Redis vs Hazelcast: Choosing the Right In-Memory Data Solution

Nowadays, users won’t wait even a few seconds for a web page to load, so speed has become absolutely essential. This is where in-memory databases step in, taking the heavy load off traditional databases. But when it comes to choosing the right tool, a big question always arises: Redis or Hazelcast? In this post, we put these two data powerhouses head to head, comparing their performance, scalability, reliability, and ease of use, so you can finally see which one comes out on top.

Introduction to Redis and Hazelcast

Redis and Hazelcast are both in-memory storage systems designed for fast data access in memory. However, they have different architectures and use cases. Redis is an open-source in-memory database known for its very high performance and its use of diverse data structures. In other words, Redis is designed to be simple and fast, and by using structures such as strings, lists, and sorted sets, it can respond to data requests in milliseconds or even less.

On the other hand, Hazelcast is a distributed in-memory data grid platform that, in addition to caching, provides distributed computing capabilities and advanced clustering features. Simply put, Redis is more suitable for applications that require exceptional speed and rich data structures, while Hazelcast is chosen for complex cluster-based systems with a focus on horizontal scalability, data partitioning, and execution of distributed tasks.

Comparison Table of Redis and Hazelcast

Before diving into a deep comparison of these two technologies, let us place them side by side in a table so you can get an overall view of their differences and similarities.

In summary, Redis is essentially designed as a key-value data structure that can function as a database, a cache, or a message broker. In contrast, Hazelcast was designed from the beginning as a distributed data grid to share data across multiple nodes in a cluster and to perform heavy processing tasks in parallel. In the following sections, we compare Redis and Hazelcast across several important criteria.

Threading Architecture: Single-Threaded vs. Multi-Threaded

One of the most important technical differences that directly affects system performance is the processing or threading model. Redis uses a single-threaded model for its core. This may raise the question of why, in the era of multi-core processors, a tool would be single-threaded. The reason lies in simplicity and the elimination of issues such as race conditions and locking, which allows Redis to respond to requests with very low latency. To utilize all CPU cores with Redis, multiple instances must be run on the same machine.

In contrast, Hazelcast uses a multi-threaded architecture, meaning a single instance can directly take advantage of the full processing power of the server’s cores. It uses a model called SEDA (Staged Event-Driven Architecture), in which input/output (I/O) threads are separated from data processing threads.

Performance Comparison Under Different Workloads

Architectural differences become more evident at large scales. According to published benchmarks, when the workload increases and a higher number of concurrent threads is required, Hazelcast can maintain near linear performance, while Redis may experience performance degradation when managing a very large number of concurrent threads, typically beyond 32 threads.

Imagine running an online auction where millions of users suddenly access the system at the same time. In such a scenario, Hazelcast, due to its multi-threaded architecture, allows requests to be processed in parallel across different data partitions, without one request having to wait for the completion of another.

Memory Management

In terms of memory management, Redis is highly efficient thanks to being written in C and using the jemalloc memory allocator. Data in Redis is stored compactly and can consume up to five times less memory than similar structures in Java. This allows you to store much more data in Redis with the same amount of RAM.

In contrast, memory management in Hazelcast is more complex. The open source version stores data on heap memory managed by the Java Garbage Collector. As data volume increases, garbage collection can introduce short but disruptive stop the world pauses that increase system latency. To address this, the Enterprise edition of Hazelcast provides a feature called High Density Memory Store, which moves data outside the control of the garbage collector.

Clustering and High Availability

One area where Hazelcast truly excels is the simplicity of cluster formation. Hazelcast uses UDP and multicast discovery to automatically detect new nodes. Simply starting a new node on the network allows it to join the cluster automatically, after which data is redistributed across the nodes.

In Redis, the clustering process is more manual. Redis does not provide native automatic node discovery, so administrative scripts or external tools are required to configure and manage a cluster.

It is also worth noting that Redis relies on a separate tool called Redis Sentinel to monitor node health and perform failover operations, which itself requires additional management.

Data Structures and Use Case Diversity

Redis is often described as the “Swiss Army knife” of the data world. It offers access to a wide range of data structures:

• Strings: For simple key–value storage

• Hashes: Ideal for storing objects and maps

• Lists: Commonly used to implement queues

• Sets and Sorted Sets: For managing unique collections and leaderboards

• Bitmaps and Geospatial Indexes: For spatial analytics and large-scale data processing

• JSON: Native support for JSON documents, allowing field-level queries without complex serialization

Hazelcast, on the other hand, focuses more on distributed data structures that are very familiar to Java developers, such as Distributed Map (IMap), Queue, and Set. One of Hazelcast’s key strengths is its deep object understanding: it can interpret complex Java object graphs and allows you to query internal object fields using SQL or the Predicate API, without needing to transfer the entire object over the network.

Data Security and Replication Challenges

Protecting data and preventing its loss is crucial. In Redis, data is replicated from the master node to slave nodes. However, there is a critical technical point: under very high network workloads, Redis may temporarily pause replication to slave nodes to maintain the performance of the master node. This can result in permanent data loss if the master fails before the data has been fully copied.

