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From Everett Anderson <ever...@nuna.com>
Subject Re: Non Deterministic Record Drops
Date Wed, 29 Jul 2015 16:42:55 GMT
Great explanation! Thanks very much!

On Tue, Jul 28, 2015 at 8:44 PM, Josh Wills <jwills@cloudera.com> wrote:

> Hey Everett,
>
> The bug was specific to the situation where the input was a PTable (never
> a PCollection) that was being processed multiple times in a pure map-only
> job. The reason is that internal to the MR planner, an input PTable is
> really just a thin wrapper around an input PCollection<Pair<K, V>> (at
> least from a job configuration perspective.) The planner keeps track of
> which input PCollections it has seen before as it walks the DAG with a
> Visitor object that knows how to update an internal graph based on the type
> of PCollection visited (input, DoFn, union, GBK, etc.)
>
> Before I added the fix to the BaseInputTable to distinguish it (in an
> equals(Object) sense) from a BaseInputCollection, it was possible for the
> planner to get confused and assign multiple Vertices in the DAG to the same
> underlying input (one for the PCollection version, and one for the PTable
> version). Some of the outputs would go to the PCollection, some would go to
> the PTable, and unless there was a GBK operation that had both "versions"
> as parents, it was possible for the planner to essentially lose either the
> PTable or the PCollection vertex when it went to finish the job, which
> meant that none of those inputs would get read. The order in which the DAG
> is walked isn't deterministic for outputs that are on the same "level"
> (i.e., all of the outputs from a map-only job), so the inputs that would
> get processed in your jobs would change from run to run depending on the
> order in which they showed up in the graph, as you saw.
>
> The change I made ensures that all of the inputs are tracked to the same
> Vertex in the graph (the one based on the underlying InputCollection that
> is wrapped by the InputPTable) by the planner, so now no inputs get lost. I
> hope that helps a little bit.
>
> J
>
>
> On Tue, Jul 28, 2015 at 10:36 AM, David Ortiz <dortiz@videologygroup.com>
> wrote:
>
>>  For what it's worth, the optimizer may still read the file more than
>> once even if there's only one read in your code.  All depends on what else
>> is being done.
>>
>>  *Sent from my Verizon Wireless 4G LTE DROID*
>>  On Jul 28, 2015 1:34 PM, Everett Anderson <everett@nuna.com> wrote:
>>  Thanks, Josh!!
>>
>>  I'm curious about the fix and didn't fully understand from the
>> description.
>>
>>  What's interesting about the test is that there's only one Pipeline
>> read(), but then multiple parallelDo()s on the resulting table, yet you
>> still hit the issue. We'd thought it must be due to the multiple reads of
>> the same file.
>>
>>  Would this have happened in other places where multiple operations were
>> performed on the same PTable or PCollection, or is it specific to the
>> operations performed on objects created directly from a read()?
>>
>>
>>
>> On Mon, Jul 27, 2015 at 6:49 PM, Josh Wills <jwills@cloudera.com> wrote:
>>
>>> That was a deeply satisfying bug. Fix is up here:
>>> https://issues.apache.org/jira/browse/CRUNCH-553
>>>
>>> On Mon, Jul 27, 2015 at 6:29 PM, Jeff Quinn <jeff@nuna.com> wrote:
>>>
>>>> Wow, thanks so much for looking into it. That minimal example
>>>> seems accurate. Previously when we dug deeper into which records were
>>>> dropped it appeared entire files were being dropped, not just parts of one
>>>> file, so that sounds consistent with what you are seeing.
>>>>
>>>> On Monday, July 27, 2015, Josh Wills <jwills@cloudera.com> wrote:
>>>>
>>>>>  Hey Jeff,
>>>>>
>>>>>  Okay cool-- I think I've managed to create a simple test that
>>>>> replicates the behavior you're seeing. I can run this test a few different
>>>>> times, and sometimes I'll get the correct output, but other times I'll
get
>>>>> an error b/c no records are processed. I'm going to investigate further
and
>>>>> see if I can identify the source of the randomness.
