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Hazelcast
Hazelcast

130
103
+ 1
36
RocksDB
RocksDB

46
55
+ 1
10
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Hazelcast vs RocksDB: 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, RocksDB is detailed as "Embeddable persistent key-value store for fast storage, developed and maintained by Facebook Database Engineering Team". RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.

Hazelcast can be classified as a tool in the "In-Memory Databases" category, while RocksDB is grouped under "Databases".

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, RocksDB provides the following key features:

  • Designed for application servers wanting to store up to a few terabytes of data on locally attached Flash drives or in RAM
  • Optimized for storing small to medium size key-values on fast storage -- flash devices or in-memory
  • Scales linearly with number of CPUs so that it works well on ARM processors

"High Availibility" is the primary reason why developers consider Hazelcast over the competitors, whereas "Very fast" was stated as the key factor in picking RocksDB.

Hazelcast and RocksDB are both open source tools. It seems that RocksDB with 14.1K GitHub stars and 3.09K forks on GitHub has more adoption than Hazelcast with 3.15K GitHub stars and 1.15K GitHub forks.

According to the StackShare community, Hazelcast has a broader approval, being mentioned in 25 company stacks & 15 developers stacks; compared to RocksDB, which is listed in 6 company stacks and 7 developer stacks.

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 RocksDB?

RocksDB is an embeddable persistent key-value store for fast storage. RocksDB can also be the foundation for a client-server database but our current focus is on embedded workloads. RocksDB builds on LevelDB to be scalable to run on servers with many CPU cores, to efficiently use fast storage, to support IO-bound, in-memory and write-once workloads, and to be flexible to allow for innovation.
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      What are some alternatives to Hazelcast and RocksDB?
      Redis
      Redis is an open source, BSD licensed, advanced key-value store. It is often referred to as a data structure server since keys can contain strings, hashes, lists, sets and sorted sets.
      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.
      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.
      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.
      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
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      How developers use Hazelcast and RocksDB
      Avatar of Vital Labs, Inc.
      Vital Labs, Inc. uses HazelcastHazelcast

      HazelCast is the foundation for the distributed system that hosts our APIs and intelligent workflows. We wrap the core HazelCast functions in Clojure protocols to implement micro-services on top of a coherent, single-process instance per virtual node.

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