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Apache Ignite vs Redis: What are the differences?

Apache Ignite and Redis are both in-memory data storage systems that offer high-performance and scalability. Let's explore the key differences between them.

  1. Data Model: Apache Ignite offers a flexible data model that supports key-value, SQL, and compute grid functionalities. It allows users to store and manipulate complex structured data using SQL queries, distributed compute operations, and in-memory key-value stores. On the other hand, Redis primarily focuses on a simple key-value data model, offering basic data structures like strings, lists, sets, and hashes.

  2. Durability: Apache Ignite provides durability by automatically storing data in memory as well as on disk, ensuring data persistence even after system restarts or failures. It supports write-ahead logging, replication, and data partitioning across the cluster. While Redis also supports data persistence, it typically relies on periodic snapshots and append-only log files for durability, which may result in some data loss in case of system failures.

  3. Scalability: Apache Ignite offers horizontal scalability by distributing data across the cluster nodes using data partitioning techniques. It can handle much larger datasets and higher workloads by leveraging distributed computing capabilities. Redis, on the other hand, has a single-threaded architecture that can limit its scalability for certain use cases, although it offers a clustering feature to scale out by sharding data across multiple nodes.

  4. Supported Data Types: Redis provides a rich set of built-in data structures like strings, lists, sets, sorted sets, and hashes, allowing users to perform various operations on these data types. Apache Ignite, in addition to key-value operations, supports more complex data structures like collections, maps, and SQL tables, enabling users to perform advanced computations and queries on the stored data.

  5. Persistence Options: Redis offers different persistence options, including both snapshotting and append-only log files (AOF). Users can choose between these options based on their requirements for data durability and recovery. Apache Ignite, on the other hand, supports write-ahead logging to ensure data durability and provides multiple choices for storage, including in-memory, disk-based, or a combination of both, allowing users to optimize performance and persistence based on their specific needs.

  6. Parallel Query Processing: Apache Ignite supports distributed SQL queries, allowing users to execute complex queries across the entire dataset in a parallel and distributed manner. It leverages its distributed computing capabilities to optimize query processing and achieve faster query response times. Redis, on the other hand, does not provide built-in support for distributed SQL queries and primarily focuses on key-value operations.

In summary, Apache Ignite is a distributed in-memory computing platform that integrates with existing data sources and provides features like distributed caching, compute grid, and streaming processing, making it suitable for large-scale, high-performance data processing and analytics. Redis, on the other hand, is an open-source, in-memory data structure store known for its simplicity, speed, and versatility, primarily used for caching, real-time analytics, and message brokering in web applications.

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Pros of Apache Ignite
Pros of Redis
  • 5
    Written in java. runs on jvm
  • 5
    Multiple client language support
  • 5
  • 5
    High Avaliability
  • 4
    Sql query support in cluster wide
  • 4
    Rest interface
  • 4
    Load balancing
  • 3
    Distributed compute
  • 3
    Better Documentation
  • 2
    Easy to use
  • 1
    Distributed Locking
  • 886
  • 542
    Super fast
  • 513
    Ease of use
  • 444
    In-memory cache
  • 324
    Advanced key-value cache
  • 194
    Open source
  • 182
    Easy to deploy
  • 164
  • 155
  • 121
  • 42
  • 40
    High Availability
  • 35
    Data Structures
  • 32
    Very Scalable
  • 24
  • 22
    Great community
  • 22
  • 19
    "NoSQL" key-value data store
  • 16
  • 13
  • 11
    Sorted Sets
  • 10
  • 10
  • 9
    Async replication
  • 9
    BSD licensed
  • 8
  • 8
    Integrates super easy with Sidekiq for Rails background
  • 7
    Keys with a limited time-to-live
  • 7
    Open Source
  • 6
    Lua scripting
  • 6
  • 5
    Awesomeness for Free
  • 5
  • 4
  • 4
    Outstanding performance
  • 4
    Runs server side LUA
  • 4
    LRU eviction of keys
  • 4
    Feature Rich
  • 4
    Written in ANSI C
  • 4
  • 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
  • 2
    Existing Laravel Integration
  • 2
    Channels concept
  • 2
    Object [key/value] size each 500 MB
  • 2

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Cons of Apache Ignite
Cons of Redis
    Be the first to leave a con
    • 15
      Cannot query objects directly
    • 3
      No secondary indexes for non-numeric data types
    • 1
      No WAL

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    - No public GitHub repository available -

    What is 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

    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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    Blog Posts

    Nov 20 2019 at 3:38AM


    Jun 6 2019 at 5:11PM


    What are some alternatives to Apache Ignite and Redis?
    The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
    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.
    MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
    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.
    Elasticsearch is a distributed, RESTful search and analytics engine capable of storing data and searching it in near real time. Elasticsearch, Kibana, Beats and Logstash are the Elastic Stack (sometimes called the ELK Stack).
    See all alternatives