July 2016
Beginner to intermediate
252 pages
6h 16m
English
Content preview from Architecting HBase Applications
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Chapter 1. What Is HBase?
Back in the 1990s, Google started to index the Web, and rapidly faced some challenges.
The first challenge was related to the size of the data to store: the Web was quickly growing from a few tens of millions of pages to the more than one billion pages we have today. Indexing the Web was becoming harder and harder with each passing day.
This led to the creation of the Google File System (GFS), which Google used internally, and in 2006, the company published “Bigtable: A Distributed Storage System for Structured Data,” a white paper on GFS. The open source community saw an opportunity and within the Apache Lucene search project started to implement a GFS equivalent filesystem, Hadoop. After some months of development as part of the Apache Lucene project, Hadoop became its own Apache project.
As Google began to store more and more data, it soon faced another challenge. This time it was related to the indexing of mass volumes of data. How do you store a gigantic index spread over multiple nodes, while maintaining high consistency, fail-over, and low-latency random reads and random writes? Google created an internal project known as BigTable to meet that need.
Yet again, the Apache open source community saw a great opportunity for leveraging the BigTable white paper and started the implementation of HBase. Apache HBase was originally started as part of the Hadoop project.
Then, in May 2010, HBase graduated to become its own top-level Apache project. And today, ...
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