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  1. Stackups
  2. Application & Data
  3. In-Memory Databases
  4. In Memory Databases
  5. Ehcache vs Hazelcast

Ehcache vs Hazelcast

OverviewComparisonAlternatives

Overview

Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K
Ehcache
Ehcache
Stacks616
Followers160
Votes4
GitHub Stars2.1K
Forks585

Ehcache vs Hazelcast: What are the differences?

  1. Key difference 1: Distributed Data: Ehcache is primarily a local caching mechanism, meaning it stores data on a single node or machine. On the other hand, Hazelcast is a distributed caching system, allowing the data to be shared across multiple nodes or machines in a cluster. This enables better scalability and fault tolerance for Hazelcast compared to Ehcache.

  2. Key difference 2: Replication: Ehcache only supports simple replication of data within a single node, while Hazelcast provides full data replication across all nodes in the cluster. This means that in case of failure, the data can be readily available on other nodes in Hazelcast, whereas in Ehcache, the data would be lost unless additional measures are taken.

  3. Key difference 3: Eventual Consistency: Ehcache emphasizes strong consistency, meaning that data is immediately updated and available for all subsequent read operations. On the other hand, Hazelcast supports eventual consistency, where data updates may take some time to propagate across the cluster. This trade-off allows Hazelcast to achieve better performance and scalability by reducing the need for synchronous updates.

  4. Key difference 4: Querying Capabilities: While Ehcache provides limited querying capabilities through its local cache, Hazelcast offers a robust querying API that allows for complex queries across distributed data. This enables developers to perform more advanced operations, such as filtering and sorting, directly on the Hazelcast cache.

  5. Key difference 5: Open Source Licensing: Ehcache is an open source caching library developed by Terracotta Inc., while Hazelcast is a fully open source in-memory data grid platform. This means that Hazelcast is more flexible and can be customized as per the specific requirements of the application.

  6. Key difference 6: Integration: Ehcache has good integration with popular frameworks like Hibernate and Spring, making it easier to incorporate caching in applications built on these frameworks. Hazelcast, on the other hand, provides a broader range of integration options, including support for Java Caching API (JSR-107) and integration with other distributed systems like Apache Kafka and Apache Ignite.

In Summary, Ehcache is a local caching mechanism with limited querying capabilities, while Hazelcast is a distributed caching system with robust querying API, full data replication, and better integration options.

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Detailed Comparison

Hazelcast
Hazelcast
Ehcache
Ehcache

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.

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.

Distributed implementations of java.util.{Queue, Set, List, Map};Distributed implementation of java.util.concurrent.locks.Lock;Distributed implementation of java.util.concurrent.ExecutorService;Distributed MultiMap for one-to-many relationships;Distributed Topic for publish/subscribe messaging;Synchronous (write-through) and asynchronous (write-behind) persistence;Transaction support;Socket level encryption support for secure clusters;Second level cache provider for Hibernate;Monitoring and management of the cluster via JMX;Dynamic HTTP session clustering;Support for cluster info and membership events;Dynamic discovery, scaling, partitioning with backups and fail-over
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Statistics
GitHub Stars
6.4K
GitHub Stars
2.1K
GitHub Forks
1.9K
GitHub Forks
585
Stacks
427
Stacks
616
Followers
474
Followers
160
Votes
59
Votes
4
Pros & Cons
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
Pros
  • 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
Integrations
Java
Java
Spring
Spring
No integrations available

What are some alternatives to Hazelcast, Ehcache?

Redis

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.

Aerospike

Aerospike

Aerospike is an open-source, modern database built from the ground up to push the limits of flash storage, processors and networks. It was designed to operate with predictable low latency at high throughput with uncompromising reliability – both high availability and ACID guarantees.

MemSQL

MemSQL

MemSQL converges transactions and analytics for sub-second data processing and reporting. Real-time businesses can build robust applications on a simple and scalable infrastructure that complements and extends existing data pipelines.

Apache Ignite

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

SAP HANA

SAP HANA

It is an application that uses in-memory database technology that allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows it to process data stored in RAM as opposed to reading it from a disk.

VoltDB

VoltDB

VoltDB is a fundamental redesign of the RDBMS that provides unparalleled performance and scalability on bare-metal, virtualized and cloud infrastructures. VoltDB is a modern in-memory architecture that supports both SQL + Java with data durability and fault tolerance.

Tarantool

Tarantool

It is designed to give you the flexibility, scalability, and performance that you want, as well as the reliability and manageability that you need in mission-critical applications

Azure Redis Cache

Azure Redis Cache

It perfectly complements Azure database services such as Cosmos DB. It provides a cost-effective solution to scale read and write throughput of your data tier. Store and share database query results, session states, static contents, and more using a common cache-aside pattern.

KeyDB

KeyDB

KeyDB is a fully open source database that aims to make use of all hardware resources. KeyDB makes it possible to breach boundaries often dictated by price and complexity.

LokiJS

LokiJS

LokiJS is a document oriented database written in javascript, published under MIT License. Its purpose is to store javascript objects as documents in a nosql fashion and retrieve them with a similar mechanism. Runs in node (including cordova/phonegap and node-webkit), nativescript and the browser.

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