DBMS > AgensGraph vs. Apache Druid vs. EJDB vs. Stardog

System Properties Comparison AgensGraph vs. Apache Druid vs. EJDB vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

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

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

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

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS
Time Series DBMS

Document store

Graph DBMS
RDF store

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

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	3.34
Rank	#88	Overall
	#48	Relational DBMS
	#7	Time Series DBMS

Score	0.27
Rank	#297	Overall
	#44	Document stores

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

bitnine.net/agensgraph

druid.apache.org

github.com/Softmotions/ejdb

www.stardog.com

Technical documentation

bitnine.net/documentation

druid.apache.org/docs/latest/design

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

docs.stardog.com

Developer

Bitnine Global Inc.

Apache Software Foundation and contributors

Softmotions

Stardog-Union

Initial release

2016

2012

2010

Current release

2.1, December 2018

29.0.1, April 2024

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Apache License 2.0

Open Source

Apache license v2

Open Source

GPLv2

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix

server-less

Linux
macOS
Windows

Data scheme

depending on used data model

yes

schema-less columns are supported

schema-free

schema-free and OWL/RDFS-schema support

Typing

predefined data types such as float or date

yes

string, integer, double, bool, date, object_id

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

SQL for querying

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Cypher Query Language
JDBC

JDBC
RESTful HTTP/JSON API

in-process shared library

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

C
Java
JavaScript
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

manual/auto, time-based

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, via HDFS, S3 or other storage engines

none

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

typically not needed, however similar functionality with collection joins possible

yes

relationships in graphs

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/Write Locking

yes

Durability

Support for making data persistent

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

fine grained access rights according to SQL-standard

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

Access rights for users and roles

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