>>>>>
>>>>>  public class RecordDropIT {
>>>>>   @Rule
>>>>>   public TemporaryPath tmpDir = TemporaryPaths.create();
>>>>>
>>>>>   @Test
>>>>>   public void testMultiReadCount() throws Exception {
>>>>>     int numReads = 2;
>>>>>     MRPipeline p = new MRPipeline(RecordDropIT.class, tmpDir.getDefaultConfiguration());
>>>>>     Path shakes = tmpDir.copyResourcePath("shakes.txt");
>>>>>     TableSource<LongWritable, Text> src = From.formattedFile(shakes,
TextInputFormat.class, LongWritable.class, Text.class);
>>>>>     List<Iterable<Integer>> values = Lists.newArrayList();
>>>>>     for (int i = 0; i < numReads; i++) {
>>>>>       PCollection<Integer> cnt = p.read(src).parallelDo(new LineCountFn<Pair<LongWritable,
Text>>(), Writables.ints());
>>>>>       values.add(cnt.materialize());
>>>>>     }
>>>>>     for (Iterable<Integer> iter : values) {
>>>>>       System.out.println(Iterables.getOnlyElement(iter));
>>>>>     }
>>>>>     p.done();
>>>>>   }
>>>>>
>>>>>   public static class LineCountFn<T> extends DoFn<T, Integer>
{
>>>>>
>>>>>     private int count = 0;
>>>>>
>>>>>     @Override
>>>>>     public void process(T input, Emitter<Integer> emitter) {
>>>>>       count++;
>>>>>     }
>>>>>
>>>>>     @Override
>>>>>     public void cleanup(Emitter<Integer> emitter) {
>>>>>       emitter.emit(count);
>>>>>     }
>>>>>   }
>>>>> }
>>>>>
>>>>>
>>>>> On Mon, Jul 27, 2015 at 6:11 PM, Jeff Quinn <jeff@nuna.com> wrote:
>>>>>
>>>>>> Hi Josh,
>>>>>>
>>>>>>  Thanks so much for your suggestions.
>>>>>>
>>>>>>  The counts are determined with two methods, I am using a simple
pig
>>>>>> script to count records, and I am also tabulating up the size in
bytes of
>>>>>> all hdfs output files. Both measures show dropped records / fewer
than
>>>>>> expected output bytes.
>>>>>>
>>>>>>  To your second point I will go back and do a sweep for that, but
I
>>>>>> am fairly sure no DoFns are making use of intermediate state values
without
>>>>>> getDetachedValue. Our team is aware of the getDetachedValue gotchas
as I
>>>>>> think it has bitten us before.
>>>>>>
>>>>>>  Thanks !
>>>>>>
>>>>>>  Jeff
>>>>>>
>>>>>>
>>>>>> On Monday, July 27, 2015, Josh Wills <jwills@cloudera.com>
wrote:
>>>>>>
>>>>>>>  One more thought-- are any of these DoFns keeping records around
>>>>>>> as intermediate state values w/o using PType.getDetachedValue
to make
>>>>>>> copies of them?
>>>>>>>
>>>>>>>  J
>>>>>>>
>>>>>>> On Mon, Jul 27, 2015 at 5:47 PM, Josh Wills <jwills@cloudera.com>
>>>>>>> wrote:
>>>>>>>
>>>>>>>>  Hey Jeff,
>>>>>>>>
>>>>>>>>  Are the counts determined by Counters? Or is it the length
of the
>>>>>>>> output files? Or both?
>>>>>>>>
>>>>>>>>  J
>>>>>>>>
>>>>>>>> On Mon, Jul 27, 2015 at 5:29 PM, David Ortiz <dpo5003@gmail.com>
>>>>>>>> wrote:
>>>>>>>>
>>>>>>>>>  Out of curiosity, any reason you went with multiple
reads as
>>>>>>>>> opposed to just performing multiple operations on the
same PTable?
>>>>>>>>> parallelDo returns a new object rather than modifying
the initial one, so a
>>>>>>>>> single collection can start multiple execution flows.
