Cassandra vs Hazelcast: What are the differences?

Introduction: Cassandra and Hazelcast are both widely used distributed database management systems, but they have distinct differences in how they handle data distribution, scalability, and fault tolerance. Understanding these differences can help businesses choose the right solution for their specific needs.

1. Data Distribution Strategy: Cassandra employs a masterless architecture where data is distributed across multiple nodes using a peer-to-peer model. Each node in the cluster is equal and interacts directly with clients. Hazelcast, on the other hand, uses a master-slave architecture where a single node acts as the master and others as slaves. The master node manages data distribution and coordination with clients.

2. Consistency and Availability: Cassandra ensures high availability by allowing different consistency levels for reads and writes, allowing trade-offs between consistency and performance. Hazelcast, however, provides strong eventual consistency for distributed data, meaning that updates will eventually propagate to all nodes, but there may be temporary inconsistencies during the propagation process.

3. Partitioning Strategy: Cassandra uses consistent hashing to distribute data evenly across nodes in a cluster. It uses a ring-based design where each node gets assigned a range of hash values. Hazelcast, on the other hand, uses a partition-based approach where data is divided into partitions, and each partition is assigned to a specific node based on a partition strategy.

4. Querying Language: Cassandra uses its own query language called CQL (Cassandra Query Language), which is similar to SQL but has some differences. Hazelcast, on the other hand, provides an in-memory data grid and does not have a native query language. It allows users to interact with data using various programming language APIs.

5. Data Model: Cassandra is column-oriented and provides flexible schema options, allowing each row to have a different set of column names and types. Hazelcast, on the other hand, is a key-value store with a distributed map data structure, where data is organized as key-value pairs.

6. Integration with Other Systems: Cassandra has built-in support for integration with Apache Hadoop and Apache Spark, making it suitable for big data analytics workflows. Hazelcast, on the other hand, provides connectors and integrations for various systems and frameworks, including Apache Kafka, Apache Camel, Spring, and Hibernate.

In Summary, Cassandra and Hazelcast differ in their data distribution strategy, consistency and availability models, partitioning strategies, querying languages, data models, and integration capabilities. Understanding these differences can help businesses make informed decisions when selecting the right distributed database solution for their needs.