How does a Hadoop system handle real-time tasks to avoid latency?

In the initial phase of Apache Hadoop, it mainly supports similar search engine functions. Today, Hadoop has been adopted by dozens of industries that rely on large data calculations to improve business processing performance. Governments, manufacturing, healthcare, retailing and other sectors are increasingly benefiting from economic development and Hadoop computing, while companies constrained by traditional corporate solutions will find competition increasingly brutal.

Choosing a suitable Hadoop release is as necessary as applying Hadoop in your business. Ultimately, you'll find out which version of the Hadoop distribution depends on the specifications of the host, although performance and scalability are the two key features you should examine carefully. Let's take a look at some specific Hadoop performance and extensibility requirements, as well as requirements for several key architectures.

Performance

Enterprises need to get rid of traditional database solutions to manage data, mainly to increase raw performance and gain scalability. This may surprise you because not all the Hadoop distribution systems created are the same.

In my other article, it was said that adding a 250 millisecond delay could ruin the whole line of sales, and we can see why the low performance (high latency) can be hard to bear. The slow performance of the site will reduce the online sales conversion rate by 7%, which means a large flow of online retailers mean millions of dollars loss.

As you can see in the figure below, comparing the MAPR M7 version to another Hadoop release, the difference in latency means different performance, and the performance gap between distributions is staggering.

When you consider the real-time application of Hadoop, such as the real time application of the financial security system, the requirement for high performance increases is even higher.

Thanks in particular to technology like Hadoop, which makes it increasingly difficult for financial criminals to steal digital assets, financial services companies such as Zions Bank have now been able to stop financial fraud before their bank clients feel any real impact. High performance and reliability are necessary for analysis and real-time data response, which can prevent destructive fraud.

Extensibility

Another major advantage of Hadoop is scalability. Instead of restricting data throughput through a single enterprise server, Hadoop can perform distributed processing of large datasets across a cluster of computers, eliminating the data ceiling by using a one-stop solution between multiple parts of the commercial hardware.

This architecture is just the starting point for data scalability improvements, and is far from over. For scalability, there are three more aspects to consider in the Hadoop platform:

File bottlenecks

The Hadoop default architecture utilizes a single namenode as the master node for the remaining data nodes. Because of a single namenode, all data is forced into a bottleneck, which limits the Hadoop cluster to only 50 million to 200 million files.

The implementation of a single Namenode also requires the use of commercial-grade NAS, rather than a budget-friendly commercial hardware.

A better choice for a single Namenode architecture is to use a distributed metadata structure. The following provides a visual comparison of two architectures:

As you can see, the distributed metadata architecture uses purely commercialized hardware, which not only saves costs, but also improves performance by up to 10-20 times, getting rid of file bottlenecks, reaching a maximum file count of 1 billion, and increasing capacity by 5,000 times times than a single namenode architecture, This is indeed a great success.

Node extensions

Some of the smaller users of Hadoop do not have high requirements for data storage and processing, so they can run on fewer nodes, while some Hadoop implementations can scale up to thousands of nodes.

This is also a great place for Hadoop scalability. Extending from an entry-level, large data implementation to a cluster with thousands of nodes is easy, and increasing the amount of commercialized hardware as required can minimize costs, which involves the cost of data processing and the costs involved in increasing requirements.

Node capacity

In addition to the number of nodes, Hadoop users should also examine each processing and storage capacity, taking into account physical storage limits. You can use nodes with higher disk density to reduce the total number of nodes, while also ensuring data storage requirements.

Architecture Basics

The performance and scalability of Hadoop can be further enhanced if you want to have the idea of a multiple architecture based distributed system.

Reduce the software layer

Too many layers of software can lead to increased navigation costs, making it difficult to improve the performance of Hadoop systems.

Make all applications run on the same platform

Some Hadoop distributions may require you to create multiple instances, and an optimization execution will allow all workloads in the same environment to be processed concurrently, which reduces the generation of duplicate data, thereby increasing scalability and performance.

Leverage public cloud platforms for better resiliency and scalability

A good release allows you to flexibly use Hadoop and a reliable cloud environment within your own firewall, such as Amazon Web services and Google computing engines.

Finally, choosing the right Hadoop distribution should meet business needs, not just the current requirements but also the future needs. Analyze the performance and scalability of each release, taking into account the architectural underpinnings, which are the foundation for successful implementation and evaluation of Hadoop within your organization.