Hadoop Performance

Hadoop Performance

Hadoop is most commonly used as a platform for Big Data Analytics, as its ability to split files and distribute them across nodes in random clusters makes it superior in storing and processing extremely large data sets synonymous with big data more efficiently.

Hadoop is one such platform, and is made up of key components including a distributed file system (HDFS), and MapReduce, a programming model that reads massive amounts of data and performs analysis against it. The information is then read through Hadoop Common, an interface allowing computer systems to read data stored within the HDFS.

The Unpredictability of Big Data

Big Data is unlike normal structured data in that it is not “predictable”. It can be made up of millions of small files that have multiple connections, which a conventional file system would have trouble managing. Big Data can also be made up of an immense amount of single files, so large that they could exceed the boundaries of a single computer and all its attached storage.

In order for Hadoop to be effective, it needs to be able to store and process this extensive amount of data at an incredibly faster rate than it would in a more traditional supercomputer architecture with a parallel file system. This requirement for speed equates to Hadoop Performance.

Hadoop Performance, or the speed at which massive data sets can be analysed, is a critical requirement of a Hadoop implementation. Big Data is not just about scale, it’s about speed as the data needs to be analysed quickly enough to deliver “timely insights” , and Hadoop performance is vital in delivering this.

Unravelling Big Data with Hadoop Performance

Although Hadoop shows a lot of promise and is relatively inexpensive as a Big Data analytics tool, it has its own share of problems. One of the major issues affecting Hadoop Performance stems from the fact that Hadoop is not a single product - it is a framework built on an open source platform, made up of many modules and integrating with numerous other tools and products. Examples of the kind of integration we see in Hadoop include linking to traditional SQL databases, and also NOSQL databases.

It gets more complex when you consider the additional tools that are being developed for the Hadoop ecosystem, such as Sqoop for moving data between Hadoop and structured databases, and Oozie - a specialist job scheduler for Hadoop, as well as a myriad of supporting technologies. The complexity of Hadoop is further amplified in the form of development in Java, and while it is a widely used programming language, to date it has rarely been used on data-oriented development.

The ramifications of an ever-changing environment and a specific programming language can severely impact Hadoop Performance. And if performance is reduced, Hadoop cannot deliver the timely insights that are necessary in a market where the critical demand of Big Data analysis is rising.

Open Solution for Hadoop

To circumvent these issues and complexities, companies like NetApp are tailoring high performing infrastructure specifically to enhance faster performing Hadoop Performance. For example, NetApp’s EF-Series all-flash arrays and E-Series storage systems have been proven to improve Hadoop performance by as much as 50 percent, according to third party lab tests commissioned by the company.

Hadoop also benefits from NetApp’s solutions in addressing operational limitations of current Hadoop clusters by reducing the operational and functional costs associated with distributed software-based mirroring. Also, because it is a decoupled solution, the storage and compute independence provides flexibility in Hadoop to manage the independence of each other, as well as eliminating job impact and application disruption of drive failures.

NetApp provides a proven and certified solution that delivers a ready-to-deploy, linearly scalable, fully serviceable online, and fully compatible enterprise infrastructure for the Hadoop platform so that businesses can control and gain insights from big data more readily. This is accomplished through the following components:

· Hardware: Hadoop data storage array. One NetApp E5560 or E5660 storage array serves data for four DataNodes. Using RAID 5, the storage array protects the data and minimizes the performance impact of disk drive failures.

· Hadoop distribution. NetApp solutions for Hadoop are certified with Cloudera’s Distribution and include Apache Hadoop (CDH) and Hortonworks Data Platform, which can be downloaded from the vendors’ website.

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