I have not done a through investigation. But from what I was told is that the amount of network data push among the nodes of our cluster is very low. Most of the time seems to be local computation.
Date: Sat, 25 Jun 2011 07:27:17 -0700 Subject: Re: improve performance of avro map reduce jobs From: [email protected] To: [email protected] To me, Avro offers a big benefit to MapReduce jobs by having a well-defined file format that defines the schema for records that was used to write it, is splittable, compressable, and has metadata. Ultimately, I'd like to see that and a binding layer on top of data serialization and more flexibility for serialization in Avro (e.g., why not be able to use its APIs but Protobuf for binding). That being said, I'm curious where you see the CPU going in your jobs? Where in the Avro serialization is it spending its time? I suspect that GenericData isn't nearly as performant as using codegen. On Sat, Jun 25, 2011 at 6:27 AM, ey-chih chow <[email protected]> wrote: Let me put the question in another way. Companies like Twitter they use Protocol Buffer as their serialization tool. It seems to have better performance. Is there any compelling reason that Avro can do and Protocol Buffer cannot ? Thanks. Ey-Chih From: [email protected] To: [email protected] Subject: improve performance of avro map reduce jobs Date: Fri, 24 Jun 2011 16:55:58 -0700 Our Map/Reduce jobs are all based on avro. We would like to enhance their performance. The objects collected in our mappers and reducers are mainly of the type GenericData.Record. Currently, most of jobs are CPU, rather than IO, bound. Can anybody suggest ways to improve the performance of the jobs? Thanks a lot. Ey-Chih Chow -- Ron Bodkin CEO Think Big Analytics m: +1 (415) 509-2895 @ronbodkin
