Lecture 4: Cluster Computing and MapReduce

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Battle Camp: Final 5 Episodes with Dana Moon + Interview with the Winner! 1:03:29

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Finally, we find out who is unbeatable, unhateable, and unbreakable in the final five episodes of Battle Camp Season One. Host Chris Burns is joined by the multi-talented comedian Dana Moon to relive the cockroach mac & cheese, Trey’s drag debut, and the final wheel spin. The Season One Winner joins Chris to debrief on strategy and dish on game play. Leave us a voice message at www.speakpipe.com/WeHaveTheReceipts Text us at (929) 487-3621 DM Chris @FatCarrieBradshaw on Instagram Follow We Have The Receipts wherever you listen, so you never miss an episode. Listen to more from Netflix Podcasts.…

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Lecture 4: Cluster Computing and MapReduce

Scale Cast – A podcast about big data, distributed systems, and scalability

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published לפני 17 שנים

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9 פרקים

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1
An Introduction to ZooKeeper Video

לפני 17 years

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In 2006 we were building distributed applications that needed a master, aka coordinator, aka controller to manage the sub processes of the applications. It was a scenario that we had encountered before and something that we saw repeated over and over again inside and outside of Yahoo!. For example, we have an application that consists of a bunch of processes. Each process needs be aware of other processes in the system. The processes need to know how requests are partitioned among the processes. They need to be aware of configuration changes and failures. Generally an application specific central control process manages these needs, but generally these control programs are specific to applications and thus represent a recurring development cost for each distributed application. Because each control program is rewritten it doesn’t get the investment of development time to become truly robust, making it an unreliable single point of failure. link to podcast…

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More Optimal Bloom Filters

לפני 17 years

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The Bloom filter, conceived by Burton H. Bloom in 1970, is a space-efficient probabilistic data structure that is used to test whether an element is a member of a set. False positives are possible, but false negatives are not. Elements can be added to the set, but not removed (though this can be addressed with a counting filter). The more elements that are added to the set, the larger the probability of false positives. For example, one might use a Bloom filter to do spell-checking in a space-efficient way. A Bloom filter to which a dictionary of correct words has been added will accept all words in the dictionary and reject almost all words which are not, which is good enough in some cases. Depending on the false positive rate, the resulting data structure can require as little as a byte per dictionary word. In the last few years Bloom filter become hot topic again and there were several modifications and improvements. In this talk I will present my last few improvements in this topic. Speaker: Ely Porat Ely Porat received his Doctorate from Bar-Ilan University in 2000. Following that, he fulfilled his military service and, in parallel, worked as a faculty member at Bar-Ilan University. Having spent the spring 2007 semester as a Visiting Scientist in Google, he is now back at Bar-Ilan University. The main body of Ely Porat’s work concerns matching problems: string matching, pattern matching, subset matching. He also worked on the nearest pair problem in high-dimensional spaces as well as sketching and edit distance. link…

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An Overview of High Performance Computing and Challenges for the Future

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In this talk we examine how high performance computing has changed over the last 10-year and look toward the future in terms of trends. These changes have had and will continue to have a major impact on our software. A new generation of software libraries and algorithms are needed for the effective and reliable use of (wide area) dynamic, distributed and parallel environments. Some of the software and algorithm challenges have already been encountered, such as management of communication and memory hierarchies through a combination of compile–time and run–time techniques, but the increased scale of computation, depth of memory hierarchies, range of latencies, and increased run–time environment variability will make these problems much harder. Link to video…

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Disk-Based Parallel Computation, Rubik’s Cube, and Checkpointin

לפני 17 years

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This talk takes us on a journey through three varied, but interconnected topics. First, our research lab has engaged in a series of disk-based computations extending over five years. Disks have traditionally been used for filesystems, for virtual memory, and for databases. Disk-based computation opens up an important fourth use: an abstraction for multiple disks that allows parallel programs to treat them in a manner similar to RAM. The key observation is that 50 disks have approximately the same parallel bandwidth as a _single_ RAM subsystem. This leaves latency as the primary concern. A second key is the use of techniques like delayed duplicate detection to avoid latency link to video…

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Lecture 5: Cluster Computing and MapReduce

לפני 17 years

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Lecture 3: Cluster Computing and MapReduce

לפני 17 years

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Lecture 4: Cluster Computing and MapReduce

לפני 17 years

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Lecture 2: Cluster Computing and MapReduce

לפני 17 years

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1
Lecture 1: Cluster Computing and MapReduce

לפני 17 years

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Lecture 1 in a five part series introducing mapreduce and cluster computing. See http://code.google.com/edu/&#8230 ; for slides and other resources. Link to video

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Lecture 4: Cluster Computing and MapReduce