Working of Hadoop
Let's now see the internals of Hadoop and its components, it's architecture, and how it works in this section. We will start off by understanding some of Hadoop’s core architecture principles, and then we will explain its architecture and important components in detail.
Hadoop core architecture principles
Hadoop was built and conceived with well-defined architecture goals and principles, as listed here, (the following are in no way authoritative as we can't find one; rather we gathered this from https://goo.gl/3nvERl):
- Linear scalability (Scale-Out rather than Scale-Up): Add more nodes for scalability to increase data storage and computing power.
- Bring code to data rather than data to code: In big data, data is usually huge and code working on data is small. So, this principle states that bring or distribute code to the nodes/machines where it can act on data and not distribute or move data. In essence, it means minimize data transfer and distribute code instead.
- Deal with failures...
