What are the Limitations of Hadoop 1.0
The limitations of Hadoop 1.0 are following.
- It just executes Map/Reduce tasks.
- The single point of failure is JobTracker.
- Per Cluster, it supports up to 4000 Nodes.
- For real-time data processing, it is not ideal.
- It is only allowed to configure one NameNode.
- The horizontal scalability of NameNode is not aided by it.
- Only one Name No. and one Namespace is supported per Cluster.
- The secondary NameNode was supposed to copy the NameNode’s hourly metadata backup.
- Only batch processing of enormous amounts of data that are already in the Hadoop system is appropriate for it.
- It just includes one component, called JobTracker, which can handle a variety of tasks, including resource management.
What is shuffling in MapReduce?
Shuffling is used in Hadoop MapReduce to move data from the key mappers to the key reducers. It is the procedure by which the system organizes the unstructured data and feeds the map’s output to the reducer as an input. For reducers, it is a significant process. They wouldn’t accept any information else. Additionally, because this procedure can start even before the map phase is finished, it helps to shorten the process duration.
What are the three modes that Hadoop can Run?
Hadoop is set up by default to operate in a non-distributed mode. A single Java process is used to run it. This mode makes use of the local file system rather than HDFS. This option is more beneficial for debugging because no configuration of the core-site.xml, hdfs-site.xml, mapred-site.xml, masters, or slaves is necessary. Hadoop’s stand-alone mode is typically the fastest.
Each daemon functions as a distinct Java process in this mode. This option needs a customized setup. The input and output are handled by the HDFS. This deployment method is useful for testing and debugging.
Fully Distributed Mode
This mode gives fully distributed computing capacity, security, fault endurance, and scalability. It is Hadoop‘s production mode. In essence, one machine in the cluster serves only as the NameNode, and another as the Resource Manager. These are specialists. Data nodes and node managers are the rest nodes. Slaves are those people. Hadoop Daemons requires the definition of environment and configuration settings.
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