Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Save more on your purchases now! discount-offer-chevron-icon
Savings automatically calculated. No voucher code required.
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Clojure Data Analysis Cookbook - Second Edition

You're reading from   Clojure Data Analysis Cookbook - Second Edition Dive into data analysis with Clojure through over 100 practical recipes for every stage of the analysis and collection process

Arrow left icon
Product type Paperback
Published in Jan 2015
Publisher
ISBN-13 9781784390297
Length 372 pages
Edition 2nd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Eric Richard Rochester Eric Richard Rochester
Author Profile Icon Eric Richard Rochester
Eric Richard Rochester
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Importing Data for Analysis 2. Cleaning and Validating Data FREE CHAPTER 3. Managing Complexity with Concurrent Programming 4. Improving Performance with Parallel Programming 5. Distributed Data Processing with Cascalog 6. Working with Incanter Datasets 7. Statistical Data Analysis with Incanter 8. Working with Mathematica and R 9. Clustering, Classifying, and Working with Weka 10. Working with Unstructured and Textual Data 11. Graphing in Incanter 12. Creating Charts for the Web Index

Defining new Cascalog operators


Cascalog comes with a number of operators; however, you'll often need to define your own, as we saw in the Aggregating data in Cascalog recipe.

For different uses, Cascalog defines a number of different categories of operators, each with different properties. Some are run in the map phase of processing, and some are run in the reduce phase. The ones in the map phase can use a number of extra optimizations, so if you can push some of your processing into that stage, you'll get better performance. In this recipe, you'll see which categories of operators are on the map side and which are on the reduce side. We'll also provide an example of each and see how they fit into the larger processing model.

Getting ready

For this recipe, we'll use the same dependencies and inclusions that we did in the Initializing Cascalog and Hadoop for distributed processing recipe. We'll also use the Doctor Who companion data from that recipe.

How to do it…

As I mentioned, Cascalog allows...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime