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



MapReduce and Hadoop Future

In the light of ☞ Google Caffeine announcement — a summary of a summary would be that Google replaced MapReduce-based index updates with a new engine that would provide more timely updates — ☞ Tony Bain is wondering if Michael Stonebraker and DeWitt’ paper ☞ MapReduce: a major step backwards hasn’t thus been proved to be correct:

Firstly, was Stonebraker and Dewitt right? It is red faced time for those who came out and aggressively defended the Map/Reduce architecture?

And secondly what impact does this have on the future of Map/Reduce now those responsible for its popularity seem to have migrated their key use case? Is the proposition for Map/Reduce today still just as good now the Google don’t do it? (Yes I am sure Google still use Map/Reduce extensively and this is a bit tongue in cheek. But the primary quoted example relates to building the search index which is what, reportedly, has been moved away from MR).

While all these questions seem to be appropriate, I think some details could help with finding the correct answers.

Firstly, I think Google’s decission to “drop” MapReduce-based index updates was determined by their particular implementation and their storage strategy. Simply put, Google’s MapReduce-based index updates required reprocessing of data, so providing timely updates was more or less impossible. But as proved by CouchDB mapreduce implementation this approach is not the only one possible. CouchDB views are built as a result of running a pair of map and reduce functions and storing it in btrees. As for updates, CouchDB doesn’t need to reprocess all initial data and rebuild the index from scratch, but only apply changes from the updates. In this regard, Stonebraker seem to have been right when saying that it is “a sub-optimal implementation, in that it uses brute force instead of indexing”.

While Hadoop, the most well know mapreduce implementation, is following closely Google’s design, that doesn’t mean that there isn’t work done to improve its behavior for special scenarios like real-time stream processing, cascading, etc.

As regards the questions related to the impact of Google’s announcement on MapReduce adoption, I’d say that taking a look at the reports from the Hadoop Summit we all would agree that for quite some time the biggest proponents of MapReduce (in its Hadoop incarnation) have been Yahoo!, Facebook, Twitter, and other such companies. And, as I said it before, it sounds like Hadoop is actually processing more data than Google’s MapReduce .

Last, but not least, as with any NoSQL technology all these do not mean that MapReduce or Hadoop will fit all scenarios.

Original title and link: MapReduce Future (NoSQL databases © myNoSQL)