A simple optimization of top-k queries that can make a huge difference: going from the default behavior of:

1. sifting through all the data (necessary),
2. sorting it all (necessary),
3. writing all the results to disk (unnecessary—saving all the limit results from each map is enough), and
4. having the reducer process again all the data (unnecessary—the previous step already reduced the amount of data down to the limit * number_of_partitions).

For reference a top-k query is:

SELECT * FROM T ORDER BY a DESC LIMIT 10

Original title and link: Hive Top-K Optimization (NoSQL database©myNoSQL)

via: http://www.qubole.com/blog/index.php/top-k-optimization/

Derrick Harris for GigaOm:

Two key members of the Facebook team that created the Hadoop query language Hive are launching their own big data startup called Qubole on Thursday. […] Qubole is also optimized to run on cloud-based resources that typically don’t offer performance on a par with their physical counterparts. Thusoo said the product incorporates a specially-designed cache system that lets queries run five times faster than traditional Hadoop jobs in the cloud, and users have the option to change the types of instances their jobs are running on if the situation requires.

Running on Amazon infrastructure.

Original title and link: Qubole: New On-Demand Hadoop Service by Hive Creators (NoSQL database©myNoSQL)

via: http://gigaom.com/cloud/exclusive-the-brains-behind-hive-launch-on-demand-hadoop-service/