DBMS > Dgraph vs. Hive vs. Postgres-XL vs. Stardog vs. Transbase

System Properties Comparison Dgraph vs. Hive vs. Postgres-XL vs. Stardog vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.45
Rank	#156	Overall
	#15	Graph DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

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

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.transaction.de/en/products/transbase.html

Technical documentation

dgraph.io/docs

cwiki.apache.org/confluence/display/Hive/Home

www.postgres-xl.org/documentation

docs.stardog.com

www.transaction.de/en/products/transbase/features.html

Developer

Dgraph Labs, Inc.

Apache Software Foundation

initially developed by Facebook

Stardog-Union

Transaction Software GmbH

Initial release

2016

2012

2014

since 2012, originally named StormDB

2010

1987

Current release

3.1.3, April 2022

10 R1, October 2018

7.3.0, May 2020

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2

Open Source

Mozilla public license

commercial

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

commercial

free development license

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

Java

C and C++

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

Linux
macOS

Linux
macOS
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

schema-free

yes

schema-free and OWL/RDFS-schema support

yes

Typing

predefined data types such as float or date

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.

yes

XML type, but no XML query functionality

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

distributed, parallel query execution

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

yes

APIs and other access methods

GraphQL query language
gRPC (using protocol buffers) API
HTTP API

JDBC
ODBC
Thrift

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

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

C++
Java
PHP
Python

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

user defined functions and aggregates, HTTP Server extensions in Java

yes

Triggers

yes

via event handlers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication via Raft

selectable replication factor

Multi-source replication in HA-Cluster

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

relationships in graphs

yes

Transaction concepts

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

ACID

MVCC

ACID

yes

Concurrency

Support for concurrent manipulation of data

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

Planned for future releases

Access rights for users, groups and roles

fine grained access rights according to SQL-standard

Access rights for users and roles

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

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