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Apache Hadoop Code Quality: Production VS Test

In order to get high-quality production code, it's not enough just to ensure maximum coverage with tests. No doubt, great results require the main project code and tests to work efficiently together. Therefore, tests have to be paid as much attention to as source code. A decent test is a key success factor, as it will catch regression in production. Let's take a look at PVS-Studio static analyzer warnings to see the importance of the fact that errors in tests are no worse than the ones in production. Today's focus: Apache Hadoop. 

About the Project

Those who were formerly interested in Big Data have probably heard or worked with the Apache Hadoop project. In a nutshell, Hadoop is a framework that can be used as a basis for building and working with Big Data systems.   

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PVS-Studio Visits Apache Hive

For the past ten years, the open-source movement has been one of the key drivers of the IT industry's development. The role of open source projects is becoming more and more prominent, not only in terms of quantity but also in terms of quality. This changes the very concept of how open source software is positioned on the IT market in general. Today, we are going to talk about Apache Hive.

Hadoop and Apache Hive

About Apache Hive

Apache Hadoop is currently thought to be one of the pioneering Big Data technologies. Its primary tasks are storing, processing, and managing large amounts of data. The main components comprising the framework are Hadoop Common, HDFS, Hadoop MapReduce, and Hadoop YARN. Over time, a large ecosystem of related projects and technologies has developed around Hadoop — many of which originally started as part of the project and then budded off to become independent. Apache Hive is one of them.