When it comes to high-performance computing, we think of sophisticated, expensive, and overly technological servers. However, you can get results as good or even superior from a solution called a computer cluster.
Cluster is a technology capable of making computers more straightforward to work together as if they were a single machine or a single system. Computer clusters have each node configured to perform the same task, controlled and software-programmed.
If we had ten machines forming the Cluster, the software responsible for storing these ten machines would be Hadoop HDFS, a file system developed within the Apache Hadoop Framework that aims to control, manage, and program within a machine environment. So we take multiple computers, connect to the same network, and put a layer of software to manage all these machines so they can behave like just one.
Hadoop HDFS does this for distributed storage, and MapReduce does this for distributed processing, just as Apache Spark also does for distributed processing. Hive or HBase uses HDFS to process and store data on multiple machines.
The concept of clustering on computers is a group of machines working together, controlled, and programmed by software. Each computer that is part of the Cluster is named node, with no node limits.
As a data scientist, we will process the data in Hadoop regardless of whether we process through a machine or 5,000 machines — this has to be transparent to the end-user. On the other hand, the data engineer will ensure good performance, safety and that all devices are operating correctly.
Nodes in a cluster must be interconnected, preferably by a network technology known for maintenance and cost control purposes. The most commonly used network type of connecting a Cluster is ethernet, a local network to interconnect machines to communicate.
Cluster computing is a viable solution because cluster nodes can be composed of elementary computers: low-cost, medium-configuration machines. Therefore, a company can take a few dozen simple configuration machines and assemble a cluster of computers and use the processing capacity of these dozens of devices as if they were a large server.
In general, these low-cost machines may have a lower total cost than buying a single large server, which is likely to have a much higher price.
According to the application, there are several types, with the ultimate goal of building the Cluster. When working with databases for critical applications within a company, the database is hardly placed on only one machine, especially in large companies requiring a system running 24 hours a day.
A high availability cluster is configured so that two servers work together so that the database does not stop working if one of these two servers falls. A company that offers a website, e-commerce, or web service has web servers with high availability where it can have half a dozen machines. In the face of some unforeseen or peak access, one device covers the other, and we have stable performance.
This type of Cluster is targeted at applications that are very demanding concerning the processing. Systems used in scientific research can benefit from this type of Cluster because it needs to analyze various data and perform very complex calculations quickly. The focus of this type of Cluster is to allow application-driven processing to deliver satisfactory results promptly.
The goal was to try to process as much as possible in the shortest possible time. When this is required for an application, we can deliver the high-performance Cluster.
One gigaflop corresponds to 1 billion instructions/second.
Therefore, depending on the application and the number of instructions required for the high processing of a large volume of data, we will increasingly need gigaflops and rely on a high-performance cluster.
This high-performance Cluster is typically a type of Cluster that we set up for Apache Hadoop. After all, we process machine learning models or even data analysis processes that need to be executed promptly.
High Availability Cluster
The goal is that the application that is served by the Cluster does not stop working. With Apache Hadoop, we typically set up a high-performance cluster to process the application in the shortest possible time.
However, in some cases, the application is so critical that we cannot interrupt processing; that is, we can also configure Apache Hadoop in high availability mode. If one server crashes, the other will continue to fulfill the requests.
A high availability cluster is mission-critical. Therefore, it is up to the company whether the Data Lake where we put Apache Hadoop with the Cluster needs high availability or not.
Cluster for Load Balancing
This type of Cluster is widely used on Web servers, a computer that runs the web service to meet the request of the page. When we open the browser and type in the LinkedIn address, we are directed to the webserver where all LinkedIn pages are located.
During the day, there are access spikes and should increase the number of web servers to balance the load. When accesses return to average, the servers are shut down gradually to pay only the infrastructure for the time of use.
We can build a cluster that has all of this. In the Hadoop cluster, it is widespread for us to have high performance and high availability, and load balancing is not very common in a Hadoop Cluster. Load balancing is more common on Web servers and databases.
In practice, some applications can use these clusters: web server, databases or systems, databases, or anything that requires high performance, high availability, or load balancing.
And there we have it. I hope you have found this helpful. Thank you for reading. 🐼🤏