By Jean-Marc Spaggiari, Kevin O'Dell
Lots of HBase books, on-line HBase courses, and HBase mailing lists/forums can be found if you would like to grasp how HBase works. but when you must take a deep dive into use circumstances, gains, and troubleshooting, Architecting HBase functions is the best resource for you.
With this booklet, you’ll research a managed set of APIs that coincide with use-case examples and simply deployed use-case versions, in addition to sizing/best practices to assist bounce commence your business software improvement and deployment.
- Learn layout patterns—and not only components—necessary for a profitable HBase deployment
- Go intensive into the entire HBase shell operations and API calls required to enforce documented use cases
- Become accustomed to the most typical matters confronted via HBase clients, establish the reasons, and comprehend the consequences
- Learn document-specific API calls which are difficult or extremely important for users
- Get use-case examples for each subject presented
Read or Download Architecting HBase Applications: A Guidebook for Successful Development and Design PDF
Best data mining books
Data Mining in Agriculture represents a finished attempt to supply graduate scholars and researchers with an analytical textual content on info mining ideas utilized to agriculture and environmental similar fields. This e-book provides either theoretical and sensible insights with a spotlight on featuring the context of every facts mining procedure really intuitively with abundant concrete examples represented graphically and with algorithms written in MATLAB®.
This ebook includes worthy reviews in information mining from either foundational and useful views. The foundational experiences of information mining might help to put a high-quality beginning for information mining as a systematic self-discipline, whereas the sensible reviews of knowledge mining could lead on to new information mining paradigms and algorithms.
This publication constitutes the refereed complaints of the seventeenth overseas convention on information Warehousing and information Discovery, DaWaK 2015, held in Valencia, Spain, September 2015. The 31 revised complete papers provided have been rigorously reviewed and chosen from ninety submissions. The papers are equipped in topical sections similarity degree and clustering; facts mining; social computing; heterogeneos networks and knowledge; information warehouses; circulation processing; purposes of huge info research; and massive info.
This e-book is dedicated to the modeling and realizing of complicated city structures. This moment quantity of realizing advanced city platforms makes a speciality of the demanding situations of the modeling instruments, relating, e. g. , the standard and volume of knowledge and the choice of a suitable modeling procedure. it truly is intended to aid city decision-makers—including municipal politicians, spatial planners, and citizen groups—in determining a suitable modeling process for his or her specific modeling requisites.
- Programmatic Advertising: The Successful Transformation to Automated, Data-Driven Marketing in Real-Time
- Event-Driven Surveillance: Possibilities and Challenges
- Persuasive Recommender Systems: Conceptual Background and Implications
- Advances in Research Methods for Information Systems Research: Data Mining, Data Envelopment Analysis, Value Focused Thinking
- Twitter Data Analytics (SpringerBriefs in Computer Science)
- Data Mining Cookbook
Additional info for Architecting HBase Applications: A Guidebook for Successful Development and Design
Counting from MapReduce The second way to count the number of rows in an HBase table is to use the Row‐ Counter MapReduce tool. The big benefit of using MapReduce to count your rows is HBase will create one mapper per region in your table. For a very big table this will distribute the work on multiple nodes to perform the count operation in parallel instead of scanning regions sequentially, which is what the shell’s count command does. RowCounter sensors Here is the most important part of the output and we will detail below the important fields to look at.
This is one of the HFiles we have initially created. By looking at the size of this file and by com‐ paring it to the initial HFiles created by the MapReduce job, we can match it to ch09/hfiles/v/ed40f94ee09b434ea1c55538e0632837. You can also look at the other regions and map them to the other input HFiles. Data validation Now that data is in the table we need to verify that it is expected. The first thing we will do is to make sure we have as many rows as expected. Then we will verify the records contain what we expect.
Table size Looking into an HFile using the HFilePrettyPrinter gives us the number of cells within a single HFile, but how many unique rows does it really represent? Since an HFile only represents a subset of rows, we need to count rows at the table level. HBase provides two different mechanisms to count the rows. 30 | Chapter 2: Underlying storage engine - Implementation Counting from the shell Counting the rows from the shell is pretty straightforward, simple, and efficient for small examples.