DBMS > AgensGraph vs. EJDB vs. Fauna vs. JSqlDb vs. Microsoft Azure Cosmos DB

System Properties Comparison AgensGraph vs. EJDB vs. Fauna vs. JSqlDb vs. Microsoft Azure Cosmos DB

Editorial information provided by DB-Engines

Name

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

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Embeddable document-store database library with JSON representation of queries (in MongoDB style)

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

JavaScript Query Language Database, stores JavaScript objects and primitives

Globally distributed, horizontally scalable, multi-model database service

Primary database model

Graph DBMS
Relational DBMS

Document store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Document store
Object oriented DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Secondary database models

Spatial DBMS

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

Score	0.17
Rank	#320	Overall
	#29	Graph DBMS
	#143	Relational DBMS

Score	0.13
Rank	#331	Overall
	#46	Document stores

Score	1.55
Rank	#143	Overall
	#26	Document stores
	#13	Graph DBMS
	#66	Relational DBMS
	#13	Time Series DBMS

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Website

bitnine.net/agensgraph

github.com/Softmotions/ejdb

fauna.com

jsqldb.org (offline)

azure.microsoft.com/services/cosmos-db

Technical documentation

bitnine.net/documentation

github.com/Softmotions/ejdb/blob/master/README.md

docs.fauna.com

learn.microsoft.com/azure/cosmos-db

Developer

Bitnine Global Inc.

Softmotions

Fauna, Inc.

Konrad von Backstrom

Microsoft

Initial release

2016

2012

2014

2018

2014

Current release

2.1, December 2018

0.8, December 2018

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

GPLv2

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

Scala

Server operating systems

Linux
OS X
Windows

server-less

hosted

Linux
macOS
Windows

hosted

Data scheme

depending on used data model

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

JSON types

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.

Secondary indexes

yes

All properties auto-indexed by default

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

Cypher Query Language
JDBC

in-process shared library

RESTful HTTP API

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

Supported programming languages

C
Java
JavaScript
Python

Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

JavaScript

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

Server-side scripts

Stored procedures

yes

user defined functions

functions in JavaScript

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

none

horizontal partitioning

consistent hashing

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

Multi-source replication

none

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

using RocksDB

yes

In-memory capabilities

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

User concepts

Access control

fine grained access rights according to SQL-standard

Identity management, authentication, and access control

Access rights can be defined down to the item level

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