![\"Comparison](\"https:\/\/data-flair.training\/blogs\/wp-content\/uploads\/sites\/2\/2016\/12\/hadoop-2.x-vs-hadoop-3.x-comparison-1.jpg\")
<\/a>Comparison Between Hadoop 2.x vs Hadoop 3.x<\/p><\/div>\n
2. Feature-wise Comparison Between Hadoop 2.x vs Hadoop 3.x<\/h2>\n
This section will let you know the Top 22 differences between Hadoop 2.x vs Hadoop 3.x. Let us now discuss each feature one by one-<\/p>\n
i. License<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Apache 2.0, Open Source<\/li>\n
- Hadoop 3.x – <\/strong>Apache 2.0, Open Source<\/li>\n<\/ul>\n
ii. Minimum supported version of Java<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Minimum supported version of java is java 7.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Minimum supported version of java is java 8<\/li>\n<\/ul>\n
iii. Fault Tolerance<\/h3>\n\n- Hadoop 2.x –<\/strong>\u00a0Fault tolerance<\/strong><\/a>\u00a0can be handled by replication (which is wastage of space).<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Fault tolerance can be handled by Erasure coding<\/a><\/strong>.<\/li>\n<\/ul>\n
iv. Data Balancing<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For data, balancing uses HDFS<\/a><\/strong> balancer.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For data, balancing uses Intra-data node balancer, which is invoked via the HDFS disk balancer<\/strong><\/a> CLI.<\/li>\n<\/ul>\n
v. Storage Scheme<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses 3X replication scheme<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Support for erasure encoding in\u00a0HDFS.<\/li>\n<\/ul>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
ii. Minimum supported version of Java<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Minimum supported version of java is java 7.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Minimum supported version of java is java 8<\/li>\n<\/ul>\n
iii. Fault Tolerance<\/h3>\n\n- Hadoop 2.x –<\/strong>\u00a0Fault tolerance<\/strong><\/a>\u00a0can be handled by replication (which is wastage of space).<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Fault tolerance can be handled by Erasure coding<\/a><\/strong>.<\/li>\n<\/ul>\n
iv. Data Balancing<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For data, balancing uses HDFS<\/a><\/strong> balancer.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For data, balancing uses Intra-data node balancer, which is invoked via the HDFS disk balancer<\/strong><\/a> CLI.<\/li>\n<\/ul>\n
v. Storage Scheme<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses 3X replication scheme<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Support for erasure encoding in\u00a0HDFS.<\/li>\n<\/ul>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
iii. Fault Tolerance<\/h3>\n\n- Hadoop 2.x –<\/strong>\u00a0Fault tolerance<\/strong><\/a>\u00a0can be handled by replication (which is wastage of space).<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Fault tolerance can be handled by Erasure coding<\/a><\/strong>.<\/li>\n<\/ul>\n
iv. Data Balancing<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For data, balancing uses HDFS<\/a><\/strong> balancer.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For data, balancing uses Intra-data node balancer, which is invoked via the HDFS disk balancer<\/strong><\/a> CLI.<\/li>\n<\/ul>\n
v. Storage Scheme<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses 3X replication scheme<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Support for erasure encoding in\u00a0HDFS.<\/li>\n<\/ul>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
iv. Data Balancing<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For data, balancing uses HDFS<\/a><\/strong> balancer.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For data, balancing uses Intra-data node balancer, which is invoked via the HDFS disk balancer<\/strong><\/a> CLI.<\/li>\n<\/ul>\n
v. Storage Scheme<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses 3X replication scheme<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Support for erasure encoding in\u00a0HDFS.<\/li>\n<\/ul>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
v. Storage Scheme<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses 3X replication scheme<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Support for erasure encoding in\u00a0HDFS.<\/li>\n<\/ul>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
vi. Storage Overhead<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS has 200% overhead in storage space.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Storage overhead is only 50%.<\/li>\n<\/ul>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
vii. Storage Overhead Example<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>If there is 6 block so there will be 18 blocks occupied the space because of the replication scheme.<\/li>\n
- Hadoop 3.x –<\/strong>\u00a0If there is 6 block so there will be 9 blocks occupied the space 6 block and 3 for parity.<\/li>\n<\/ul>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
viii. YARN Timeline Service<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses an old timeline service which has scalability issues.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Improve the timeline service v2 and improves the scalability and reliability of timeline service.<\/li>\n<\/ul>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
