HI Sriguru, Thank you for the tips. Just to clarify a few things.
Our machines have 32 GB of RAM. I'm planning on setting each machine to run 12 mappers and 2 reducers with the heap size set to 2048MB so total memory usage for the heap at 28GB. If this is the case should io.sort.mb be set to 70% of 2048MB (so ~1400 MB)? Also, I did not see a fs.inmemorysize.mb setting in any of the hadoop configuration files. Is that the correct setting I should be looking for? Should this also be set to 70% of the heap size or does it need to share with the io.sort.mb setting. I assume if I'm bumping up io.sort.mb that much I also need to increase io.sort.factor from the default of 10. Is there a recommended relation between these two? Thank you for your help! ~Ed On Sun, Sep 26, 2010 at 3:05 AM, Srigurunath Chakravarthi < [email protected]> wrote: > Ed, > Tuning io.sort.mb will be certainly worthwhile if you have enough RAM to > allow for a higher Java heap per map task without risking swapping. > > Similarly, you can decrease spills on the reduce side using > fs.inmemorysize.mb. > > You can use the following thumb rules for tuning those two: > > - Set these to ~70% of Java heap size. Pick heap sizes to utilize ~80% RAM > across all processes (maps, reducers, TT, DN, other) > - Set it small enough to avoid swap activity, but > - Set it large enough to minimize disk spills. > - Ensure that io.sort.factor is set large enough to allow full use of > buffer space. > - Balance space for output records (default 95%) & record meta-data (5%). > Use io.sort.spill.percent and io.sort.record.percent > > Your mileage may vary. We've seen job exec time improvements worth 1-3% > via spill-avoidance for miscellaneous applications. > > Your other option of running a map per 32MB or 64MB of input should give > you better performance if your map task execution time is significant (i.e., > much larger than a few seconds) compared to the overhead of launching map > tasks and reading input. > > Regards, > Sriguru > > >-----Original Message----- > >From: pig [mailto:[email protected]] > >Sent: Saturday, September 25, 2010 2:36 AM > >To: [email protected] > >Subject: Proper blocksize and io.sort.mb setting when using compressed > >LZO files > > > >Hello, > > > >We just recently switched to using lzo compressed file input for our > >hadoop > >cluster using Kevin Weil's lzo library. The files are pretty uniform > >in > >size at around 200MB compressed. Our block size is 256MB. > >Decompressed the > >average LZO input file is around 1.0GB. I noticed lots of our jobs are > >now > >spilling lots of data to disk. We have almost 3x more spilled records > >than > >map input records for example. I'm guessing this is because each > >mapper is > >getting a 200 MB lzo file which decompresses into 1GB of data per > >mapper. > > > >Would you recommend solving this by reducing the block size to 64MB, or > >even > >32MB and then using the LZO indexer so that a single 200MB lzo file is > >actually split among 3 or 4 mappers? Would it be better to play with > >the > >io.sort.mb value? Or, would it be best to play with both? Right now > >the > >io.sort.mb value is the default 200MB. Have other lzo users had to > >adjust > >their block size to compensate for the "expansion" of the data after > >decompression? > > > >Thank you for any help! > > > >~Ed >
