DBMS > EJDB vs. OrigoDB vs. Quasardb vs. RethinkDB vs. Sphinx

System Properties Comparison EJDB vs. OrigoDB vs. Quasardb vs. RethinkDB vs. Sphinx

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

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Document store

Document store
Object oriented DBMS

Time Series DBMS

Document store

Search engine

Secondary database models

Spatial DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

github.com/Softmotions/ejdb

Technical documentation

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

origodb.com/docs

doc.quasar.ai/master

rethinkdb.com/docs

sphinxsearch.com/docs

Developer

Softmotions

Robert Friberg et al

quasardb

The Linux Foundation

since July 2017

Sphinx Technologies Inc.

Initial release

2012

2009

under the name LiveDB

2009

2001

Current release

3.14.1, January 2024

2.4.1, August 2020

3.5.1, February 2023

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

Open Source

GPL version 2, commercial licence 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

Linux
OS X
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

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

string, binary, float, bool, date, geometry

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

full-text index on all search fields

SQL

Support of SQL

SQL-like query language

SQL-like query language (SphinxQL)

APIs and other access methods

in-process shared library

.NET Client API
HTTP API
LINQ

HTTP API

Proprietary protocol

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

Triggers

yes

Domain Events

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

client side managed; servers are not synchronized

Sharding

consistent hashing

Sharding

range based

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

Source-replica replication with selectable replication factor

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

depending on model

Transaction concepts

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

ACID

Atomic single-document operations

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

MVCC based

yes

Durability

Support for making data persistent

yes

Write ahead log

yes

by using LevelDB

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Role based authorization

Cryptographically strong user authentication and audit trail

yes

users and table-level permissions

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