ALL COVERED TOPICS

NoSQL Benchmarks NoSQL use cases NoSQL Videos NoSQL Hybrid Solutions NoSQL Presentations Big Data Hadoop MapReduce Pig Hive Flume Oozie Sqoop HDFS ZooKeeper Cascading Cascalog BigTable Cassandra HBase Hypertable Couchbase CouchDB MongoDB OrientDB RavenDB Jackrabbit Terrastore Amazon DynamoDB Redis Riak Project Voldemort Tokyo Cabinet Kyoto Cabinet memcached Amazon SimpleDB Datomic MemcacheDB M/DB GT.M Amazon Dynamo Dynomite Mnesia Yahoo! PNUTS/Sherpa Neo4j InfoGrid Sones GraphDB InfiniteGraph AllegroGraph MarkLogic Clustrix CouchDB Case Studies MongoDB Case Studies NoSQL at Adobe NoSQL at Facebook NoSQL at Twitter

NAVIGATE MAIN CATEGORIES

Close

Comparing Pregel and MapReduce

Following his post on graph processing, Ricky Ho explains the major difference between Pregel and MapReduce applied to graph processing:

Since Pregel model retain worker state (the same worker is responsible for the same set of nodes) across iteration, the graph can be loaded in memory once and reuse across iterations. This will reduce I/O overhead as there is no need to read and write to disk at each iteration. For fault resilience, there will be a periodic check point where every worker write their in-memory state to disk.

Also, Pregel (with its stateful characteristic), only send local computed result (but not the graph structure) over the network, which implies the minimal bandwidth consumption.

If you need to summarize that even further it is basically:

  • reducing I/O as much as possible
  • ensuring data locality

via: http://horicky.blogspot.com/2010/07/graph-processing-in-map-reduce.html