket>. Some customers gets x50 times more data that other > > customers at a specific time frame (2hrs - 1 day). So in a one RS, we > have > > regions getting 10 write requests per hour, vs 50k write requests per > hour. > > So the region mapped to the slow-filling customer id, doesn't get to the > > 256MB flush limit and hence isn't flushed, while the regions mapped to > the > > fast-filling customer id, are flushing very quickly since they are > filling > > very quickly. > > Let's say the 1st WAL file contains the put of a slow-filling customerId. > > the fast-filling customerId, fills up the rest of that file. After 20-30 > > seconds, the file gets rolled, and another file fills up with fast > filling > > customerId. After a while, we get to 32 WAL Files. The 1st file wasn't > > deleted since its region wasn't flushed. The 32 limit makes HBase go into > > stress mode, and dump all involving regions contains in those 32 WAL > Files. > > In our case, we saw that it flushes 111 regions. Lots of the store files > > are 3k-3mb sized. So our compaction queue start filling up with those > store > > files needs to be compacted. > > At the of the road, the RS gets dead. > > > > Our secondary issue is those of empty regions - we get to a situation > where > > a region is mapped to a specific <customerId>, <bucket>, and date range > > (1/7 - 3/7). Those when we are in August (we TTL set to 30 days), those > > regions gets empty and will never get filled again. > > We assume this somehow wrecks havoc in the load balancer, and also MSLAB > > probably steals 1-2 GB of memory for those empty regions. > > > > Thanks! > > > > > > > > On Sat, Nov 16, 2013 at 7:25 PM, Mike Axiak <m...@axiak.net> wrote: > > > > > Hi, > > > > > > One new key pattern that we're starting to use is a salt based on a > > shard. > > > For example, let's take your key: > > > > > > <customerId><bucket><timestampInMs><uniqueId> > > > > > > Consider a shard between 0 and 15 inclusive. We determine this with: > > > > > > <shard> = abs(hash32(uniqueId) % 16) > > > > > > We can then define a salt to be based on customerId and the shard: > > > > > > <salt> = hash32(<shard><customerId>) > > > > > > So then the new key becomes: > > > > > > <salt><customerId><timestampInMs><uniqueId> > > > > > > This will distribute the data for a given customer across the N shards > > that > > > you pick, while having a deterministic function for a given row key (so > > > long as the # of shards you pick is fixed, otherwise you can migrate > the > > > data). Placing the bucket after the customerId doesn't help distribute > > the > > > single customer's data at all. Furthermore, by using a separate hash > > > (instead of just <shard><customerId>), you're guaranteeing that new > data > > > will appear in a somewhat random location (i.e., solving the problem of > > > adding a bunch of new data for a new customer). > > > > > > I have a key simulation script in python that I can start tweaking and > > > share with people if they'd like. > > > > > > Hope this helps, > > > Mike > > > > > > > > > On Sat, Nov 16, 2013 at 1:16 AM, Ted Yu <yuzhih...@gmail.com> wrote: > > > > > > > bq. all regions of that customer > > > > >
