Music Analytics Opportunities

Once upon a time the words music listening habits were private as their bedrooms, music lovers used to buy the CDs, recordings and other physical copies of music and never publicly shared, Record companies were aware which radio station played their songs and where their CDs were popular, but that information painted an incomplete picture at best. Who knew what music people were sharing on tapes and CDs burnt in the privacy of their own bedrooms?

A traditional business metrics like number of CDs were sold and nothing happened after that, who purchased what and whom to assist what to buy, all this was anonymous. Thats all changed after explosion of online music sources like torrenting, music streaming sites and social media platforms, are now playing a very key role for music industry to understand their fans, spot upcoming talents like never before and anyones personal music interest nowadays becoming a public. Music analytics is now worth around $24.35 billion per year.

At the same time that the internet is taking power away from record labels, it is also giving them the ability to predict future hits.

What is new in Hadoop 2

Upcoming release of Hadoop, is becoming a major milestone in Hadoop development containing several significant improvements in HDFS and MapReduce(YARN) and also includes a very important new capabilities as well.

Hadoop 2 will be delivering a first release of new features like HDFS improvements including new append-pipeline, federation, wire compatibility, Namenode High Availability, HDFS Snapshots, better storage density and file formats, Caching and hierarchical storage management  and performance improvements. It is covering architectural improvements in High Availability of Namenode, Federation and Snapshots. Apache Hadoop YARN is the new basis for running MapReduce and other applications on a Hadoop cluster. It representing Hadoop as a more generic data integration and processing system. As we already discussed about MapReduce 2 (YARN) providing many more generic functionalities on data processing by simple and efficient ways.

One very good feature I would like to focus more is Namenode High Availability. Earlier versions of Hadoop has a single Nomenode controlling over the cluster, but it becoming a single point of failure(SPOF), if Namenode machine is unavailable, cluster as a whole would be unavailable till it either rebooted or replaced by another machine. Namenode High Availability feature address the same problem by providing option by providing two Namenodes (introduced StandbyNode, a hot backup of HDFS Namenode) sharing a same cluster with active/passive configuration.

Today in the market Hadoop 2.0.5-alpha version is available but still in under development, it includes new developer and user-facing incompatibilities, features, and major improvement. You can find the Hadoop 2.0.5-alpha release notes here. It is not really available for production but we can explore it for learning purpose and developing your POCs.

Apache Hadoop YARN : Next Generation MapReduce

MapReduce has a complete transformation in hadoop-0.x and now we have MapReduce v2 or YARN

Main inspiration behind development of MapReduce v2 that is YARN is to divide major functionality of JobTracker that resource management and job scheduling/monitoring into a  separate daemons. MapReduce v2 have a global resource management(RM) and Application Master per application(single client job or job workflows)

The Resource-Manager(RM) has authority to control over the Node-Manager(NM), the per-node slave and co-ordinates resources among all the applications in the system. The Application-Master(AM) is the framework, has a responsibility coordinating with Resource-Manager for resources negotiation and Node-Manager to execute and monitor the tasks.

As MapReduce v2 has two core responsibilities i.e.  resource management and job scheduling/monitoring so Resource-Manager(RM) have two core components, Scheduler and Applications-Manager

Scheduler is responsible for allocating execution time slots and resources to the various running applications as per the requirements/configurations, the Scheduler is pure Scheduler, it does not perform monitoring or status tracking of the application. The Scheduler performs its scheduling function as per the resource requirements of the applications; it does it through resource Container which examines elements such as memory, cpu, disk, network etc. 

Application-Manager(AM) is responsible for accepting the jobs, negotiating with Container for executing the application specific Application-Master and restarting the Application-Master Container on application failure or hardware failure. The Node-Manager is the per slave machine agent who is responsible for Containers, monitoring their resource usage and reporting the same to the Resource-Manager. The per-application Application-Master has the responsibility of negotiating appropriate resource Containers from the Scheduler, tracking their status and monitoring for progress.

MapReduce v2 jobs are compatible with all previous stable releases means all previous jobs will run on MapReduce v2 just need to recompile.

Reference:
http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html