As it turns out, this is due to our /tmp partition being too small.
We'll either need to increase it or put hadoop.tmp.dir on a bigger partition.
On 2012-01-11, at 4:29 PM, Frank Grimes wrote:
> Ok, so I wrote a MapReduce job to merge the files and it appears to be
> working with a limited input set.
> Thanks again, BTW.
>
> However, if I increase the amount of input data I start getting the following
> types of errors:
>
> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not find any
> valid local directory for output/file.out/file.out
> or
> org.apache.hadoop.util.DiskChecker$DiskErrorException: Could not find any
> valid local directory for output/map_0.out
>
> Are there any logs I should be looking at to determine the exact cause of
> these errors?
> Are there any settings I could/should be increasing?
>
> Note that in order to avoid unnecessary sorting overhead, I made each key a
> constant (1L) so that the logs are combined but ordering isn't necessarily
> preserved.
> i.e.
>
> public static class AvroReachMapper extends
> AvroMapper<DeliveryLogEvent, Pair<Long, DeliveryLogEvent>> {
> public void map(DeliveryLogEvent levent,
> AvroCollector<Pair<Long, DeliveryLogEvent>> collector, Reporter reporter)
> throws IOException {
>
> collector.collect(new Pair<Long, DeliveryLogEvent>(1L,
> levent));
> }
> }
>
> public static class Reduce extends AvroReducer<Long, DeliveryLogEvent,
> DeliveryLogEvent> {
>
> @Override
> public void reduce(Long key, Iterable<DeliveryLogEvent> values,
> AvroCollector<DeliveryLogEvent> collector,
> Reporter reporter)
> throws IOException {
>
> for (DeliveryLogEvent event : values) {
> collector.collect(event);
> }
> }
>
> }
>
> I've also noticed that /tmp/mapred seems to fill up and doesn't automatically
> get cleaned out.
> Is Hadoop itself supposed to clean up those old temporary work files or do we
> need a Cron job for that?
>
> Thanks,
>
> Frank Grimes
>
>
>
>
> On 2012-01-06, at 3:56 PM, Joey Echeverria wrote:
>
>> I would use a MapReduce job to merge them.
>>
>> -Joey
>>
>> On Fri, Jan 6, 2012 at 11:55 AM, Frank Grimes <frankgrime...@gmail.com>
>> wrote:
>>> Hi Joey,
>>>
>>> That's a very good suggestion and might suit us just fine.
>>>
>>> However, many of the files will be much smaller than the HDFS block size.
>>> That could affect the performance of the MapReduce jobs, correct?
>>> Also, from my understanding it would put more burden on the name node
>>> (memory usage) than is necessary.
>>>
>>> Assuming we did want to combine the actual files... how would you suggest
>>> we might go about it?
>>>
>>> Thanks,
>>>
>>> Frank Grimes
>>>
>>>
>>> On 2012-01-06, at 1:05 PM, Joey Echeverria wrote:
>>>
>>>> I would do it by staging the machine data into a temporary directory
>>>> and then renaming the directory when it's been verified. So, data
>>>> would be written into directories like this:
>>>>
>>>> 2012-01/02/00/stage/machine1.log.avro
>>>> 2012-01/02/00/stage/machine2.log.avro
>>>> 2012-01/02/00/stage/machine3.log.avro
>>>>
>>>> After verification, you'd rename the 2012-01/02/00/stage directory to
>>>> 2012-01/02/00/done. Since renaming a directory in HDFS is an atomic
>>>> operation, you get the guarantee the you're looking for without having
>>>> to do extra IO. There shouldn't be a benefit to merging the individual
>>>> files unless they're too small.
>>>>
>>>> -Joey
>>>>
>>>> On Fri, Jan 6, 2012 at 9:21 AM, Frank Grimes <frankgrime...@gmail.com>
>>>> wrote:
>>>>> Hi Bobby,
>>>>>
>>>>> Actually, the problem we're trying to solve is one of completeness.
>>>>>
>>>>> Say we have 3 machines generating log events and putting them to HDFS on
>>>>> an
>>>>> hourly basis.
>>>>> e.g.
>>>>> 2012-01/01/00/machine1.log.avro
>>>>> 2012-01/01/00/machine2.log.avro
>>>>> 2012-01/01/00/machine3.log.avro
>>>>>
>>>>> Sometime after the hour, we would have a scheduled job verify that all the
>>>>> expected machines' log files are present and complete in HDFS.
>>>>>
>>>>> Before launching MapReduce jobs for a given date range, we want to verify
>>>>> that the job will run over complete data.
>>>>> If not, the query would error out.
>>>>>
>>>>> We want our query/MapReduce layer to not need to be aware of logs at the
>>>>> machine level, only the presence or not of an hour's worth of logs.
>>>>>
>>>>> We were thinking that after verifying all in individual log files for an
>>>>> hour, they could be combined into 2012-01/01/00/log.avro.
>>>>> The presence of 2012-01-01-00.log.avro would be all that needs to be
>>>>> verified.
>>>>>
>>>>> However, we're new to both Avro and Hadoop so not sure of the most
>>>>> efficient
>>>>> (and reliable) way to accomplish this.
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Frank Grimes
>>>>>
>>>>>
>>>>> On 2012-01-06, at 11:46 AM, Robert Evans wrote:
>>>>>
>>>>> Frank,
>>>>>
>>>>> That depends on what you mean by combining. It sounds like you are trying
>>>>> to
>>>>> aggregate data from several days, which may involve doing a join so I
>>>>> would
>>>>> say a MapReduce job is your best bet. If you are not going to do any
>>>>> processing at all then why are you trying to combine them? Is there
>>>>> something that requires them all to be part of a single file? MapReduce
>>>>> processing should be able to handle reading in multiple files just as well
>>>>> as reading in a single file.
>>>>>
>>>>> --Bobby Evans
>>>>>
>>>>> On 1/6/12 9:55 AM, "Frank Grimes" <frankgrime...@gmail.com> wrote:
>>>>>
>>>>> Hi All,
>>>>>
>>>>> I was wondering if there was an easy way to combing multiple .avro files
>>>>> efficiently.
>>>>> e.g. combining multiple hours of logs into a daily aggregate
>>>>>
>>>>> Note that our Avro schema might evolve to have new (nullable) fields added
>>>>> but no fields will be removed.
>>>>>
>>>>> I'd like to avoid needing to pull the data down for combining and
>>>>> subsequent
>>>>> "hadoop dfs -put".
>>>>>
>>>>> Would https://issues.apache.org/jira/browse/HDFS-222 be able to handle
>>>>> that
>>>>> automatically?
>>>>> FYI, the following seems to indicate that Avro files might be easily
>>>>> combinable: https://issues.apache.org/jira/browse/AVRO-127
>>>>>
>>>>> Or is an M/R job the best way to go for this?
>>>>>
>>>>> Thanks,
>>>>>
>>>>> Frank Grimes
>>>>>
>>>>>
>>>>
>>>>
>>>>
>>>> --
>>>> Joseph Echeverria
>>>> Cloudera, Inc.
>>>> 443.305.9434
>>>
>>
>>
>>
>> --
>> Joseph Echeverria
>> Cloudera, Inc.
>> 443.305.9434
>
