Hadoop 2.x and Hadoop 3.x are both versions of the Apache Hadoop open-source software framework for distributed storage and processing of large data sets. Both versions are designed to provide a scalable and fault-tolerant platform for big data processing, but there are some key differences between the two versions.
Here are some of the main differences between Hadoop 2.x and Hadoop 3.x:
- Resource Management: In Hadoop 2.x, the resource management is done using the YARN (Yet Another Resource Negotiator) framework. In Hadoop 3.x, the resource management is done using the YARN NextGen (YARN NG) framework, which is more flexible and efficient than the original YARN framework.
- Data Processing: Hadoop 2.x uses the MapReduce programming model for data processing, which is based on the idea of dividing data processing into two separate stages: map and reduce. Hadoop 3.x, however, supports more than just the MapReduce programming model, it also supports other data processing models like Apache Spark and Apache Tez.
- Storage: Hadoop 2.x uses the HDFS (Hadoop Distributed File System) for storage, which is a distributed file system that is designed to run on commodity hardware. Hadoop 3.x also uses HDFS, but it has been enhanced to support additional features like erasure coding and a more efficient storage format called HDFS-RAID.
- Cluster Scalability: Hadoop 2.x is limited to a maximum of 4000 nodes per cluster, while Hadoop 3.x can scale up to 10,000 nodes or more.
- Hardware Support: Hadoop 2.x is not optimized for cloud-native environments and modern hardware like NVMe drives and GPUs, while Hadoop 3.x is designed to support these technologies.
- Security: Hadoop 3.x has enhanced security features like Data at Rest Encryption and Role-Based Access Control (RBAC) which are not available in Hadoop 2.x
- Resource Isolation: Hadoop 3.x has better support for resource isolation and management of resources like CPU and Memory.
Overall, Hadoop 3.x is an evolution of Hadoop 2.x, it builds on the features of Hadoop 2.x and provides new features and improvements that allow for more efficient and scalable big data processing in the cloud and on-premise environments