DBMS > Dgraph vs. EsgynDB vs. Graph Engine vs. TigerGraph vs. XTDB

System Properties Comparison Dgraph vs. EsgynDB vs. Graph Engine vs. TigerGraph vs. XTDB

Editorial information provided by DB-Engines

Name

Description

Distributed and scalable native Graph DBMS

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Graph DBMS

Relational DBMS

Graph DBMS
Key-value store

Graph DBMS

Document store

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

Score	1.53
Rank	#152	Overall
	#15	Graph DBMS

Score	0.25
Rank	#312	Overall
	#138	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

dgraph.io/docs

www.graphengine.io/docs/manual

docs.tigergraph.com

www.xtdb.com/docs

Developer

Dgraph Labs, Inc.

Esgyn

Microsoft

Juxt Ltd.

Initial release

2016

2015

2010

2017

2019

Current release

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT License

commercial

Open Source

MIT 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

C++, Java

.NET and C

C++

Clojure

Server operating systems

Linux
OS X
Windows

Linux

.NET

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

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

SQL-like query language (GSQL)

limited SQL, making use of Apache Calcite

APIs and other access methods

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

ADO.NET
JDBC
ODBC

RESTful HTTP API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

HTTP REST
JDBC

Supported programming languages

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

All languages supporting JDBC/ODBC/ADO.Net

C#
C++
F#
Visual Basic

C++
Java

Clojure
Java

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Triggers

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

Multi-source replication between multi datacenters

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

fine grained access rights according to SQL-standard

Role-based access control

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More resources

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