September 2014
Intermediate to advanced
512 pages
13h 54m
English
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Chapter 7. Utilizing data structures and algorithms at scale
This chapter covers
- Representing and using data structures such as graphs, HyperLogLog, and Bloom filters in MapReduce
- Applying algorithms such as PageRank and semi-joins to large amounts of data
- Learning how social network companies recommend making connections with people outside your network
In this chapter we’ll look at how you can implement algorithms in MapReduce to work with internet-scale data. We’ll focus on nontrivial data, which is commonly represented using graphs.
We’ll also look at how you can use graphs to model connections between entities, such as relationships in a social network. We’ll run through a number of useful algorithms that can be performed over graphs, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.