Hazelcast vs RocksDB: What are the differences?

Introduction

Hazelcast and RocksDB are two popular technologies used for data storage and processing. While they have some similarities, they also have key differences that distinguish them from each other. Let's explore these differences below.

1. Ease of Use: Hazelcast is an in-memory data grid that provides a distributed and scalable platform for caching and processing data. It offers a simple and easy-to-use API, making it suitable for developers who want a quick and straightforward solution for data storage. On the other hand, RocksDB is an embeddable persistent key-value store designed for fast storage devices. It is more complex to set up and configure compared to Hazelcast, requiring more expertise to optimize its performance.

2. Data Persistence: One of the significant differences between Hazelcast and RocksDB is their approach to data persistence. Hazelcast primarily focuses on an in-memory data grid, where data is stored in RAM and can be lost in case of a power failure or system shutdown. On the contrary, RocksDB is a disk-based storage engine that provides durability by persisting data to disk, ensuring data consistency and reliability even in the face of failures.

3. Performance: Hazelcast's primary goal is to enable blazing-fast data processing by utilizing in-memory storage. It leverages distributed computing techniques to distribute and process data across multiple nodes, providing high throughput and low latency for data-intensive applications. RocksDB, on the other hand, prioritizes durability and disk-optimized performance. It is designed to maximize write throughput and minimize write amplification, making it well-suited for use cases that require high write rates and terabyte-scale data storage.

4. Data Size: Hazelcast is optimized for storing and processing smaller data sets that fit entirely within the available memory. As it relies on RAM, the storage capacity is limited by the size of the RAM available in the cluster. In contrast, RocksDB is designed to handle larger data sizes that exceed the available memory. By storing data on disk, it can scale to handle petabytes of data efficiently.

5. Use Cases: Hazelcast is commonly used for caching, session replication, distributed computing, and real-time processing where low latency and high throughput are essential. Its in-memory nature makes it suitable for applications that require fast data access and processing. RocksDB, on the other hand, is often used as an embeddable storage engine within larger systems, providing durable key-value storage for applications such as databases, distributed file systems, and messaging systems.

6. Language Support: Another key difference between Hazelcast and RocksDB is the language support they offer. Hazelcast has extensive support for various programming languages, including Java, .NET, C++, Python, and Go, allowing developers to use their preferred language when interacting with the data grid. On the contrary, RocksDB primarily provides APIs for C++, Java, and Python, with fewer language options compared to Hazelcast.

In summary, Hazelcast focuses on providing an in-memory data grid with easy-to-use APIs, high performance, and low latency, making it suitable for real-time data processing. On the other hand, RocksDB is an embeddable persistent key-value store that emphasizes durability, disk-optimized performance, and the ability to handle large data sets beyond the available memory.