Hadoop Distributed File System (HDFS) Meaning
HDFS is the storage system of Hadoop framework. It is a distributed file system that can conveniently run on commodity hardware for processing unstructured data. Due to this functionality of HDFS, it is capable of being highly fault-tolerant. Here, data is stored in multiple locations, and in the event of one storage location failing to provide the required data, the same data can be easily fetched from another location. It owes its existence to the Apache Nutch project, and today it is a top-level Apache Hadoop project. Currently, HDFS is a major constituent of Hadoop, along with Hadoop YARN, Hadoop MapReduce, and Hadoop Common.
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Features of HDFS
HDFS is a highly scalable and reliable storage system for the Big Data platform, Hadoop. Working closely with Hadoop YARN for data processing and data analytics, it improves the data management layer of the Hadoop cluster making it efficient enough to process big data, concurrently. HDFS also works in close coordination with HBase. Let’s find out some of the highlights that make this technology special.
| HDFS Key Features | Description |
| Storing bulks of data | HDFS is capable of storing terabytes and petabytes of data. |
| Minimum intervention | It manages thousands of nodes without operators’ intervention. |
| Computing | HDFS provides the benefits of distributed and parallel computing at once. |
| Scaling out | It works on scaling out, rather than on scaling up, without a single downtime. |
| Rollback | HDFS allows returning to its previous version post an upgrade. |
| Data integrity | It deals with corrupted data by replicating it several times. |
- Servers in HDFS are fully connected, and they communicate through TCP-based protocols.
- Though designed for huge databases, normal file systems (FAT, NTFS, etc.) can also be viewed.
- Current status of a node is obtained through the checkpoint node.
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HDFS Architecture
Hadoop Distributed File System follows the master–slave data architecture. Each cluster comprises a single Namenode that acts as the master server in order to manage the file system namespace and provide the right access to clients. The next terminology in the HDFS cluster is the Datanode that is usually one per node in the HDFS cluster. The Datanode is assigned with the task of managing the storage attached to the node that it runs on. HDFS also includes a file system namespace that is being executed by the Namenode for general HDFS Operations like file opening, closing, and renaming, and even for directories. The Namenode also maps the blocks to Datanodes.
Namenode and Datanode are actually Java programming codes that can be extensively run on commodity hardware machines. These machines could most probably be running on Linux OS or GNU. The entire HDFS is based on the Java programming language. The single Namenode on a Hadoop cluster centralizes all the arbitration and repository-related issues without creating any ambiguity.
HDFS data platform format follows a strictly hierarchical file system. An application or a user first creates a directory, and there will be files within this directory. The file system hierarchy is identical to other file systems. It is possible to add or remove a file and also move files from a directory to another. A user can even rename files.
Data replication is an essential part of the HDFS format. Since it is hosted on the commodity hardware, it is expected that nodes can go down without any warning as such. So, the data is stored in a redundant manner in order to access it at any times. HDFS stores a file in a sequence of blocks. It is easy to configure the block size and the replication factor. Blocks of files are replicated in order to ensure that there is always fault tolerance. It is possible to specify the required number of replicas of a certain file. All files have write-once and read-multiple-times format. The Namenode is the deciding authority when it comes to issuing the number of replication blocks needed.
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Why should you use Hadoop Distributed File System?
- It is distributed across hundreds or even thousands of servers with each node storing a part of the file system. Since the storage is done on commodity hardware, there are more chances of the node failing and, with that, the data can be lost. HDFS gets over that problem by storing the same data in multiple sets.
- HDFS works quite well for data loads that come in a streaming format. So, it is more suited for batch processing applications rather than for interactive use. It is important to note that HDFS works for high throughput rather than low latency.
- HDFS works exclusively well for large datasets, and the standard size of datasets could be anywhere between gigabytes and terabytes. It provides high-aggregate data bandwidth, and it is possible to scale hundreds of nodes in a single cluster. Hence, millions of files are supported in a single instance.
- It is extremely important to stick to data coherency. The standard files that come routinely in the HDFS fold are the read-once and write-many-times files so that the data can remain the same and it can be accessed multiple times without any issues regarding data coherency.
- HDFS works on the assumption that moving of computation is much easier, faster, and cheaper than moving of data of humongous size, which can create network congestion and lead to longer overall turnaround times. HDFS provides the facility to let applications access data at the place where they are located.
- HDFS is highly profitable in the sense that it can easily work on commodity hardware that are of different types without any issue of compatibility. Hence, it is very well suited for taking advantage of cheaply and readily available commodity hardware components.
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Scope of HDFS
HDFS is not as much as a database as it is a data warehouse. It is not possible to deploy a query language in HDFS. The data in HDFS is available by mapping and reducing functions. The data adheres to a simple and robust coherency model. HDFS is one of the core components of Hadoop. Hence, mastering this technology can give you an upper hand when it comes to applying for jobs in the Hadoop-related domains.
Hadoop Distributed File System (HDFS) Requirement
HDFS is absolutely important for Big Data needs. It is not possible to host data in a centralized location since the amount of data is so large and there are cost and capacity constraints, among other things. But because of the distributed nature of HDFS, you can host data across multiple server locations to deploy it for processing. The ability to host data on commodity hardware makes it more appealing, because when the load increases all you have to do is to increase the number of servers or nodes. HDFS takes care of the faulty nodes by storing the same data in a redundant manner. Moreover, scaling up or scaling down is extremely easy with HDFS as all it needs is addition or subtraction of commodity hardware to meet the newly changed requirements.
Also, HDFS conveniently solves the issue of processing streaming data since it is a platform that is used for batch processing and not for interactive use. This lends itself to Big Data applications where data is coming thick and fast and it has to be continuously processed in order to make sense out of it in real time or near to real time. HDFS overtakes the earlier database management systems that could no longer take care of all that streaming data and derive insights in real time.
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HDFS Career Opportunities
Hadoop is ubiquitous in today’s world, and for Hadoop there is no other more reliable storage system than HDFS. Hence if you master this technology, you can get into a highly paid job and take your career to the next level.
- The annual salary of a Hadoop Developer in the United States is $102,000
- The annual salary of a Senior Hadoop Developer in the United States is $131,000
If you are quite aware of the intricacies of working with the Hadoop cluster, are able to understand the nuances of Datanode, Namenode, master–slave architecture, their inter-dependencies, and how they work in tandem to solve Big Data Hadoop problems, then you are ready to take on high-paying Hadoop jobs in top MNCs around the world.
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Advantages of Hadoop Distributed File System
There are many advantages of learning this technology as HDFS is by far the most resilient and fault-tolerant technology that is available as an open-source platform, which can be scaled up or scaled down depending on the needs, making it really hard for finding an HDFS replacement for Big Data Hadoop storage needs. So, you will have a head start when it comes to working on the Hadoop platform if you are able to decipher HDFS concepts. Some of the biggest enterprises on earth are deploying Hadoop in unprecedented scales, and things can only get better in the future. Companies like Amazon, Facebook, Google, Microsoft, Yahoo, General Electrics, and IBM run massive Hadoop clusters in order to parse their inordinate amounts of data. Therefore, as a forward-thinking IT professional, this technology can help you leave your competitors way behind and make a big leap in your career.
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