Partitioning and data distribution are fundamental concepts in Hazelcast that enable efficient and scalable storage and processing of data across a distributed cluster of nodes.
These mechanisms allow Hazelcast to divide data into partitions and distribute those partitions across multiple nodes, enabling parallel processing, fault tolerance, and high availability.
Here's an explanation of their use in Hazelcast :Partitioning :
* Partitioning involves dividing the dataset into smaller subsets called partitions or shards based on a partitioning strategy. Each partition represents a distinct subset of the data.
* Hazelcast employs a hash-based partitioning strategy by default, where each data record is assigned to a partition based on its key's hash value. This ensures an even distribution of data across partitions.
* Partitioning enables parallel processing of data by allowing different partitions to be processed concurrently by different nodes in the cluster. Each node is responsible for storing and processing a subset of the partitions, distributing the processing load across multiple nodes.
Data Distribution :
* Data distribution involves distributing partitions across multiple nodes in the cluster to achieve fault tolerance, load balancing, and scalability.
* Hazelcast employs a distributed data storage model, where each partition is replicated across multiple nodes in the cluster. This replication ensures data redundancy and fault tolerance, allowing data to remain accessible even in the event of node failures.
* Data distribution also facilitates load balancing by evenly distributing partitions across nodes, ensuring that the processing load is evenly distributed and no single node becomes a bottleneck.
* Hazelcast dynamically rebalances data distribution across nodes as the cluster topology changes, such as nodes joining or leaving the cluster, to maintain balanced data distribution and optimal resource utilization.
