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Hazelcast

420
467
+ 1
59
VoltDB

17
69
+ 1
18
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Hazelcast vs VoltDB: What are the differences?

What is Hazelcast? 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.

What is VoltDB? In-memory relational DBMS capable of supporting millions of database operations per second. 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.

Hazelcast and VoltDB belong to "In-Memory Databases" category of the tech stack.

Some of the features offered by Hazelcast are:

  • Distributed implementations of java.util.{Queue, Set, List, Map}
  • Distributed implementation of java.util.concurrent.locks.Lock
  • Distributed implementation of java.util.concurrent.ExecutorService

On the other hand, VoltDB provides the following key features:

  • In-Memory Performance with On-Disk Durability
  • Transparent Scalability with Data Consistency
  • NewSQL – All the benefits of SQL with Unlimited Scalability

"High Availibility" is the primary reason why developers consider Hazelcast over the competitors, whereas "SQL + Java" was stated as the key factor in picking VoltDB.

Hazelcast is an open source tool with 3.18K GitHub stars and 1.16K GitHub forks. Here's a link to Hazelcast's open source repository on GitHub.

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Pros of Hazelcast
Pros of VoltDB
  • 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
  • 5
    SQL + Java
  • 4
    In-memory database
  • 4
    A brainchild of Michael Stonebraker
  • 3
    Very Fast
  • 2
    NewSQL

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Cons of Hazelcast
Cons of VoltDB
  • 4
    License needed for SSL
    Be the first to leave a con

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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 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.

    Need advice about which tool to choose?Ask the StackShare community!

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

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    What are some alternatives to Hazelcast and VoltDB?
    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