What is a Resilient Distributed Dataset (RDD) in Apache Spark?

Vaibhav Kothia May 26, 2023 0 Comments

A Resilient Distributed Dataset (RDD) is a fundamental data structure of Apache Spark. It is an immutable distributed collection of objects. Each dataset in RDD is divided into logical partitions, which may be computed on different nodes of the cluster. RDDs can contain any type of Python, Java, or Scala objects, including user-defined classes.

For example, consider a list of numbers [1, 2, 3, 4, 5, 6, 7, 8], which can be divided into two RDDs:

RDD1 = [1, 2, 3, 4]
RDD2 = [5, 6, 7, 8]

Each RDD can then be further divided into logical partitions, such as:

RDD1 Partition 1 = [1, 2]
RDD1 Partition 2 = [3, 4]
RDD2 Partition 1 = [5, 6]
RDD2 Partition 2 = [7, 8]

These partitions can then be computed on different nodes of the cluster in parallel.

How does Apache Kafka handle data replication?

Vaibhav Kothia May 26, 2023 0 Comments

Apache Kafka handles data replication by replicating messages from a leader to one or more followers. The leader is responsible for managing the message replication process, while the followers passively replicate the leader.

For example, let’s say there is a Kafka cluster with three nodes, A, B, and C. Node A is the leader and nodes B and C are the followers. When a message is published to the cluster, it is first written to the leader (node A). The leader then replicates the message to the followers (nodes B and C). If the leader fails, one of the followers (node B or C) will be elected as the new leader and will continue to replicate messages to the other followers.

How does Redis handle data replication?

Vaibhav Kothia May 26, 2023 0 Comments

Redis data replication is a process of synchronizing data across multiple Redis servers. It is used to increase data availability and fault tolerance.

Redis data replication works by having a master server that is responsible for writing data and multiple slaves that continuously replicate the data from the master. When the master receives a write command, it sends the data to the slaves, which then store the data in their own memory. This ensures that if the master fails, the slaves can take over and provide the same data.

For example, let’s say you have a Redis cluster with a master and three slaves. The master receives a write command to store a key-value pair in the database. The master will then send this data to the slaves, which will then store the data in their own memory. This ensures that if the master fails, the slaves can take over and provide the same data.