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

Hazelcast vs XAP

OverviewComparisonAlternatives

Overview

Hazelcast
Hazelcast
Stacks427
Followers474
Votes59
GitHub Stars6.4K
Forks1.9K
XAP
XAP
Stacks1
Followers2
Votes0

Hazelcast vs XAP: What are the differences?

Introduction

When comparing Hazelcast and XAP, it's important to understand the key differences between these two technologies. Both are popular choices for distributed computing solutions, but they have distinct features that set them apart.

  1. Architecture: Hazelcast is an open-source in-memory data grid platform that provides distributed caching and in-memory data storage. On the other hand, XAP (GigaSpaces) is a distributed application server that offers a complete solution for complex event processing, real-time analytics, and high-performance transaction processing.

  2. Integration Capabilities: Hazelcast's integration capabilities focus on providing easy integration with various programming languages and frameworks. In contrast, XAP offers extensive integration with enterprise systems, databases, and messaging platforms, making it suitable for integrating with existing enterprise environments seamlessly.

  3. Scaling Options: Hazelcast is known for its scalability and can easily scale horizontally by adding more nodes to the cluster. XAP, on the other hand, provides dynamic scaling capabilities with built-in support for auto-scaling based on workloads and resource utilization.

  4. Transaction Management: Hazelcast provides support for distributed transactions through its distributed transaction manager while XAP offers a more robust transaction management framework with support for XA transactions, ensuring data consistency across distributed systems.

  5. Deployment Flexibility: Hazelcast is often deployed as a standalone in-memory data grid or caching solution, whereas XAP is designed for deployments requiring high availability, fault tolerance, and data consistency in mission-critical applications.

  6. Real-time Analytics: XAP has a strong focus on real-time analytics capabilities, providing tools and frameworks for processing and analyzing large volumes of data in real-time, making it suitable for use cases requiring fast and efficient data processing.

In Summary, when comparing Hazelcast and XAP, it's essential to consider factors like architecture, integration capabilities, scaling options, transaction management, deployment flexibility, and real-time analytics to choose the best solution for your distributed computing needs.

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Detailed Comparison

Hazelcast
Hazelcast
XAP
XAP

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.

It provides an essential set of data store features, such as transactions, indexes, and query language (SQL-like queries). It also handles common functions such as messaging, event processing, data access, and transaction processing (ACID compliant) completely and exclusively in-memory.

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
In-memory data grid; Stream processing for mission-critical applications; Empowers event-driven microservices and distributed applications for real-time big data innovation
Statistics
GitHub Stars
6.4K
GitHub Stars
-
GitHub Forks
1.9K
GitHub Forks
-
Stacks
427
Stacks
1
Followers
474
Followers
2
Votes
59
Votes
0
Pros & Cons
Pros
  • 11
    High Availibility
  • 6
    Distributed compute
  • 6
    Distributed Locking
  • 5
    Sharding
  • 4
    Load balancing
Cons
  • 4
    License needed for SSL
No community feedback yet
Integrations
Java
Java
Spring
Spring
Spring
Spring
Java
Java
.NET
.NET

What are some alternatives to Hazelcast, XAP?

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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