DBMS > EJDB vs. Fauna vs. Microsoft Azure Table Storage vs. NSDb

System Properties Comparison EJDB vs. Fauna vs. Microsoft Azure Table Storage vs. NSDb

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Editorial information provided by DB-Engines

Name

Description

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.

A Wide Column Store for rapid development using massive semi-structured datasets

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Primary database model

Document store

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Wide column store

Time Series DBMS

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

Score	0.30
Rank	#294	Overall
	#44	Document stores

Score	1.55
Rank	#154	Overall
	#26	Document stores
	#14	Graph DBMS
	#72	Relational DBMS
	#13	Time Series DBMS

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Website

github.com/Softmotions/ejdb

fauna.com

azure.microsoft.com/en-us/services/storage/tables

nsdb.io

Technical documentation

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

docs.fauna.com

nsdb.io/Architecture

Developer

Softmotions

Fauna, Inc.

Microsoft

Initial release

2012

2014

2012

2017

License

Commercial or Open Source

Open Source

GPLv2

commercial

Open Source

Apache Version 2.0

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

Java, Scala

Server operating systems

server-less

hosted

Linux
macOS

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

yes

yes: int, bigint, decimal, string

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 fields are automatically indexed

SQL

Support of SQL

SQL-like query language

APIs and other access methods

in-process shared library

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Supported programming languages

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

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Scala

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

consistent hashing

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

In-memory capabilities

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

User concepts

Access control

Identity management, authentication, and access control

Access rights based on private key authentication or shared access signatures

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More resources

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