ix. Default Ports Range<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a02.0 some default ports are\u00a0Linux<\/a><\/strong>\u00a0ephemeral port range. So at the time of startup, they will fail to bind.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>But in Hadoop 3.0 these ports have been moved out of the ephemeral range.<\/li>\n<\/ul>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
x. Tools<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Uses\u00a0Hive<\/a><\/strong>, pig, Tez, Hama, Giraph and other Hadoop tools.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hive,\u00a0pig<\/a><\/strong>, Tez, Hama, Giraph and other Hadoop tools are available.<\/li>\n<\/ul>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\nxi. Compatible File System<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>HDFS\u00a0(Default FS), FTP File system: This stores all its data on remotely accessible FTP servers. Amazon S3 (Simple Storage Service) file system Windows Azure Storage Blobs (WASB) file system.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It supports all the previous one as well as Microsoft Azure Data Lake filesystem.<\/li>\n<\/ul>\n
xii. Datanode Resources<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Datanode resource is not dedicated for the\u00a0MapReduce<\/a><\/strong>\u00a0we can use it for other application.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also data node resources can be used for other Applications too.<\/li>\n<\/ul>\n
xiii. MR API Compatibility<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>MR API compatible with Hadoop 1.x program to execute on Hadoop 2.X<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also MR API is compatible with running\u00a0Hadoop\u00a01.x programs to execute on Hadoop 3.X<\/li>\n<\/ul>\n
xiv. Support for Microsoft Windows<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>It can be deployed on windows.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also supports for Microsoft windows.<\/li>\n<\/ul>\n
xv. Slots\/Container<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop\u00a01 works on the concept of slots but Hadoop 2.X works on the concept of the container. Through in the container, we can run the generic task.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>It also works on the concept of a container.<\/li>\n<\/ul>\n
xvi. Single Point of Failure<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Has Features to overcome SPOF so whenever Namenode fails it recovers automatically.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Has Feature to overcome SPOF so whenever Namenode fails it recovers automatically no needs manual intervention to overcome it.<\/li>\n<\/ul>\n
xvii. HDFS Federation<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>In\u00a0Hadoop\u00a01.0, only single NameNode to manage all Namespace but in Hadoop 2.0, multiple NameNode for multiple Namespace.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 3.x also have multiple Namenode for multiple namespaces.<\/li>\n<\/ul>\n
Learn Hadoop HDFS Federation in detail.<\/a><\/p>\nxviii. Scalability<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>We can scale up to 10,000 Nodes per cluster.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Better scalability. we can scale more than 10,000 nodes per cluster.<\/li>\n<\/ul>\n
xix. Faster Access to Data<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Due to data Node caching we can fast access the data.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also through Datanode caching we can fast access the data.<\/li>\n<\/ul>\n
xx. HDFS Snapshot<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Hadoop 2 adds the support for a snapshot. It provides disaster recovery and protection for user error.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Hadoop 2 also support for the snapshot feature.<\/li>\n<\/ul>\n
xxi. Platform<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>Can serve as a platform for a wide variety of\u00a0data analytics<\/strong>\u00a0<\/a>possible to run event processing, streaming, and real-time operations.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>Here also it is possible to run event processing, streaming and real-time operation on the top of\u00a0YARN<\/strong>.<\/a><\/li>\n<\/ul>\n
xxii. Cluster Resource Management<\/h3>\n\n- Hadoop 2.x –\u00a0<\/strong>For cluster resource Management it uses\u00a0YARN. It improves scalability, high availability<\/strong><\/a>, Multi-tenancy.<\/li>\n
- Hadoop 3.x –\u00a0<\/strong>For a cluster, resource Management Uses\u00a0YARN,\u00a0with all the features.<\/li>\n<\/ul>\n
3. Conclusion<\/h2>\n
As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n
Learn Apache Hadoop Ecosystem Components in detail.<\/a><\/p>\n Learn Hadoop HDFS Federation in detail.<\/a><\/p>\n As we have discussed 22 important differences between Hadoop 2.x vs Hadoop 3.x, now we can decide which is better between Hadoop 2 and Hadoop 3 for installation. We are providing both Hadoop 2.x installation on Ubuntu<\/strong><\/a> and Hadoop 3.x installation on Ubuntu<\/a><\/strong> or convenience.xi. Compatible File System<\/h3>\n
\n
xii. Datanode Resources<\/h3>\n
\n
xiii. MR API Compatibility<\/h3>\n
\n
xiv. Support for Microsoft Windows<\/h3>\n
\n
xv. Slots\/Container<\/h3>\n
\n
xvi. Single Point of Failure<\/h3>\n
\n
xvii. HDFS Federation<\/h3>\n
\n
xviii. Scalability<\/h3>\n
\n
xix. Faster Access to Data<\/h3>\n
\n
xx. HDFS Snapshot<\/h3>\n
\n
xxi. Platform<\/h3>\n
\n
xxii. Cluster Resource Management<\/h3>\n
\n
3. Conclusion<\/h2>\n
\nIf you like this blog or found any other difference between Hadoop 2.x vs Hadoop 3.x, so please let us know by leaving a comment below.
\nSee Also-<\/strong><\/p>\n