DBMS > BigObject vs. EsgynDB vs. OrigoDB vs. Stardog vs. TerarkDB

System Properties Comparison BigObject vs. EsgynDB vs. OrigoDB vs. Stardog vs. TerarkDB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A fully ACID in-memory object graph database

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Document store
Object oriented DBMS

Graph DBMS
RDF store

Key-value store

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

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

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

github.com/bytedance/terarkdb

Technical documentation

docs.bigobject.io

origodb.com/docs

docs.stardog.com

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

BigObject, Inc.

Esgyn

Robert Friberg et al

Stardog-Union

ByteDance, originally Terark

Initial release

2015

2009

under the name LiveDB

2010

2016

Current release

7.3.0, May 2020

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

commercial

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

commercial

restricted open source version 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++, Java

Java

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

schema-free and OWL/RDFS-schema support

schema-free

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like DML and DDL statements

yes

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

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

.NET Client API
HTTP API
LINQ

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

C++ API
Java API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C++
Java

Server-side scripts

Stored procedures

Lua

Java Stored Procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

Domain Events

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication between multi datacenters

Source-replica replication

Multi-source replication in HA-Cluster

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

depending on model

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 lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Write ahead log

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

Role based authorization

Access rights for users and roles

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