From the post:
Adapting MapReduce for a higher performance has been one of the popular discussion topics. Let’s continue with our series on Adaptive MapReduce and explore the feature available via JAQL in IBM BigInsights commercial offering. This implementation also points to a much more vital corollary that enterprise offerings of Apache Hadoop are not just mere packaging and re-sell but have a bigger research initiative going on beneath the covers.
Two papers are explored by the post:
 Rares Vernica, Andrey Balmin, Kevin S. Beyer, Vuk Ercegovac: Adaptive MapReduce using situation-aware mappers. EDBT 2012: 420-431
We propose new adaptive runtime techniques for MapReduce that improve performance and simplify job tuning. We implement these techniques by breaking a key assumption of MapReduce that mappers run in isolation. Instead, our mappers communicate through a distributed meta-data store and are aware of the global state of the job. However, we still preserve the fault-tolerance, scalability, and programming API of MapReduce. We utilize these “situation-aware mappers” to develop a set of techniques that make MapReduce more dynamic: (a) Adaptive Mappers dynamically take multiple data partitions (splits) to amortize mapper start-up costs; (b) Adaptive Combiners improve local aggregation by maintaining a cache of partial aggregates for the frequent keys; (c) Adaptive Sampling and Partitioning sample the mapper outputs and use the obtained statistics to produce balanced partitions for the reducers. Our experimental evaluation shows that adaptive techniques provide up to 3x performance improvement, in some cases, and dramatically improve performance stability across the board.
 Andrey Balmin, Vuk Ercegovac, Rares Vernica, Kevin S. Beyer: Adaptive Processing of User-Defined Aggregates in Jaql. IEEE Data Eng. Bull. 34(4): 36-43 (2011)
Adaptive techniques can dramatically improve performance and simplify tuning for MapReduce jobs. However, their implementation often requires global coordination between map tasks, which breaks a key assumption of MapReduce that mappers run in isolation. We show that it is possible to preserve fault-tolerance, scalability, and ease of use of MapReduce by allowing map tasks to utilize a limited set of high-level coordination primitives. We have implemented these primitives on top of an open source distributed coordination service. We expose adaptive features in a high-level declarative query language, Jaql, by utilizing unique features of the language, such as higher-order functions and physical transparency. For instance, we observe that maintaining a small amount of global state could help improve performance for a class of aggregate functions that are able to limit the output based on a global threshold. Such algorithms arise, for example, in Top-K processing, skyline queries, and exception handling. We provide a simple API that facilitates safe and efficient development of such functions.
The bar for excellence in the use of Hadoop keeps getting higher!