DBMS > EJDB vs. HEAVY.AI vs. RDFox vs. StarRocks

System Properties Comparison EJDB vs. HEAVY.AI vs. RDFox vs. StarRocks

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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)

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

High performance knowledge graph and semantic reasoning engine

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Secondary database models

Spatial DBMS

Relational DBMS

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

Score	0.31
Rank	#296	Overall
	#44	Document stores

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	0.93
Rank	#198	Overall
	#93	Relational DBMS

Website

github.com/Softmotions/ejdb

github.com/heavyai/heavydb
www.heavy.ai

www.oxfordsemantic.tech

www.starrocks.io

Technical documentation

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

docs.heavy.ai

docs.oxfordsemantic.tech

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Softmotions

HEAVY.AI, Inc.

Oxford Semantic Technologies

The Linux Foundation

since Feb 2023

Initial release

2012

2016

2017

2020

Current release

5.10, January 2022

6.0, Septermber 2022

3.3, June 2024

License

Commercial or Open Source

Open Source

GPLv2

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

Apache Version 2.0

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++ and CUDA

C++

C++, Java

Server operating systems

server-less

Linux

Linux
macOS
Windows

Linux

Data scheme

schema-free

yes

RDF schemas

yes

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.

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

in-process shared library

JDBC
ODBC
Thrift
Vega

RESTful HTTP API
SPARQL 1.1

JDBC
MySQL protocol

Supported programming languages

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

All languages supporting JDBC/ODBC/Thrift
Python

C
Java

Java

Server-side scripts

Stored procedures

user defined functions

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

replication via a shared file system

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 stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

typically not needed, however similar functionality with collection joins possible

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

Roles, resources, and access types

Role based access control and fine grained access rights

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