DBMS > Hazelcast vs. JSqlDb vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Sqrrl

System Properties Comparison Hazelcast vs. JSqlDb vs. Microsoft Azure Cosmos DB vs. PouchDB vs. Sqrrl

Editorial information provided by DB-Engines

Name

JSqlDB seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.

Description

A widely adopted in-memory data grid

JavaScript Query Language Database, stores JavaScript objects and primitives

Globally distributed, horizontally scalable, multi-model database service

JavaScript DBMS with an API inspired by CouchDB

Adaptable, secure NoSQL built on Apache Accumulo

Primary database model

Key-value store

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	6.87
Rank	#55	Overall
	#6	Key-value stores

Score	29.85
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	2.35
Rank	#116	Overall
	#22	Document stores

Website

hazelcast.com

jsqldb.org (offline)

azure.microsoft.com/services/cosmos-db

pouchdb.com

sqrrl.com

Technical documentation

hazelcast.org/imdg/docs

learn.microsoft.com/azure/cosmos-db

pouchdb.com/guides

Developer

Hazelcast

Konrad von Backstrom

Microsoft

Apache Software Foundation

Amazon

originally Sqrrl Data, Inc.

Initial release

2008

2018

2014

2012

Current release

5.3.6, November 2023

0.8, December 2018

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

Open Source

commercial

Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

JavaScript

Java

Server operating systems

All OS with a Java VM

Linux
macOS
Windows

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

the object must implement a serialization strategy

Secondary indexes

yes

All properties auto-indexed by default

yes

via views

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP REST

only for PouchDB Server
JavaScript API

Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

JavaScript

Actionscript
C

using GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

functions in JavaScript

JavaScript

View functions in JavaScript

Triggers

yes

Events

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

Sharding

with a proxy-based framework, named couchdb-lounge

Sharding

making use of Hadoop

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

none

yes

Implicit feature of the cloud service

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

selectable replication factor

making use of Hadoop

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Immediate Consistency

Document store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.

Foreign keys

Referential integrity

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

one or two-phase-commit; repeatable reads; read commited

Multi-item ACID transactions with snapshot isolation within a partition

Atomic updates per row, document, or graph entity

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

by using IndexedDB, WebSQL or LevelDB as backend

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

Role-based access control

Access rights can be defined down to the item level

Cell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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