Redis vs. Hazelcast: Comparison of In-Memory and Distributed Data Stores

In the realm of distributed data stores and caching solutions, Redis vs. Hazelcast are two prominent contenders, each with its unique strengths and features. In this blog post, we will conduct a detailed comparison of Redis and Hazelcast, enabling you to make an informed choice for your specific data storage and caching needs.



Redis is a blazingly fast, open-source, in-memory data store that serves various purposes, such as caching, real-time analytics, and message queuing. It is revered for its simplicity and speed. Redis stores data in memory, providing lightning-fast read operations.

Use Cases

  • Caching: Redis is often used to cache frequently accessed data, alleviating the load on the primary data store.
  • Session Storage: Its sub-millisecond response times make it a perfect choice for storing user session data.
  • Real-time Analytics: Redis is capable of handling real-time data analytics, thanks to its low-latency data access.
  • Pub-Sub Messaging: Redis supports Publish-Subscribe (Pub-Sub) messaging, making it ideal for real-time applications.


  • Exceptional read performance.
  • Simple data structures, including strings, lists, sets, and hashes.
  • Built-in replication and clustering support.
  • Active community with a plethora of libraries and extensions.


  • Limited support for complex search and query operations.
  • Data is stored in memory, which can be volatile.
  • Scaling can be challenging for very large datasets.




Hazelcast is an open-source, in-memory data grid platform that is highly scalable and designed for distributed computing. It offers a broad range of features for caching, data distribution, and real-time processing. Hazelcast can be used to build highly available and scalable systems.

Use Cases

  • Distributed Caching: Hazelcast is designed for distributed caching, improving data access speeds.
  • Data Distribution: It supports data distribution across a cluster, ensuring data availability and resilience.
  • Distributed Computation: Hazelcast can distribute and parallelize computation tasks.
  • Real-time Event Processing: It enables real-time event processing and stream processing.


  • Excellent support for distributed systems.
  • Horizontal scalability and high availability.
  • Distributed data structures and computation capabilities.
  • Reliable and resilient architecture.


  • More complex setup compared to Redis.
  • Limited query capabilities compared to dedicated databases.
  • Less suitable for some use cases, like full-text search.


Comparison Table

Aspect Redis Hazelcast
Data Storage In-memory In-memory
Data Structures Key-Value, Lists, Sets, Hashes, etc. Various distributed data structures
Querying Limited support Limited support
Scalability Limited for very large datasets Highly scalable for distributed systems
Use Cases Caching, Real-time Analytics, Pub-Sub Distributed Caching, Data Distribution, Distributed Computation
Resource Requirements Moderate Moderate to High
Complexity Low Moderate


1. When should I use Redis?

Redis is an excellent choice when you need ultra-fast data retrieval, especially for read-intensive operations. It is perfect for caching, real-time analytics, and applications where low-latency access is crucial.

2. When should I use Hazelcast?

Hazelcast is best suited for scenarios where you require a distributed and scalable data grid for caching, data distribution, and distributed computation. It’s ideal for building highly available, resilient systems.

3. Can Redis and Hazelcast be used together?

Yes, Redis and Hazelcast can complement each other in a distributed architecture. Redis can handle certain caching tasks, while Hazelcast can manage distributed computation and data distribution.

4. Can Hazelcast perform complex queries?

Hazelcast provides limited support for complex queries. For more advanced query capabilities, it may be necessary to integrate dedicated databases into your architecture.

5. What are the hardware requirements for Hazelcast?

Hazelcast’s hardware requirements depend on the scale and complexity of your distributed system. It typically requires moderate to high resources to handle distributed data and computation tasks effectively.


In summary, Redis and Hazelcast are both robust choices for in-memory data storage and caching, but they cater to different use cases. Redis excels at lightning-fast data retrieval and is ideal for caching and real-time analytics. Hazelcast, on the other hand, is a distributed data grid platform suitable for building highly available and scalable systems, making it a great fit for distributed computing and data distribution. Your choice should align with the specific requirements and complexity of your project.

Explore the documentation and resources provided by both Redis and Hazelcast to gain a deeper understanding of their features and capabilities.

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