Around the year 2003, Doug Cutting and Mike Cafarella started work on a project called Nutch, a highly extensible, feature-rich, and open source crawler and indexer project. The goal was to provide an off-the-shelf crawler to meet the demands of document discovery. Nutch can work in a distributed fashion on a handful of machines and be polite by respecting the robots.txt file on websites. It is highly extensible by providing the plugin architecture for developers to add custom components, for example, third-party plugins, to read different media types from the Web.
Note
Robot Exclusion Standard or the robots.txt protocol is an advisory protocol that suggests crawling behavior. It is a file placed on website roots that suggest the public pages and directories that can or cannot be crawled. One characteristic of a polite crawler is its respect for the advisory instructions placed within the robots.txt file.
Nutch, together with indexing technologies such as Lucene and Solr, provided the necessary components to build search engines, but this project was not at web scale. The initial demonstration of Nutch involved crawling 100 million web pages using four machines. Moreover, debugging and maintaining it was tedious. In 2004, concepts from the seminal MapReduce and GFS publications from Google addressed some of Nutch's scaling issues. The Nutch contributors started integrating distributed filesystem features and the MapReduce programming model into the project. The scalability of Nutch improved by 2006, but it was not yet web scale. A few 100 million web documents could be crawled and indexed using 20 machines. Programming, debugging, and maintaining these search engines became easier.
In 2006, Yahoo hired Doug Cutting, and Hadoop was born. The Hadoop project was part of Apache Software Foundation (ASF), but was factored out of the existing Nutch project and allowed to evolve independently. A number of minor releases were done between 2006 and 2008, at the end of which Hadoop became a stable and web-scale data-processing MapReduce framework. In 2008, Hadoop won the terabyte sort benchmark competition, announcing its suitability for large-scale, reliable cluster-computing using MapReduce.
The Hadoop project has a long genealogy, starting from the early releases in 2007 and 2008. This project that is part of Apache Software Foundation (ASF) will be termed Apache Hadoop throughout this book. The Apache Hadoop project is the parent project for subsequent releases of Hadoop and its distribution. It is analogous to the main stem of a river, while branches or distributions can be compared to the distributaries of a river.
The following figure shows the Hadoop lineage with respect to Apache Hadoop. In the figure, the black squares represent the major Apache Hadoop releases, and the ovals represent the distributions of Hadoop. Other releases of Hadoop are represented by dotted black squares.
Apache Hadoop has three important branches that are very relevant. They are:
The 0.20.1 branch
The 0.20.2 branch
The 0.21 branch
The Apache Hadoop releases followed a straight line till 0.20. It always had a single major release, and there was no forking of the code into other branches. At release 0.20, there was a fan out of the project into three major branches. The 0.20.2 branch is often termed MapReduce v1.0, MRv1, or simply Hadoop 1.0.0. The 0.21 branch is termed MapReduce v2.0, MRv2, or Hadoop 2.0. A few older distributions are derived from 0.20.1. The year 2011 marked a record number of releases across different branches.
There are two other releases of significance, though they are not considered major releases. They are the Hadoop-0.20-append and Hadoop-0.20-Security releases. These releases introduced the HDFS append and security-related features into Hadoop, respectively. With these enhancements, Apache Hadoop came closer to becoming enterprise-ready.
Append is the primary feature of the Hadoop-0.20-append release. It allows users to run HBase without the risk of data loss. HBase is a popular column-family store that runs on HDFS, providing an online data store in a batch-oriented Hadoop environment. Specifically, the append feature helps write durability of HBase logs, ensuring data safety. Traditionally, HDFS supported input-output for MapReduce batch jobs. The requirement for these jobs was to open a file once, write a lot of data into it, and close the file. The closed file was immutable and read many times. The semantics supported were write-once-read-many-times. No one could read the file when a write was in progress.
Any process that failed or crashed during a write had to rewrite the file. In MapReduce, a user always reran tasks to generate the file. However, this is not true for transaction logs for online systems such as HBase. If the log-writing process fails, it can lead to data loss as the transaction cannot be reproduced. Reproducibility of a transaction, and in turn data safety, comes from log writing. The append feature in HDFS mitigates this risk by enabling HBase and other transactional operations on HDFS.
The Hadoop team at Yahoo took the initiative to add security-related features in the Hadoop-0.20-Security release. Enterprises have different teams, with each team working on different kinds of data. For compliance, client privacy, and security, isolation, authentication, and authorization of Hadoop jobs and data is important. The security release is feature-rich to provide these three pillars of enterprise security.
The full Kerberos authentication system is integrated with Hadoop in this release. Access Control Lists (ACLs) were introduced on MapReduce jobs to ensure proper authority in exercising jobs and using resources. Authentication and authorization put together provided the isolation necessary between both jobs and data of the different users of the system.
The following figure gives a timeline view of the major releases and milestones of Apache Hadoop. The project has been there for 8 years, but the last 4 years has seen Hadoop make giant strides in big data processing. In January 2010, Google was awarded a patent for the MapReduce technology. This technology was licensed to the Apache Software Foundation 4 months later, a shot in the arm for Hadoop. With legal complications out of the way, enterprises—small, medium, and large—were ready to embrace Hadoop. Since then, Hadoop has come up with a number of major enhancements and releases. It has given rise to businesses selling Hadoop distributions, support, training, and other services.
Hadoop 1.0 releases, referred to as 1.X in this book, saw the inception and evolution of Hadoop as a pure MapReduce job-processing framework. It has exceeded its expectations with a wide adoption of massive data processing. The stable 1.X release at this point of time is 1.2.1, which includes features such as append and security. Hadoop 1.X tried to stay flexible by making changes, such as HDFS append, to support online systems such as HBase. Meanwhile, big data applications evolved in range beyond MapReduce computation models. The flexibility of Hadoop 1.X releases had been stretched; it was no longer possible to widen its net to cater to the variety of applications without architectural changes.
Hadoop 2.0 releases, referred to as 2.X in this book, came into existence in 2013. This release family has major changes to widen the range of applications Hadoop can solve. These releases can even increase efficiencies and mileage derived from existing Hadoop clusters in enterprises. Clearly, Hadoop is moving fast beyond MapReduce to stay as the leader in massive scale data processing with the challenge of being backward compatible. It is becoming a generic cluster-computing and storage platform from being only a MapReduce-specific framework.