A new map reduce framework for iterative and pipelined jobs.

I’m pleased to announce Peregrine 0.5.0 – a new map reduce framework optimized for iterative and pipelined map reduce jobs.

This originally started off with some internal work at Spinn3r to build a fast and efficient Pagerank implementation. We realized that what we wanted was a MR runtime optimized for this type of work which differs radically from the traditional Hadoop design.

Peregrine implements a partitioned distributed filesystem where key/value pairs are routed to defined partitions. This enables work to be joined against previous iterations or different units of work by the same key on the same local system.

Peregrine is optimized for ETL jobs where the primary data storage system is an external database such as Cassandra, Hbase, MySQL, etc. Jobs are then run as a Extract, Transform and Load stages with intermediate data being stored in the Peregrine FS.

We enable features such as Map/Reduce/Merge as well as some additional functionality like ExtractMap and ReduceLoad (in ETL parlance).

A key innovation here is a partitioning layout algorithm that can support fast many to many recovery similar to HDFS but still support partitioned operation with deterministic key placement.

We’ve also tried to optimize for single instance performance and use modern IO primitives as much as possible. This includes NOT shying away from operating specific features such as mlock, fadvise, fallocate, etc.

There is still a bit more work I want to do before I am ready to benchmark it against Hadoop. Instead of implementing a synthetic benchmark we wanted to get a production ready version first which would allow people to port existing
applications and see what the before / after performance numbers looked like in the real world.

For more information please see:

http://peregrine_mapreduce.bitbucket.org/

As well as our design documentation:

http://peregrine_mapreduce.bitbucket.org/design/



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