Datanodes

Definition

Datanodes are the components in a distributed file system that store the actual data blocks within a Hadoop cluster. They work in conjunction with a master server, called the NameNode, which manages the namespace and regulates access to these data blocks. Datanodes ensure data redundancy and fault tolerance by replicating blocks across different nodes, making data accessible even in case of node failures.

5 Must Know Facts For Your Next Test

1. Datanodes are responsible for serving read and write requests from clients, responding with the required data blocks or storing incoming data.
2. Each datanode periodically sends a heartbeat signal to the NameNode to inform it that it is functioning properly; if the heartbeat stops, the NameNode considers the datanode as dead.
3. Data is typically split into blocks (usually 128MB or 256MB) which are then distributed across multiple datanodes to optimize storage and processing.
4. Datanodes handle the replication of data blocks automatically according to the replication policy set in HDFS, ensuring that data is safely stored across different locations.
5. In case of hardware failure or node outages, datanodes help maintain data availability by allowing other nodes in the cluster to serve requests for the missing data.

Review Questions

• How do datanodes interact with the NameNode in a Hadoop cluster?
  • Datanodes interact with the NameNode by reporting their status through periodic heartbeat signals and by storing data blocks as directed by the NameNode. The NameNode maintains the metadata about which datanodes hold specific data blocks and coordinates data retrieval and storage requests from clients. This relationship ensures that while datanodes handle actual data storage and retrieval, the NameNode oversees organization and access management, creating an efficient distributed file system.
• Discuss how replication works among datanodes and why it is essential for data integrity in a Hadoop environment.
  • Replication among datanodes involves duplicating data blocks across multiple nodes based on a predefined replication factor. This process is essential for maintaining data integrity because it ensures that even if one or more datanodes fail, copies of the data remain accessible from other functioning nodes. If a block becomes unavailable due to node failure, HDFS automatically creates new replicas on healthy datanodes, thus preserving the availability and reliability of the dataset within the Hadoop ecosystem.
• Evaluate the role of datanodes in achieving fault tolerance within Hadoop's architecture, considering potential challenges they may face.
  • Datanodes play a critical role in achieving fault tolerance within Hadoop's architecture by implementing block replication and monitoring their health through heartbeat signals. However, challenges such as network partitioning, hardware failures, or overload situations can affect their performance and availability. To address these issues, Hadoop employs strategies like automatic re-replication of lost blocks and load balancing among datanodes to maintain seamless access to data. This ability to adapt under duress contributes significantly to Hadoop's robustness as a distributed computing platform.

Related terms

NameNode: The master server in a Hadoop cluster that manages the metadata and namespace of the distributed file system, directing clients to the appropriate datanodes.

HDFS: Hadoop Distributed File System, a scalable file system that is designed to store vast amounts of data across multiple machines while providing high throughput access to application data.

Replication: The process of duplicating data blocks across multiple datanodes to ensure reliability and fault tolerance in a Hadoop cluster.