Capacity Planning in Apache Cassandra | Edureka Video Lecture | Cassandra: Learn and Master - Database Management

Ans. Capacity planning in Apache Cassandra refers to the process of determining the required hardware resources and configurations to meet the performance and storage demands of a Cassandra cluster. It involves estimating the amount of data to be stored, the read and write throughput requirements, and the number of nodes needed to handle the workload efficiently.

Ans. Apache Cassandra uses a distributed architecture to handle data replication for capacity planning. It replicates data across multiple nodes in a cluster using a replication factor. The replication factor determines the number of copies of each data item that will be stored on different nodes. By configuring the replication factor, administrators can ensure data durability, fault tolerance, and high availability.

Ans. Several factors should be considered while performing capacity planning in Apache Cassandra: 1. Data volume: Estimate the expected amount of data to be stored and plan for adequate storage capacity. 2. Workload: Analyze the read and write throughput requirements to determine the number of nodes needed to handle the workload effectively. 3. Replication strategy: Choose an appropriate replication strategy based on the desired level of data durability and availability. 4. Hardware resources: Consider the CPU, memory, and storage requirements of each node in the cluster to ensure sufficient resources are available. 5. Growth projections: Anticipate future data growth and plan for scalability by adding additional nodes to the cluster.

Ans. Performance optimization during capacity planning in Apache Cassandra can be achieved through the following strategies: 1. Data model design: Design the data model based on the query patterns and access patterns to ensure efficient data retrieval. 2. Partitioning: Distribute data evenly across multiple partitions to allow for parallel processing and avoid hotspots. 3. Compaction strategy: Configure the compaction strategy to balance read and write performance by minimizing the number of disk I/O operations. 4. Read and write consistency: Choose appropriate consistency levels for read and write operations based on the application requirements to balance performance and data consistency. 5. Hardware optimization: Use high-performance hardware components, such as SSDs for storage and powerful CPUs, to enhance overall system performance.

Ans. Yes, Apache Cassandra can dynamically scale its capacity by adding or removing nodes from the cluster. This feature allows organizations to easily accommodate changes in data volume and workload without causing downtime or affecting the performance of the system. By adding new nodes, the cluster can distribute the data and workload across a larger number of machines, increasing the overall capacity. Similarly, removing nodes can reduce the capacity as needed. Apache Cassandra's decentralized architecture enables seamless scaling without centralized bottlenecks.