DBMS > EJDB vs. OrigoDB vs. Quasardb vs. Vitess

System Properties Comparison EJDB vs. OrigoDB vs. Quasardb vs. Vitess

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

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

A fully ACID in-memory object graph database

Distributed, high-performance timeseries database

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store

Document store
Object oriented DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

github.com/Softmotions/ejdb

origodb.com

quasar.ai

vitess.io

Technical documentation

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

origodb.com/docs

doc.quasar.ai/master

vitess.io/docs

Developer

Softmotions

Robert Friberg et al

quasardb

The Linux Foundation, PlanetScale

Initial release

2012

2009

under the name LiveDB

2009

2013

Current release

3.14.1, January 2024

15.0.2, December 2022

License

Commercial or Open Source

Open Source

GPLv2

Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache Version 2.0, commercial licenses available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C++

Server operating systems

server-less

Linux
Windows

BSD
Linux
OS X
Windows

Docker
Linux
macOS

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

User defined using .NET types and collections

yes

integer and binary

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.

can be achieved using .NET

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

in-process shared library

.NET Client API
HTTP API
LINQ

HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

.Net

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

proprietary syntax

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

depending on model

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

Write ahead log

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Role based authorization

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

no user groups or roles

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Hubble Unexpectedly Finds Double Quasar in Distant Universe
4 October 2023, Science@NASA

Record quasar is most luminous object in the universe
20 February 2024, EarthSky

Quasar Partners with PTC to Empower IoT Customers with High-Performance Data Solutions
11 September 2023, Datanami

Quasar Selected by National Renewable Energy Laboratory to Help with Energy System De-risking and Optimization
6 June 2023, PR Newswire

QUASAR yacht (Bilgin, 46.8m, 2016)
3 July 2023, Boat International

provided by Google News

Vitess, the database clustering system powering YouTube, graduates CNCF incubation
5 November 2019, SiliconANGLE News

PlanetScale Unveils Distributed MySQL Database Service Based on Vitess
18 May 2021, Datanami

PlanetScale grabs YouTube-developed open-source tech, promises Vitess DBaaS with on-the-fly schema changes
18 May 2021, The Register

They scaled YouTube — now they’ll shard everyone with PlanetScale
13 December 2018, TechCrunch

Massively Scaling MySQL Using Vitess
19 February 2019, InfoQ.com

provided by Google News

Share this page