HDFS: how Apache Hadoop’s file system works, advantages and disadvantages

HDFS is the distributed file system of Hadoop Apache. It is a central component of the Apache Framework, and more specifically its storage system. Find out how HDFS works, the advantages and disadvantages of HDFS.

HDFS (Hadoop Distributed File System) is a distributed file system for storing and retrieving files in record time. It is one of the basic components of the Apache Hadoop framework, and more precisely of its storage system. HDFS Hadoop is one of Apache’s top level projects.

HDFS definition

Due to its massive capacity and reliability, HDFS is a storage system very suitable for Big Data. In combination with YARN, this system increases the data management possibilities of the HDFS Hadoop cluster and thus enables efficient handling of big data. One of its main features is the ability to store terabytes or even petabytes of data.

The system is capable of managing thousands of nodes without operator intervention. It allows you to benefit simultaneously from the advantages of parallel and distributed computing. After a modification, it allows to easily restore the previous version of a data.

HDFS can be run on commodity hardware, which makes it very error tolerant.. Each data is stored in several locations, and can therefore be retrieved in all circumstances. Similarly, this replication helps to combat potential data corruption.

The servers are connected and communicate via TCP protocols.. Although it is designed for massive databases, normal file systems such as FAT and NTFS are also compatible. Finally, a CheckpointNode feature allows you to check the status of nodes in real time.

How does HDFS work?

Here is a tutorial for HDFS. The Hadoop Distributed File System is based on a “master/slave HDFS architecture”. Each cluster has an individual Namenode as the main server. This ensures that clients can access the right data at the right time. The Namenode also takes care of opening, closing, renaming files or even folders. Each node also has one or more Datanodes, which are assigned the task of managing the storage associated with the node. The blocks are mapped by the Namenode for the Datanodes.

Actually, Namenode and Datanode are Java programming codes that can be run on commodity hardware machines.. These machines usually run Linux OS or GNU. Note that the entire HDFS is based on the Java programming language. Thus, the Namenode of each Hadoop cluster centralizes all folder and file management to avoid any ambiguity.

The format follows a system of file prioritization. An application or user first creates a folder with files. The hierarchy of files is identical to other file systems. It is possible to add or delete a file and move the files within a folder or even rename them.

The data replication is an essential part of the HDFS format.. As the system is hosted on commodity hardware, it is normal that nodes can fail without warning. This is why the data is stored redundantly, as a sequence of blocks. The user can easily configure the block size and replication factor. The blocks of files are replicated to ensure error tolerance.

What are the advantages of HDFS?

HDFS has several obvious advantages. The file system is distributed across hundreds or even thousands of servers, and each node stores a portion of the file system. For avoid the risk of losing data, each data is stored in three locations. It is also very efficient for processing data streams.

For large datasets, of the order of several gigabytes or terabytes, this distributed file system also works very well. It is therefore very suitable for Big Data. From tens of millions of files can be supported on a single instance. In addition, it ensures the consistency and integrity of the data to avoid any inconvenience.

HDFS also avoids network congestion by shifting operations rather than data movement. This allows applications to access data at the location where it is stored. A final strength is its portability. It can run on different types of commodity hardwares without any compatibility issues.

More than a database, this distributed file system presents itself as a Data Warehouse. It is impossible to deploy a query language on HDFS, and the data is accessible through mapping and reduction functions (Hadoop MapReduce). The data adheres to a simple and robust consistency model.

Why do we need HDFS?

HDFS is essential for Big Data. The data are now too numerous to be stored centrally, in particular because of cost and storage capacity constraints. However, due to the distributed nature of the latter, it is possible to distribute the data to different servers in order to save money.

The possibility to host this file system on a Commodity Hardware is very convenient. If additional storage space is required, simply increase the number of servers or nodes. HDFS takes care of the problem nodes by storing the same data redundantly in three different locations. In addition, this system is very efficient for processing data flows, making it ideal for Big Data, where data must be processed in real time to make sense.

HDFS addresses the problems of previous data management systems that could not support data flows and analyze them in real time.. Scaling is very easy, so this system can be adapted to all needs without any difficulty.

As a central component of Apache Hadoop, it is a tool whose mastery can prove to be very profitable, as this skill is highly sought after on the job market.. In the United States, a Big Data Hadoop developer can earn more than $100,000 a year.

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