Apache Ignite vs Hazelcast: What are the differences?

Introduction:

Both Apache Ignite and Hazelcast are in-memory data grid platforms that provide distributed computing capabilities and high-performance caching. However, there are key differences between the two that make them suitable for different use cases.

1. Architecture: Apache Ignite follows a shared-nothing architecture, where each node owns and manages its own portion of the data. It allows for horizontal scalability and fault tolerance by distributing data across multiple nodes. On the other hand, Hazelcast follows a symmetric cluster architecture, where all nodes are equal in terms of functionality and data ownership. This architecture enables linear scalability and makes it easier to add or remove nodes from the cluster.

2. Clustering: Apache Ignite supports native partitioning, in which data is partitioned across the cluster nodes based on a configurable key. This enables data locality and efficient data access. Hazelcast, on the other hand, supports both partitioned and replicated clusters. In a partitioned cluster, the data is partitioned across the nodes, while in a replicated cluster, the data is fully replicated on all the nodes. This flexibility allows developers to choose the most suitable clustering strategy for their use case.

3. Data Consistency: Apache Ignite provides strong data consistency guarantees with the ACID (Atomicity, Consistency, Isolation, Durability) transaction model. It ensures that all updates are atomic and consistent across all nodes in the cluster. Hazelcast, on the other hand, uses an eventually consistent model, where updates are propagated asynchronously across the cluster, leading to eventual consistency. This approach provides higher availability and scalability but sacrifices strong consistency guarantees.

4. Querying: Apache Ignite supports a distributed SQL query engine that allows developers to run SQL queries on the in-memory data with full ACID guarantees. It supports a wide range of SQL features and provides integration with other analytics tools. Hazelcast, on the other hand, provides a distributed query API that allows executing predicates on the distributed data. While it does not provide full SQL support, it offers flexible and efficient querying capabilities.

5. Streaming and Event Processing: Apache Ignite provides a comprehensive streaming and event processing framework that allows developers to process continuous data streams in real-time. It supports event-based processing, complex event processing (CEP), and can be integrated with external message brokers. Hazelcast, on the other hand, provides a basic event listener mechanism that allows subscribing to events on the distributed data structures. It does not provide a dedicated streaming or CEP framework.

6. Integration and Ecosystem: Apache Ignite integrates well with other Apache projects like Apache Hadoop, Spark, and Flink. It provides connectors and plugins for seamless integration with these projects, enabling use cases like batch processing, machine learning, and real-time analytics. Hazelcast also provides integration with various technologies and frameworks, but its ecosystem is not as extensive as Apache Ignite's.

In summary, Apache Ignite and Hazelcast have different architectures, clustering models, consistency guarantees, querying capabilities, event processing frameworks, and ecosystem integrations. These differences make each platform suitable for specific use cases and requirements.