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Hazelcast

350
472
+ 1
59
LokiJS

37
55
+ 1
3
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Hazelcast vs LokiJS: What are the differences?

Developers describe Hazelcast as "Clustering and highly scalable data distribution platform for Java". 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. On the other hand, LokiJS is detailed as "In-memory JavaScript Datastore with Persistence". 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.

Hazelcast and LokiJS can be categorized as "In-Memory Databases" tools.

Hazelcast and LokiJS are both open source tools. It seems that LokiJS with 4.95K GitHub stars and 389 forks on GitHub has more adoption than Hazelcast with 3.18K GitHub stars and 1.16K GitHub forks.

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Pros of Hazelcast
Pros of LokiJS
  • 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
  • 3
    Can query the objects directly

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Cons of Hazelcast
Cons of LokiJS
  • 4
    License needed for SSL
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    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 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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    Jobs that mention Hazelcast and LokiJS as a desired skillset
    LaunchDarkly
    Oakland, California, United States
    What companies use Hazelcast?
    What companies use LokiJS?
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    What tools integrate with Hazelcast?
    What tools integrate with LokiJS?

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    What are some alternatives to Hazelcast and LokiJS?
    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