Thanks everyone. Yes, using the Google Code version referenced on the wiki: http://wiki.apache.org/hadoop/UsingLzoCompression
I will try the latest version and see if that fixes the problem. http://github.com/kevinweil/hadoop-lzo Thanks On Fri, Jul 9, 2010 at 3:22 AM, Todd Lipcon <[email protected]> wrote: > On Thu, Jul 8, 2010 at 10:38 AM, Ted Yu <[email protected]> wrote: >> >> Todd fixed a bug where LZO header or block header data may fall on read >> boundary: >> >> http://github.com/toddlipcon/hadoop-lzo/commit/f3bc3f8d003bb8e24f254b25bca2053f731cdd58 >> >> >> I am wondering if that is related to the issue you saw. > > I don't think this bug would show up in intermediate output compression, but > it's certainly possible. There have been a number of bugs fixed in LZO over > on github - are you using the github version or the one from Google Code > which is out of date? Either mine or Kevin's repo on github should be a good > version (I think we called the newest 0.3.4) > -Todd > >> >> On Wed, Jul 7, 2010 at 11:49 PM, bmdevelopment <[email protected]> >> wrote: >>> >>> A little more on this. >>> >>> So, I've narrowed down the problem to using Lzop compression >>> (com.hadoop.compression.lzo.LzopCodec) >>> for mapred.map.output.compression.codec. >>> >>> <property> >>> <name>mapred.map.output.compression.codec</name> >>> <value>com.hadoop.compression.lzo.LzopCodec</value> >>> </property> >>> >>> If I do the above, I will get the Shuffle Error. >>> If I use DefaultCodec for mapred.map.output.compression.codec. >>> there is no problem. >>> >>> Is this a known issue? Or is this a bug? >>> Doesn't seem like it should be the expected behavior. >>> >>> I would be glad to contribute any further info on this if necessary. >>> Please let me know. >>> >>> Thanks >>> >>> On Wed, Jul 7, 2010 at 5:02 PM, bmdevelopment <[email protected]> >>> wrote: >>> > Hi, No problems. Thanks so much for your time. Greatly appreciated. >>> > >>> > I agree that it must be a configuration problem and so today I was able >>> > to start from scratch and did a fresh install of 0.20.2 on the 5 node >>> > cluster. >>> > >>> > I've now noticed that the error occurs when compression is enabled. >>> > I've run the basic wordcount example as so: >>> > http://pastebin.com/wvDMZZT0 >>> > and get the Shuffle Error. >>> > >>> > TT logs show this error: >>> > WARN org.apache.hadoop.mapred.ReduceTask: java.io.IOException: Invalid >>> > header checksum: 225702cc (expected 0x2325) >>> > Full logs: >>> > http://pastebin.com/fVGjcGsW >>> > >>> > My mapred-site.xml: >>> > http://pastebin.com/mQgMrKQw >>> > >>> > If I remove the compression config settings, the wordcount works fine >>> > - no more Shuffle Error. >>> > So, I have something wrong with my compression settings I imagine. >>> > I'll continue looking into this to see what else I can find out. >>> > >>> > Thanks a million. >>> > >>> > On Tue, Jul 6, 2010 at 5:34 PM, Hemanth Yamijala <[email protected]> >>> > wrote: >>> >> Hi, >>> >> >>> >> Sorry, I couldn't take a close look at the logs until now. >>> >> Unfortunately, I could not see any huge difference between the success >>> >> and failure case. Can you please check if things like basic hostname - >>> >> ip address mapping are in place (if you have static resolution of >>> >> hostnames set up) ? A web search is giving this as the most likely >>> >> cause users have faced regarding this problem. Also do the disks have >>> >> enough size ? Also, it would be great if you can upload your hadoop >>> >> configuration information. >>> >> >>> >> I do think it is very likely that configuration is the actual problem >>> >> because it works in one case anyway. >>> >> >>> >> Thanks >>> >> Hemanth >>> >> >>> >> On Mon, Jul 5, 2010 at 12:41 PM, bmdevelopment >>> >> <[email protected]> wrote: >>> >>> Hello, >>> >>> I still have had no luck with this over the past week. >>> >>> And even get the same exact problem on a completely different 5 node >>> >>> cluster. >>> >>> Is it worth opening an new issue in jira for this? >>> >>> Thanks >>> >>> >>> >>> >>> >>> On Fri, Jun 25, 2010 at 11:56 PM, bmdevelopment >>> >>> <[email protected]> wrote: >>> >>>> Hello, >>> >>>> Thanks so much for the reply. >>> >>>> See inline. >>> >>>> >>> >>>> On Fri, Jun 25, 2010 at 12:40 AM, Hemanth Yamijala >>> >>>> <[email protected]> wrote: >>> >>>>> Hi, >>> >>>>> >>> >>>>>> I've been getting the following error when trying to run a very >>> >>>>>> simple >>> >>>>>> MapReduce job. >>> >>>>>> Map finishes without problem, but error occurs as soon as it >>> >>>>>> enters >>> >>>>>> Reduce phase. >>> >>>>>> >>> >>>>>> 10/06/24 18:41:00 INFO mapred.JobClient: Task Id : >>> >>>>>> attempt_201006241812_0001_r_000000_0, Status : FAILED >>> >>>>>> Shuffle Error: Exceeded MAX_FAILED_UNIQUE_FETCHES; bailing-out. >>> >>>>>> >>> >>>>>> I am running a 5 node cluster and I believe I have all my settings >>> >>>>>> correct: >>> >>>>>> >>> >>>>>> * ulimit -n 32768 >>> >>>>>> * DNS/RDNS configured properly >>> >>>>>> * hdfs-site.xml : http://pastebin.com/xuZ17bPM >>> >>>>>> * mapred-site.xml : http://pastebin.com/JraVQZcW >>> >>>>>> >>> >>>>>> The program is very simple - just counts a unique string in a log >>> >>>>>> file. >>> >>>>>> See here: http://pastebin.com/5uRG3SFL >>> >>>>>> >>> >>>>>> When I run, the job fails and I get the following output. >>> >>>>>> http://pastebin.com/AhW6StEb >>> >>>>>> >>> >>>>>> However, runs fine when I do *not* use substring() on the value >>> >>>>>> (see >>> >>>>>> map function in code above). >>> >>>>>> >>> >>>>>> This runs fine and completes successfully: >>> >>>>>> String str = val.toString(); >>> >>>>>> >>> >>>>>> This causes error and fails: >>> >>>>>> String str = val.toString().substring(0,10); >>> >>>>>> >>> >>>>>> Please let me know if you need any further information. >>> >>>>>> It would be greatly appreciated if anyone could shed some light on >>> >>>>>> this problem. >>> >>>>> >>> >>>>> It catches attention that changing the code to use a substring is >>> >>>>> causing a difference. Assuming it is consistent and not a red >>> >>>>> herring, >>> >>>> >>> >>>> Yes, this has been consistent over the last week. I was running >>> >>>> 0.20.1 >>> >>>> first and then >>> >>>> upgrade to 0.20.2 but results have been exactly the same. >>> >>>> >>> >>>>> can you look at the counters for the two jobs using the JobTracker >>> >>>>> web >>> >>>>> UI - things like map records, bytes etc and see if there is a >>> >>>>> noticeable difference ? >>> >>>> >>> >>>> Ok, so here is the first job using write.set(value.toString()); >>> >>>> having >>> >>>> *no* errors: >>> >>>> http://pastebin.com/xvy0iGwL >>> >>>> >>> >>>> And here is the second job using >>> >>>> write.set(value.toString().substring(0, 10)); that fails: >>> >>>> http://pastebin.com/uGw6yNqv >>> >>>> >>> >>>> And here is even another where I used a longer, and therefore unique >>> >>>> string, >>> >>>> by write.set(value.toString().substring(0, 20)); This makes every >>> >>>> line >>> >>>> unique, similar to first job. >>> >>>> Still fails. >>> >>>> http://pastebin.com/GdQ1rp8i >>> >>>> >>> >>>>>Also, are the two programs being run against >>> >>>>> the exact same input data ? >>> >>>> >>> >>>> Yes, exactly the same input: a single csv file with 23K lines. >>> >>>> Using a shorter string leads to more like keys and therefore more >>> >>>> combining/reducing, but going >>> >>>> by the above it seems to fail whether the substring/key is entirely >>> >>>> unique (23000 combine output records) or >>> >>>> mostly the same (9 combine output records). >>> >>>> >>> >>>>> >>> >>>>> Also, since the cluster size is small, you could also look at the >>> >>>>> tasktracker logs on the machines where the maps have run to see if >>> >>>>> there are any failures when the reduce attempts start failing. >>> >>>> >>> >>>> Here is the TT log from the last failed job. I do not see anything >>> >>>> besides the shuffle failure, but there >>> >>>> may be something I am overlooking or simply do not understand. >>> >>>> http://pastebin.com/DKFTyGXg >>> >>>> >>> >>>> Thanks again! >>> >>>> >>> >>>>> >>> >>>>> Thanks >>> >>>>> Hemanth >>> >>>>> >>> >>>> >>> >>> >>> >> >>> > >> > > > > -- > Todd Lipcon > Software Engineer, Cloudera >