Hazelcast takes a stricter approach. In a Hazelcast cluster, all nodes are equal, and each node stores both its own primary data and backup copies of other nodes' data. Hazelcast strives to maintain data synchronization under all conditions, and tests have shown that even under near network saturation, it does not stop data replication, unlike Redis.

Stream Processing: Beyond a Simple Cache

If your application needs to analyze data as soon as it arrives, such as detecting fraud in banking transactions, Hazelcast takes the lead. Hazelcast provides a platform called Hazelcast Jet, now integrated into the main platform, which is a very fast stream processing engine. This engine can process, filter, and enrich billions of events per second with millisecond latency.

Redis also offers a feature called Redis Streams, which is powerful and used for implementing message queues and event logging. However, Redis Streams primarily acts as a storage layer for streams, and the processing logic usually needs to be implemented on the application side, whereas Hazelcast provides the entire processing stack within the cluster itself.

For a deeper look at Redis configuration fundamentals, read What is Redis Port to learn how Redis handles network communication and how to configure it properly.

Licensing Changes in 2024: What You Need to Know

In early 2024, Redis shifted from the BSD license to a dual-license model (RSALv2 and SSPLv1). The main goal of this change was to prevent commercial cloud providers, such as AWS and Google, from exploiting Redis without contributing to its development.

Hazelcast continues to use an open-source model under Apache 2.0 for its Community edition and a commercial model for its Enterprise edition.

As the table shows, Hazelcast appears more straightforward and appealing in this area. 

Concerned about licensing and long-term flexibility? Explore Redis Alternatives to discover open-source and cloud-friendly options that may better align with your business needs.

The Ultimate Guide to Smart Selection

Now that you’re familiar with all the details, it’s time to finalize your decision. Your choice shouldn’t be about “which is better,” but rather “which fits my needs best.”

Golden Scenarios for Redis

When should you choose Redis?

1. Session management and fast caching: With sub-millisecond latency, Redis is the top choice for storing user sessions and caching database query results in high-traffic web applications.

2. Leaderboards: Redis’s Sorted Set data structure is specifically designed for large-scale rankings, a feature not as straightforward in Hazelcast.

3. Vector databases and AI: In 2024–2025, Redis has gained prominence as a vector database for applications like semantic search and RAG (Retrieval-Augmented Generation) in AI.

4. Message queues and Pub/Sub: With support for Pub/Sub and lists, Redis is excellent for lightweight, real-time communication between microservices.

Summary: Redis excels in scenarios where raw speed and a variety of data structures are the priority.

Golden Scenarios for Hazelcast

When should you choose Hazelcast?

1. Distributed computing: Unlike Redis, which sends data to the client for processing, Hazelcast allows computational code (e.g., Runnable or Callable in Java) to run directly on the nodes where the data resides. This colocated computing significantly reduces network transfer costs.

2. Stream processing: Using the Jet engine, Hazelcast can perform complex operations such as aggregation and filtering on data streams at rates of billions of events per second.

3. Advanced caching patterns: Hazelcast natively supports Write-Through and Read-Through caching, allowing the system to handle database reads/writes automatically, which keeps application code cleaner and simpler.

4. Seamless Java ecosystem integration: For organizations with a Java/Spring-based infrastructure, Hazelcast is highly favored due to full compatibility with Java interfaces and its ability to run in embedded mode within applications.

Summary: Hazelcast is unrivaled in complex distributed environments and heavy computational processing.

Still comparing data stores? Read our Redis vs MongoDB guide to understand when an in-memory engine or a document database is the better fit for your architecture.

Implementation Strategies in Production Environments

When integrating these tools into your codebase, it is important to pay close attention to data access patterns.

Cache Aside Pattern (Lazy Loading)

This is the most common way Redis is used. The application first checks the cache, and if the data is not found (a cache miss), it reads the data from the database and then writes it to the cache. This approach is simple, but managing consistency between the database and the cache is entirely your responsibility.

Read Through and Write Through Patterns

Hazelcast has a clear advantage in this area. You can configure Hazelcast to connect directly to the database. When you request data from Hazelcast that is not already present, Hazelcast itself fetches it from the database and returns it to you. This results in much cleaner application code and moves synchronization logic out of the application layer.

Embedded Pattern

One unique capability of Hazelcast is its ability to run in embedded mode. In this setup, Hazelcast runs inside the same JVM as your application. The key benefit is the elimination of network latency, since the data resides in the same memory space as the application itself. This is especially powerful for microservices that require ultra-fast access to shared data.

Want to choose the right caching solution for your use case? Check out our Redis vs Memcached comparison to see which one fits your performance and scalability needs best.

Conclusion

In the end, both Redis and Hazelcast are outstanding tools that have redefined data processing standards. Redis, with its focus on simplicity and single-node performance, and Hazelcast, with its emphasis on power and distributed capabilities at the cluster level, each reigns supreme in its domain. We hope this report has helped you gain a clearer perspective and deeper technical understanding to design the future architecture of your system. Remember, the best tool is the one that delivers the greatest value to your end users with the least complexity.