Scale-out Beyond MapReduce
published: Sept. 27, 2013, recorded: August 2013, views: 20551
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The amount of data being collected is growing at a staggering pace. The default is to capture and store any and all data, in anticipation of potential future strategic value, and vast amounts of data are being generated by instrumenting key customer and systems touchpoints. Until recently, data was gathered for well-defined objectives such as auditing, forensics, reporting and line-of-business operations; now, exploratory and predictive analysis is becoming ubiquitous. These differences in data scale and usage are leading to a new generation of data management and analytic systems, where the emphasis is on supporting a wide range of data to be stored uniformly and analyzed seamlessly using whatever techniques are most appropriate, including traditional tools like SQL and BI and newer tools for graph analytics and machine learning. These new systems use scale-out architectures for both data storage and computation. Hadoop has become a key building block in the new generation of scale-out systems. Early versions of analytic tools over Hadoop, such as Hive and Pig for SQL-like queries, were implemented by translation into Map-Reduce computations. This approach has inherent limitations, and the emergence of resource managers such as YARN and Mesos has opened the door for newer analytic tools to bypass the Map-Reduce layer. This trend is especially significant for iterative computations such as graph analytics and machine learning, for which Map-Reduce is widely recognized to be a poor fit. In this talk, I will examine this architectural trend, and argue that resource managers are a first step in re-factoring the early implementations of Map-Reduce, and that more work is needed if we wish to support a variety of analytic tools on a common scale-out computational fabric. I will then present REEF, which runs on top of resource managers like YARN and provides support for task monitoring and restart, data movement and communications, and distributed state management. Finally, I will illustrate the value of using REEF to implement iterative algorithms for graph analytics and machine learning.
Download slides: kdd2013_ramakrishnan_map_reduce_01.pdf (6.4 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !
Write your own review or comment: