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Hazelcast

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XAP

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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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Pros of Hazelcast
Pros of XAP
  • 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
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    Cons of Hazelcast
    Cons of XAP
    • 4
      License needed for SSL
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      - No public GitHub repository available -

      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 XAP?

      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.

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

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      What are some alternatives to Hazelcast and XAP?
      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.
      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
      See all alternatives