Ignore what I said and see https://forums.aws.amazon.com/thread.jspa?threadID=51232
bzip2 was documented somewhere as being splittable but this appears to not actually be implemented at least in AWS S3. /a On Mon, Jun 10, 2013 at 12:41 PM, Alan Crosswell <[email protected]> wrote: > Suggest that if you have a choice, you use bzip2 compression instead of > gzip as bzip2 is block-based and Pig can split reading a large bzipped file > across multiple mappers while gzip can't be split that way. > > > On Mon, Jun 10, 2013 at 12:06 PM, William Oberman < > [email protected]> wrote: > >> I still don't fully understand (and am still debugging), but I have a >> "problem file" and a theory. >> >> The file has a "corrupt line" that is a huge block of null characters >> followed by a "\n" (other lines are json followed by "\n"). I'm thinking >> that's a problem with my cassandra -> s3 process, but is out of scope for >> this thread.... I wrote scripts to examine the file directly, and if I >> stop counting at the weird line, I get the "gz" count. If I count all >> lines (e.g. don't fail at the corrupt line) I get the "uncompressed" >> count. >> >> I don't know how to debug hadoop/pig quite as well, though I'm trying now. >> But, my working theory is that some combination of pig/hadoop aborts >> processing the gz stream on a null character, but keeps chugging on a >> non-gz stream. Does that sound familiar? >> >> will >> >> >> On Sat, Jun 8, 2013 at 8:00 AM, William Oberman <[email protected] >> >wrote: >> >> > They are all *.gz, I confirmed that first :-) >> > >> > >> > On Saturday, June 8, 2013, Niels Basjes wrote: >> > >> >> What are the exact filenames you used? >> >> The decompression of input files is based on the filename extention. >> >> >> >> Niels >> >> On Jun 7, 2013 11:11 PM, "William Oberman" <[email protected]> >> >> wrote: >> >> >> >> > I'm using pig 0.11.2. >> >> > >> >> > I had been processing ASCII files of json with schema: >> (key:chararray, >> >> > columns:bag {column:tuple (timeUUID:chararray, value:chararray, >> >> > timestamp:long)}) >> >> > For what it's worth, this is cassandra data, at a fairly low level. >> >> > >> >> > But, this was getting big, so I compressed it all with gzip (my "ETL" >> >> > process is already chunking the data into 1GB parts, making the .gz >> >> files >> >> > ~100MB). >> >> > >> >> > As a sanity check, I decided to do a quick check of pre/post, and the >> >> > numbers aren't matching. Then I've done a lot of messing around >> trying >> >> to >> >> > figure out why and I'm getting more and more puzzled. >> >> > >> >> > My "quick check" was to get an overall count. It looked like >> (assuming >> >> A >> >> > is a LOAD given the schema above): >> >> > ------- >> >> > allGrp = GROUP A ALL; >> >> > aCount = FOREACH allGrp GENERATE group, COUNT(A); >> >> > DUMP aCount; >> >> > ------- >> >> > >> >> > Basically the original data returned a number GREATER than the >> >> compressed >> >> > data number (not by a lot, but still...). >> >> > >> >> > Then I uncompressed all of the compressed files, and did a size >> check of >> >> > original vs. uncompressed. They were the same. Then I "quick >> checked" >> >> the >> >> > uncompressed, and the count of that was == original! So, the way in >> >> which >> >> > pig processes the gzip'ed data is actually somehow different. >> >> > >> >> > Then I tried to see if there are nulls floating around, so I loaded >> >> "orig" >> >> > and "comp" and tried to catch the "missing keys" with outer joins: >> >> > ----------- >> >> > joined = JOIN orig by key LEFT OUTER, comp BY key; >> >> > filtered = FILTER joined BY (comp::key is null); >> >> > ----------- >> >> > And filtered was empty! I then tried the reverse (which makes no >> sense >> >> I >> >> > know, as this was the smaller set), and filtered is still empty! >> >> > >> >> > All of these loads are through a custom UDF that extends LoadFunc. >> But, >> >> > there isn't much to that UDF (and it's been in use for many months >> now). >> >> > Basically, the "raw" data is JSON (from cassandra's sstable2json >> >> program). >> >> > And I parse the json and turn it into the pig structure of the >> schema >> >> > noted above. >> >> > >> >> > Does anything make sense here? >> >> > >> >> > Thanks! >> >> > >> >> > will >> >> > >> >> >> > >> > >> > >> > >
