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Apache Ignite

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Ehcache

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Apache Ignite vs Ehcache: What are the differences?

Introduction

Apache Ignite and Ehcache are both popular options for in-memory caching in the Java ecosystem. While they serve the same purpose of improving application performance by storing data in memory, there are some key differences between the two. In this article, we will explore the main distinctions between Apache Ignite and Ehcache.

  1. Scalability and Distributed Computing: One of the primary differences between Apache Ignite and Ehcache is their approach to scalability and distributed computing. Apache Ignite is designed for distributed architectures and can be easily scaled across multiple nodes in a cluster. It allows for data and computation to be distributed across the nodes, providing high availability and fault tolerance. On the other hand, Ehcache is more focused on local caching and does not provide built-in support for distributed caching or computing. It is primarily intended for single-node caching scenarios.

  2. Data Partitioning and Replication: Apache Ignite offers advanced data partitioning and replication capabilities. It automatically partitions the data across multiple nodes in a cluster, ensuring that each node only holds a portion of the overall data set. This enables parallel processing and efficient data retrieval. In addition, Ignite allows for configurable data replication, ensuring data redundancy and fault tolerance. Ehcache, on the other hand, does not provide built-in support for data partitioning and replication. It relies on a single-node caching model and does not offer automatic data distribution or replication.

  3. Computational Capabilities: While both Apache Ignite and Ehcache provide caching functionality, Apache Ignite goes beyond caching and offers a wide range of computational capabilities. Ignite supports running distributed computations across the cluster, allowing for parallel processing and improved performance. It provides APIs for distributed SQL queries, machine learning, and real-time streaming analytics. Ehcache, on the other hand, is primarily focused on caching and does not offer extensive computational features.

  4. Integration with Other Technologies: Apache Ignite is designed to integrate seamlessly with various other technologies and frameworks. It provides connectors and integrations for popular data processing frameworks like Apache Spark, Apache Hadoop, and Apache Cassandra. It also offers support for various persistence stores, such as JDBC, NoSQL databases, and Hadoop Distributed File System (HDFS). Ehcache, on the other hand, is more standalone and does not offer as many integrations or connectors out of the box. It is typically used as a standalone caching solution without extensive integration with other technologies.

  5. Transaction Support: Another major difference between Apache Ignite and Ehcache is their support for transactions. Apache Ignite provides full support for distributed transactions, allowing multiple nodes in a cluster to participate in a single transaction. It ensures consistency and isolation across the distributed cache. Ehcache, on the other hand, does not offer built-in support for distributed transactions. It relies on the underlying storage system for transaction support, which may not be suitable for all use cases.

  6. Management and Monitoring Capabilities: Apache Ignite provides comprehensive management and monitoring capabilities out of the box. It offers a web-based management console for monitoring the cluster status, metrics, and performance. It also provides a REST API for programmatic monitoring and management. Ehcache, on the other hand, does not offer as many management and monitoring features. It does provide basic statistics and monitoring information but lacks a dedicated management console or extensive programmatic APIs.

In summary, Apache Ignite and Ehcache differ in scalability and distributed computing capabilities, data partitioning and replication support, computational capabilities, integration with other technologies, transaction support, and management and monitoring capabilities.

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Pros of Apache Ignite
Pros of Ehcache
  • 5
    Written in java. runs on jvm
  • 5
    Multiple client language support
  • 5
    Free
  • 5
    High Avaliability
  • 4
    Rest interface
  • 4
    Sql query support in cluster wide
  • 4
    Load balancing
  • 3
    Distributed compute
  • 3
    Better Documentation
  • 2
    Easy to use
  • 1
    Distributed Locking
  • 1
    Way Faster than Redis and Elasticache Redis
  • 1
    Easy setup
  • 1
    Simpler to run in testing environment
  • 1
    Container doesn't have to be running for local tests

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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 Ehcache?

Ehcache is an open source, standards-based cache for boosting performance, offloading your database, and simplifying scalability. It's the most widely-used Java-based cache because it's robust, proven, and full-featured. Ehcache scales from in-process, with one or more nodes, all the way to mixed in-process/out-of-process configurations with terabyte-sized caches.

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What companies use Apache Ignite?
What companies use Ehcache?
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What tools integrate with Apache Ignite?
What tools integrate with Ehcache?
    No integrations found

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    What are some alternatives to Apache Ignite and Ehcache?
    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.
    MySQL
    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.
    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.
    MongoDB
    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.
    See all alternatives