Update on Hadoop Version 3.0: Key Enhancements
Upgrades in Hadoop 3.x Enhance Fault Tolerance, Scalability, and Storage Efficiency
Hadoop, the open-source big data processing framework, has undergone significant changes with the release of version 3.x. These updates primarily focus on improving fault tolerance, storage efficiency, scalability, and software requirements.
One of the most notable advancements is the introduction of erasure coding for data storage. This feature provides fault tolerance by reconstructing lost data, similar to RAID technology. Compared to Hadoop 2.x’s replication method, erasure coding reduces storage overhead nearly by half, making storage more efficient and cost-effective [1].
Another key improvement is the support for multiple NameNodes. Hadoop 3.x can now handle more than two NameNodes, with multiple standby NameNodes (high availability), enhancing cluster fault tolerance and availability. In contrast, Hadoop 2.x typically supported only one active and one standby NameNode [1].
Hadoop 3.x also requires at least JDK 8 to run, which is a departure from Hadoop 2.x's support for older Java versions [1].
The new version includes numerous internal improvements, bug fixes, and optimizations. These can be seen in successive 3.x minor releases, such as 3.3.3 to 3.3.6, offering better configurability, support for newer APIs (e.g., HDFS write-ahead logs), and integration with modern Java SDKs [4].
In terms of scalability and resource management, Hadoop 3.x offers enhanced flexibility and stability to better handle larger, more complex clusters. Both versions use YARN for cluster resource management and MapReduce for processing, but Hadoop 3.x provides a more robust solution for larger-scale operations.
Moreover, Hadoop 3.x has introduced the Timeline Service v.2 for YARN, improving reliability and scalability. This service stores generic information about completed applications, including user information, queue name, container information, and count of attempts per application [1].
The new version also supports Azure Data Lake and Aliyun Object Storage System as additional Hadoop-compatible filesystem options. To handle significant skewness caused by adding or removing disks within a DataNode, Hadoop 3.x includes the intra-DataNode balancing feature.
The shell scripts in Hadoop 3.x have been rewritten to fix bugs and provide the functionality of rewriting. Additionally, the new Hadoop-client-API and Hadoop-client-runtime are available in Hadoop 3.x, providing Hadoop dependencies in a single jar file for easier development and testing.
Lastly, the count of Map and Reduce Task, counters, and information about completed applications can be accessed in Hadoop 3.x with the help of the Timeline client. These details are stored in Timeline Service v.2 using HBase for storage [1].
In summary, Hadoop 3.x offers enhanced fault tolerance through erasure coding, better high-availability with multiple NameNodes, updated Java requirements, and overall improved efficiency and scalability over Hadoop 2.x [1][4].
[1] Apache Hadoop 3.x.0 Release Notes. (2019). Apache. Retrieved from https://hadoop.apache.org/releases.html#3.x.0 [4] Apache Hadoop 3.3.6 Release Notes. (2021). Apache. Retrieved from https://hadoop.apache.org/releases.html#3.3.6
In the realm of data-and-cloud-computing, the adoption of modern technology such as Hadoop trie structures could potentially optimize data storage and retrieval processes in Hadoop 3.x, further improving its efficiency.
Additionally, within the backdrop of scalable data processing, a strategy employing priority queue algorithms, like heaps, can be utilized for resource management in Hadoop 3.x, ensuring efficient task execution across clusters of varying sizes.
