Hadoop
Last updated
Last updated
Hadoop is a highly scalable, open-source, distributed computing platform that allows you to store and process large amounts of data. It is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
Hadoop is designed to be scalable by distributing the processing of data across a large number of computers. It also allows you to store and analyze data in a way that is faster than traditional methods. Hadoop is used to store and analyze data from a variety of sources, including databases, web servers, and file systems.
The master node keeps track of the status of all the data nodes. If a data node goes down, the master node takes over the processing of that block. The slave nodes process the data on their own. HDFS requires a high-speed Internet connection. It is usually best to have at least a 10 Mbps network connection.
HDFS works on a time-based algorithm, which means that every block is processed in a predetermined time interval. This provides a high degree of scalability as all the nodes process the data in real time. HDFS is a great solution for data warehouse and business intelligence tasks. It is also a good solution for high-volume, high-frequency data analysis.
When a client connection is received from a master server, the NameNode running on the master server looks into both the local namespace and the master server’s namespace to find the matching records. The NameNode running on the master server then executes the lookup and returns a list of records that match the query.
DataNodes then get the records and start storing them. A data block is a record of data. Data nodes use the data blocks to store different types of data, such as text, images, videos, etc. NameNode maintains data node connections with clients based on the replication status of DataNodes. If a DataNode goes down, the client can still communicate with the NameNode. The client then gets the latest list of data blocks from the NameNode and communicates with the DataNode that has the newest data block.
Thus, DataNode is a compute-intensive task. It is therefore recommended to use the smallest possible DataNode. As DataNode stores data, it is recommended to choose a node that is close to the centre of the data. In a distributed system, all the nodes have to run the same version of Java, so it is recommended to use open-source Java. If there are multiple DataNodes, then they are expected to work in tandem to achieve a certain level of performance.