>>>>>>>>>
>>>>>>>>>  On Mon, Jul 27, 2015, 8:11 PM Jeff Quinn <jeff@nuna.com>
wrote:
>>>>>>>>>
>>>>>>>>>> Hello,
>>>>>>>>>>
>>>>>>>>>>  We have observed and replicated strange behavior
with our
>>>>>>>>>> crunch application while running on MapReduce via
the AWS ElasticMapReduce
>>>>>>>>>> service. Running a very simple job which is mostly
map only, we see that an
>>>>>>>>>> undetermined subset of records are getting dropped.
Specifically, we
>>>>>>>>>> expect 30,136,686 output records and have seen output
on different trials
>>>>>>>>>> (running over the same data with the same binary):
>>>>>>>>>>
>>>>>>>>>> 22,177,119 records
>>>>>>>>>> 26,435,670 records
>>>>>>>>>> 22,362,986 records
>>>>>>>>>> 29,798,528 records
>>>>>>>>>>
>>>>>>>>>>  These are all the things about our application which
might be
>>>>>>>>>> unusual and relevant:
>>>>>>>>>>
>>>>>>>>>>  - We use a custom file input format, via From.formattedFile.
It
>>>>>>>>>> looks like this (basically a carbon copy
>>>>>>>>>> of org.apache.hadoop.mapreduce.lib.input.TextInputFormat):
>>>>>>>>>>
>>>>>>>>>>  import org.apache.hadoop.io.LongWritable;
>>>>>>>>>> import org.apache.hadoop.io.Text;
>>>>>>>>>> import org.apache.hadoop.mapreduce.InputSplit;
>>>>>>>>>> import org.apache.hadoop.mapreduce.RecordReader;
>>>>>>>>>> import org.apache.hadoop.mapreduce.TaskAttemptContext;
>>>>>>>>>> import org.apache.hadoop.mapreduce.lib.input.FileInputFormat;
>>>>>>>>>> import org.apache.hadoop.mapreduce.lib.input.LineRecordReader;
>>>>>>>>>>
>>>>>>>>>> import java.io.IOException;
>>>>>>>>>>
>>>>>>>>>>  public class ByteOffsetInputFormat extends FileInputFormat<LongWritable,
Text> {
>>>>>>>>>>
>>>>>>>>>>   @Override
>>>>>>>>>>   public RecordReader<LongWritable, Text> createRecordReader(
>>>>>>>>>>       InputSplit split, TaskAttemptContext context)
throws IOException,
>>>>>>>>>>       InterruptedException {
>>>>>>>>>>     return new LineRecordReader();
>>>>>>>>>>   }
>>>>>>>>>> }
>>>>>>>>>>
>>>>>>>>>> - We call org.apache.crunch.Pipeline#read using this
InputFormat many times, for the job in question it is called ~160 times as the input is ~100
different files. Each file ranges in size from 100MB-8GB. Our job only uses this input format
for all input files.
>>>>>>>>>>
>>>>>>>>>> - For some files org.apache.crunch.Pipeline#read
is called twice one the same file, and the resulting PTables are processed in different ways.
>>>>>>>>>>
>>>>>>>>>> - It is only the data from these files which org.apache.crunch.Pipeline#read
has been called on more than once during a job that have dropped records, all other files
consistently do not have dropped records
>>>>>>>>>>
>>>>>>>>>> Curious if any Crunch users have experienced similar
behavior before, or if any of these details about my job raise any red flags.
>>>>>>>>>>
>>>>>>>>>> Thanks!
>>>>>>>>>>
>>>>>>>>>> Jeff Quinn
>>>>>>>>>>
>>>>>>>>>> Data Engineer
>>>>>>>>>>
>>>>>>>>>> Nuna
>>>>>>>>>>
>>>>>>>>>>
>>>>>>>>>> *DISCLAIMER:* The contents of this email, including
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>>>>>>>>>> attachments, may contain information that is confidential,
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>>>>>>>>>> nature, protected health information (PHI), or otherwise
protected by law
>>>>>>>>>> from disclosure, and is solely for the use of the
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>>>>>>>>>> you are not the intended recipient, you are hereby
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>>>>>>>>>> disclosure or copying of this email, including any
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>>>>>>>>>> unauthorized and strictly prohibited. If you have
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>>>>>>>>>> error, please notify the sender of this email. Please
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>>>>>>>>>> copies of this email from your system. Any opinions
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>>>>>>>>>> implied in this email and all attachments, are those
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>>>>>>>>>> and do not necessarily reflect those of Nuna Health,
Inc.
