Sure,
I see:
Map input eecords: 10,000
Map output records: 600,000
Map output bytes: 307,216,800,000 (each reacord is about 500kb - that
fits the application and is to be expected)
Map spilled records: 1,802,965 (ahhh... now that you ask for it - here
there also is a factor of 3 between output and spilled).
So - question now is: why are three times as many records spilled than
actually produced by the mappers?
In my map function, I do not perform any additional file writing besides
the context.write() for the intermediate records.
Thanks, Tim
Am 24.02.2010 05:28, schrieb Amogh Vasekar:
Hi,
Can you let us know what is the value for :
Map input records
Map spilled records
Map output bytes
Is there any side effect file written?
Thanks,
Amogh
On 2/23/10 8:57 PM, "Tim Kiefer"<tim-kie...@gmx.de> wrote:
No... 900GB is in the map column. Reduce adds another ~70GB of
FILE_BYTES_WRITTEN and the total column consequently shows ~970GB.
Am 23.02.2010 16:11, schrieb Ed Mazur:
Hi Tim,
I'm guessing a lot of these writes are happening on the reduce side.
On the JT web interface, there are three columns: map, reduce,
overall. Is the 900GB figure from the overall column? The value in the
map column will probably be closer to what you were expecting. There
are writes on the reduce side too during the shuffle and multi-pass
merge.
Ed
2010/2/23 Tim Kiefer<tim-kie...@gmx.de>:
Hi Gang,
thanks for your reply.
To clarify: I look at the statistics through the job tracker. In the
webinterface for my job I have columns for map, reduce and total. What I
was refering to is "map" - i.e. I see FILE_BYTES_WRITTEN = 3 * Map
Output Bytes in the map column.
About the replication factor: I would expect the exact same thing -
changing to 6 has no influence on FILE_BYTES_WRITTEN.
About the sorting: I have io.sort.mb = 100 and io.sort.factor = 10.
Furthermore, I have 40 mappers and map output data is ~300GB. I can't
see how that ends up in a factor 3?
- tim
Am 23.02.2010 14:39, schrieb Gang Luo:
Hi Tim,
the intermediate data is materialized to local file system. Before it is
available for reducers, mappers will sort them. If the buffer (io.sort.mb) is
too small for the intermediate data, multi-phase sorting happen, which means
you read and write the same bit more than one time.
Besides, are you looking at the statistics per mapper through the job tracker,
or just the information output when a job finish? If you look at the
information given out at the end of the job, note that this is an overall
statistics which include sorting at reduce side. It also include the amount of
data written to HDFS (I am not 100% sure).
And, the FILE-BYTES_WRITTEN has nothing to do with the replication factor. I
think if you change the factor to 6, FILE_BYTES_WRITTEN is still the same.
-Gang
Hi there,
can anybody help me out on a (most likely) simple unclarity.
I am wondering how intermediate key/value pairs are materialized. I have a job
where the map phase produces 600,000 records and map output bytes is ~300GB.
What I thought (up to now) is that these 600,000 records, i.e., 300GB, are
materialized locally by the mappers and that later on reducers pull these
records (based on the key).
What I see (and cannot explain) is that the FILE_BYTES_WRITTEN counter is as
high as ~900GB.
So - where does the factor 3 come from between Map output bytes and
FILE_BYTES_WRITTEN??? I thought about the replication factor of 3 in the file
system - but that should be HDFS only?!
Thanks
- tim
