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Hazelcast

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59
Redis

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3.9K
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Hazelcast vs Redis: What are the differences?

Introduction

Markdown code comparing the key differences between Hazelcast and Redis.

  1. In-Memory Data Grid vs. NoSQL Database: Hazelcast is primarily an in-memory data grid (IMDG) that stores and processes data in memory, providing fast access and low latency. On the other hand, Redis is a NoSQL database that persists data on disk and also supports in-memory caching. This fundamental difference influences their use cases, with Hazelcast being better suited for scenarios requiring real-time data processing and Redis for data persistence and caching.

  2. Data Models: Hazelcast supports a wide range of data structures such as distributed maps, queues, sets, and lists, allowing for complex data models and distributed computations. Redis, on the other hand, focuses on simpler data structures like strings, hashes, lists, sets, and sorted sets, emphasizing efficiency and simplicity.

  3. Consistency and Durability: Hazelcast provides strong data consistency guarantees through its distributed data structures. It ensures that data is always synchronized across the cluster, even in the presence of failures. Redis, on the other hand, offers eventual consistency by default, where data updates may take some time to propagate across nodes. Additionally, Redis allows users to choose different persistence mechanisms like snapshots or append-only logs for data durability.

  4. Programming Language Support: Hazelcast provides official clients for multiple programming languages, including Java, .NET, C++, Python, and Node.js, enabling developers to integrate it seamlessly into their application stack. Redis also offers official clients for a wide range of programming languages, making it accessible for different development environments.

  5. Distributed Computing Capabilities: Hazelcast includes a distributed computing framework, enabling distributed processing of data across the cluster using parallel algorithms. It supports distributed tasks, computing grid, event-driven programming, and distributed execution of user-defined functions. Redis, on the other hand, does not provide built-in distributed computing capabilities, focusing more on data operations and caching.

  6. Scalability and Fault-Tolerance: Both Hazelcast and Redis support horizontal scalability, allowing users to add or remove nodes dynamically to accommodate growing or shrinking workloads. However, Hazelcast provides automatic data replication, ensuring fault-tolerance even in the event of node failures. Redis requires users to configure replication manually to achieve fault-tolerance.

In summary, Hazelcast is an in-memory data grid with strong consistency and distributed computing capabilities, while Redis is a NoSQL database with a focus on simplicity, efficiency, and caching.

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Pros of Hazelcast
Pros of Redis
  • 11
    High Availibility
  • 6
    Distributed Locking
  • 6
    Distributed compute
  • 5
    Sharding
  • 4
    Load balancing
  • 3
    Map-reduce functionality
  • 3
    Simple-to-use
  • 3
    Written in java. runs on jvm
  • 3
    Publish-subscribe
  • 3
    Sql query support in cluster wide
  • 2
    Optimis locking for map
  • 2
    Performance
  • 2
    Multiple client language support
  • 2
    Rest interface
  • 1
    Admin Interface (Management Center)
  • 1
    Better Documentation
  • 1
    Easy to use
  • 1
    Super Fast
  • 886
    Performance
  • 542
    Super fast
  • 513
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
  • 194
    Open source
  • 182
    Easy to deploy
  • 164
    Stable
  • 155
    Free
  • 121
    Fast
  • 42
    High-Performance
  • 40
    High Availability
  • 35
    Data Structures
  • 32
    Very Scalable
  • 24
    Replication
  • 22
    Great community
  • 22
    Pub/Sub
  • 19
    "NoSQL" key-value data store
  • 16
    Hashes
  • 13
    Sets
  • 11
    Sorted Sets
  • 10
    NoSQL
  • 10
    Lists
  • 9
    Async replication
  • 9
    BSD licensed
  • 8
    Bitmaps
  • 8
    Integrates super easy with Sidekiq for Rails background
  • 7
    Keys with a limited time-to-live
  • 7
    Open Source
  • 6
    Lua scripting
  • 6
    Strings
  • 5
    Awesomeness for Free
  • 5
    Hyperloglogs
  • 4
    Transactions
  • 4
    Outstanding performance
  • 4
    Runs server side LUA
  • 4
    LRU eviction of keys
  • 4
    Feature Rich
  • 4
    Written in ANSI C
  • 4
    Networked
  • 3
    Data structure server
  • 3
    Performance & ease of use
  • 2
    Dont save data if no subscribers are found
  • 2
    Automatic failover
  • 2
    Easy to use
  • 2
    Temporarily kept on disk
  • 2
    Scalable
  • 2
    Existing Laravel Integration
  • 2
    Channels concept
  • 2
    Object [key/value] size each 500 MB
  • 2
    Simple

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Cons of Hazelcast
Cons of Redis
  • 4
    License needed for SSL
  • 15
    Cannot query objects directly
  • 3
    No secondary indexes for non-numeric data types
  • 1
    No WAL

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What is Hazelcast?

With its various distributed data structures, distributed caching capabilities, elastic nature, memcache support, integration with Spring and Hibernate and more importantly with so many happy users, Hazelcast is feature-rich, enterprise-ready and developer-friendly in-memory data grid solution.

What is Redis?

Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.

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Jobs that mention Hazelcast and Redis as a desired skillset
LaunchDarkly
Oakland, California, United States
What companies use Hazelcast?
What companies use Redis?
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What tools integrate with Redis?

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What are some alternatives to Hazelcast and Redis?
Apache Spark
Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
Cassandra
Partitioning means that Cassandra can distribute your data across multiple machines in an application-transparent matter. Cassandra will automatically repartition as machines are added and removed from the cluster. Row store means that like relational databases, Cassandra organizes data by rows and columns. The Cassandra Query Language (CQL) is a close relative of SQL.
Memcached
Memcached is an in-memory key-value store for small chunks of arbitrary data (strings, objects) from results of database calls, API calls, or page rendering.
Apache Ignite
It is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads delivering in-memory speeds at petabyte scale
RabbitMQ
RabbitMQ gives your applications a common platform to send and receive messages, and your messages a safe place to live until received.
See all alternatives