>>>>>>>>>
>>>>>>>>>
>>>>>>>>
>>>>>>>>
>>>>>>>>   --
>>>>>>>>  Director of Data Science
>>>>>>>> Cloudera <http://www.cloudera.com>
>>>>>>>> Twitter: @josh_wills <http://twitter.com/josh_wills>
>>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>
>>>>>>>  --
>>>>>>>  Director of Data Science
>>>>>>> Cloudera <http://www.cloudera.com>
>>>>>>> Twitter: @josh_wills <http://twitter.com/josh_wills>
>>>>>>>
>>>>>>
>>>>>> *DISCLAIMER:* The contents of this email, including any attachments,
>>>>>> may contain information that is confidential, proprietary in nature,
>>>>>> protected health information (PHI), or otherwise protected by law
from
>>>>>> disclosure, and is solely for the use of the intended recipient(s).
If you
>>>>>> are not the intended recipient, you are hereby notified that any
use,
>>>>>> disclosure or copying of this email, including any attachments, is
>>>>>> unauthorized and strictly prohibited. If you have received this email
in
>>>>>> error, please notify the sender of this email. Please delete this
and all
>>>>>> copies of this email from your system. Any opinions either expressed
or
>>>>>> implied in this email and all attachments, are those of its author
only,
>>>>>> and do not necessarily reflect those of Nuna Health, Inc.
>>>>>>
>>>>>
>>>>>
>>>>>
>>>>>  --
>>>>>  Director of Data Science
>>>>> Cloudera <http://www.cloudera.com>
>>>>> Twitter: @josh_wills <http://twitter.com/josh_wills>
>>>>>
>>>>
>>>> *DISCLAIMER:* The contents of this email, including any attachments,
>>>> may contain information that is confidential, proprietary in nature,
>>>> protected health information (PHI), or otherwise protected by law from
>>>> disclosure, and is solely for the use of the intended recipient(s). If you
>>>> are not the intended recipient, you are hereby notified that any use,
>>>> disclosure or copying of this email, including any attachments, is
>>>> unauthorized and strictly prohibited. If you have received this email in
>>>> error, please notify the sender of this email. Please delete this and all
>>>> copies of this email from your system. Any opinions either expressed or
>>>> implied in this email and all attachments, are those of its author only,
>>>> and do not necessarily reflect those of Nuna Health, Inc.
>>>>
>>>
>>>
>>>
>>>  --
>>>  Director of Data Science
>>> Cloudera <http://www.cloudera.com>
>>> Twitter: @josh_wills <http://twitter.com/josh_wills>
>>>
>>
>>
>> *DISCLAIMER:* The contents of this email, including any attachments, may
>> contain information that is confidential, proprietary in nature, protected
>> health information (PHI), or otherwise protected by law from disclosure,
>> and is solely for the use of the intended recipient(s). If you are not the
>> intended recipient, you are hereby notified that any use, disclosure or
>> copying of this email, including any attachments, is unauthorized and
>> strictly prohibited. If you have received this email in error, please
>> notify the sender of this email. Please delete this and all copies of this
>> email from your system. Any opinions either expressed or implied in this
>> email and all attachments, are those of its author only, and do not
>> necessarily reflect those of Nuna Health, Inc.
>> *This email is intended only for the use of the individual(s) to whom it
>> is addressed. If you have received this communication in error, please
>> immediately notify the sender and delete the original email.*
>>
>
>
>
> --
> Director of Data Science
> Cloudera <http://www.cloudera.com>
> Twitter: @josh_wills <http://twitter.com/josh_wills>
>

-- 
*DISCLAIMER:* The contents of this email, including any attachments, may 
contain information that is confidential, proprietary in nature, protected 
health information (PHI), or otherwise protected by law from disclosure, 
and is solely for the use of the intended recipient(s). If you are not the 
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copying of this email, including any attachments, is unauthorized and 
strictly prohibited. If you have received this email in error, please 
notify the sender of this email. Please delete this and all copies of this 
email from your system. Any opinions either expressed or implied in this 
email and all attachments, are those of its author only, and do not 
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