Hazelcast vs MongoDB

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Hazelcast
Hazelcast

129
103
+ 1
36
MongoDB
MongoDB

17K
13.4K
+ 1
3.8K
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Hazelcast vs MongoDB: What are the differences?

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; MongoDB: The database for giant ideas. MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.

Hazelcast belongs to "In-Memory Databases" category of the tech stack, while MongoDB can be primarily classified under "Databases".

"High Availibility" is the top reason why over 4 developers like Hazelcast, while over 788 developers mention "Document-oriented storage" as the leading cause for choosing MongoDB.

Hazelcast and MongoDB are both open source tools. It seems that MongoDB with 16.3K GitHub stars and 4.1K forks on GitHub has more adoption than Hazelcast with 3.18K GitHub stars and 1.16K GitHub forks.

Uber Technologies, Lyft, and Codecademy are some of the popular companies that use MongoDB, whereas Hazelcast is used by Yammer, Seat Pagine Gialle, and Para. MongoDB has a broader approval, being mentioned in 2189 company stacks & 2218 developers stacks; compared to Hazelcast, which is listed in 26 company stacks and 16 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 MongoDB?

MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
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    What are some alternatives to Hazelcast and MongoDB?
    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.
    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
    Decisions about Hazelcast and MongoDB
    MongoDB
    MongoDB

    I starting using MongoDB because it was much easier to implement in production then hosted SQL, and found that a lot of the limitation you think of from a document store vs a relational database were overcome by connecting the application to a graphql API, making retrieval seamless. Mongos latest upgrades as well as Stitch and Mongo mobile make it a perfect fit especially if your application will be cross platform web and mobile.

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    Anton Sidelnikov
    Anton Sidelnikov
    Backend Developer at Beamery · | 6 upvotes · 9.1K views
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    In my opinion PostgreSQL is totally over MongoDB - not only works with structured data & SQL & strict types, but also has excellent support for unstructured data as separate data type (you can store arbitrary JSONs - and they may be also queryable, depending on one of format's you may choose). Both writes & reads are much faster, then in Mongo. So you can get best on Document NoSQL & SQL in single database..

    Formal downside of PostgreSQL is clustering scalability. There's not simple way to build distributed a cluster. However, two points:

    1) You will need much more time before you need to actually scale due to PG's efficiency. And if you follow database-per-service pattern, maybe you won't need ever, cause dealing few billion records on single machine is an option for PG.

    2) When you need to - you do it in a way you need, including as a part of app's logic (e.g. sharding by key, or PG-based clustering solution with strict model), scalability will be very transparent, much more obvious than Mongo's "cluster just works (but then fails)" replication.

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    Zach Coffin
    Zach Coffin
    Software Developer · | 3 upvotes · 7.5K views
    PostgreSQL
    PostgreSQL
    MongoDB
    MongoDB

    I started using PostgreSQL because I started a job at a company that was already using it as well as MongoDB. The main difference between the two from my perspective is that postgres columns are a chore to add/remove/modify whereas you can throw whatever you want into a mongo collection. And personally I prefer the query language for postgres over that of mongo, but they both have their merits. Maybe some