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Avro >> mail # user >> Can spill to disk be in compressed Avro format to reduce I/O?


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Re: Can spill to disk be in compressed Avro format to reduce I/O?


On 1/12/12 8:27 AM, "Frank Grimes" <[EMAIL PROTECTED]> wrote:

> Hi All,
>
> We have Avro data files in HDFS which are compressed using the Deflate codec.
> We have written an M/R job using the Avro Mapred API to combine those files.
>
> It seems to be working fine, however when we run it we notice that the
> temporary work area (spills, etc) seem to be uncompressed.
> We're thinking we might see a speedup due to reduced I/O if the temporary
> files are compressed as well.

If all you want to do is combine the files, there is no reason to
deserialize and reserialize the contents, and a map-only job could suffice.
If this is the case, you might want to consider one of two optoins:
1.  Use a map only job, with a combined file input.  This will produce one
file per mapper and no intermediate data.
2.  Use the Avro data file API to append to a file.  I am not sure if this
will work with HDFS without some modifications to Avro, but it should be
possible since the data file APIs can take InputStream/OutputStream.  The
data file API has the ability to append data blocks from the file if the
schemas are an exact match.  This can be done without deserialization, and
optionally can change the compression level or leave it alone.

>
> Is there a way to enable "mapred.compress.map.output" in such a way that those
> temporary files are compressed as Avro/Deflate?
> I tried simply setting conf.setBoolean("mapred.compress.map.output", true);
> but it didn't seem to have any effect.

I am not sure, as I haven't tried it myself.  However, the Avro M/R should
be able to leverage all of the Hadoop compressed intermediate forms.
LZO/Snappy are fast and in our cluster Snappy is the default.  Deflate can
be a lot slower but much more compact.

>
> 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. (we don't care about ordering)

In that case, I think you can use a map only job.  There may be some work to
get a single mapper to read many files however.

>
> FYI, here are my mapper and reducer.
>
>
> 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);
> }
> }
>
> }
>
> Also, I'm setting the following:
>
> AvroJob.setInputSchema(conf, DeliveryLogEvent.SCHEMA$);
> AvroJob.setMapperClass(conf, Mapper.class);
> AvroJob.setMapOutputSchema(conf, SCHEMA);
>
> AvroJob.setOutputSchema(conf, DeliveryLogEvent.SCHEMA$);
> AvroJob.setOutputCodec(conf, DataFileConstants.DEFLATE_CODEC);
> AvroOutputFormat.setDeflateLevel(conf, 9);
> AvroOutputFormat.setSyncInterval(conf, 1024 * 256);
>
> AvroJob.setReducerClass(conf, Reducer.class);
>
> JobClient.runJob(conf);
>
>
> Thanks,
>
> Frank Grimes