You can find this info yourself, Dejan 1. Locate table dir on HDFS 2. List all regions (directories) 3. Iterate files in each directory and find the oldest one (creation time) 4. The region with the oldest file is your candidate for major compaction
/HBASE_ROOT/data/namespace/table/region (If my memory serves me right :)) -Vlad On Wed, Jul 8, 2015 at 1:07 PM, Dejan Menges <dejan.men...@gmail.com> wrote: > Hi Mikhail, > > Actually, reason is quite stupid on my side - to avoid compacting one > region over and over again while others are waiting in line (reading HTML > and sorting only on number of store files gets you at some point having > bunch of regions having exactly the same number of store files). > > Thanks for this hint - this is exactly something I was looking for. Was > trying previously to figure out if it's possible to query meta for this > information (using currently 0.98.0, 0.98.4 and waiting for HDP 2.3 from > Hortonworks to upgrade immediately) but for our current version didn't > found that possible, that's why I decided going this way. > > On Wed, Jul 8, 2015 at 10:02 PM Mikhail Antonov <olorinb...@gmail.com> > wrote: > > > I totally understand the reasoning behind compacting regions with > > biggest number of store files, but didn't follow why it's best to > > compact regions which have biggest store files, maybe I'm missing > > something? I'd maybe compact regions which have the smallest avg > > storefile size? > > > > You may also want to take a look at > > https://issues.apache.org/jira/browse/HBASE-12859, and compact regions > > for which MC was last run longer time ago. > > > > -Mikhail > > > > On Wed, Jul 8, 2015 at 10:30 AM, Dejan Menges <dejan.men...@gmail.com> > > wrote: > > > Hi Behdad, > > > > > > Thanks a lot, but this part I do already. My question was more what to > > use > > > to most intelligently (what exposed or not exposed metrics) figure out > > > where major compaction is needed the most. > > > > > > Currently, choosing the region which has biggest number of store files > + > > > the biggest amount of store files is doing the job, but wasn't sure if > > > there's maybe something better so far to choose from. > > > > > > Cheers, > > > Dejan > > > > > > On Wed, Jul 8, 2015 at 7:19 PM Behdad Forghani <beh...@exapackets.com> > > > wrote: > > > > > >> To start major compaction for tablename from cli, you need to run: > > >> echo major_compact tablename | hbase shell > > >> > > >> I do this after bulk loading to the table. > > >> > > >> FYI, to avoid surprises, I also turn off load balancer and rebalance > > >> regions manually. > > >> > > >> The cli command to turn off balancer is: > > >> echo balance_switch false | hbase shell > > >> > > >> To rebalance regions after a bulk load or other changes, run: > > >> echo balance | hbase shell > > >> > > >> You can run these two command using ssh. I use Ansible to do these. > > >> Assuming you have defined hbase_master in your hosts file, you can > run: > > >> ansible -i hosts hbase_master -a "echo major_compact tablename | hbase > > >> shell" > > >> > > >> Behdad Forghani > > >> > > >> On Wed, Jul 8, 2015 at 8:03 AM, Dejan Menges <dejan.men...@gmail.com> > > >> wrote: > > >> > > >> > Hi, > > >> > > > >> > What's the best way to automate major compactions without enabling > it > > >> > during off peak period? > > >> > > > >> > What I was testing is simple script which runs on every node in > > cluster, > > >> > checks if there is major compaction already running on that node, if > > not > > >> > picks one region for compaction and run compaction on that one > region. > > >> > > > >> > It's running for some time and it helped us get our data to much > > better > > >> > shape, but now I'm not quite sure how to choose anymore which region > > to > > >> > compact. So far I was reading for that node > rs-status#regionStoreStats > > >> and > > >> > first choosing the one with biggest amount of storefiles, and then > > those > > >> > with biggest storefile sizes. > > >> > > > >> > Is there maybe something more intelligent I could/should do? > > >> > > > >> > Thanks a lot! > > >> > > > >> > > > > > > > > -- > > Thanks, > > Michael Antonov > > >
