DBMS > Bangdb vs. GraphDB vs. SurrealDB vs. TigerGraph

System Properties Comparison Bangdb vs. GraphDB vs. SurrealDB vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

A fully ACID transactional, developer-friendly, multi-model DBMS

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Graph DBMS
RDF store

Document store
Graph DBMS

Graph DBMS

Secondary database models

Spatial DBMS

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	3.25
Rank	#91	Overall
	#7	Graph DBMS
	#4	RDF stores

Score	1.02
Rank	#190	Overall
	#33	Document stores
	#18	Graph DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

Technical documentation

docs.bangdb.com

graphdb.ontotext.com/documentation

surrealdb.com/docs

docs.tigergraph.com

Social network pages

Developer

Sachin Sinha, BangDB

Ontotext

SurrealDB Ltd

Initial release

2012

2000

2022

2017

Current release

BangDB 2.0, October 2021

10.4, October 2023

v1.5.0, May 2024

License

Commercial or Open Source

Open Source

BSD 3

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

commercial

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, C++

Java

Rust

C++

Server operating systems

Linux

All OS with a Java VM
Linux
OS X
Windows

Linux
macOS
Windows

Linux

Data scheme

schema-free

schema-free and OWL/RDFS-schema support; RDF shapes

schema-free

yes

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

SQL

Support of SQL

SQL like support with command line tool

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

SQL-like query language

SQL-like query language (GSQL)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

GraphQL
RESTful HTTP API
WebSocket

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

Supported programming languages

C
C#
C++
Java
Python

.Net
C#
Clojure
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Deno
Go
JavaScript (Node.js)
Rust

C++
Java

Server-side scripts

Stored procedures

well-defined plugin interfaces; JavaScript server-side extensibility

yes

Triggers

yes, Notifications (with Streaming only)

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Foreign keys

Referential integrity

yes

Constraint checking

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, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes, run db with in-memory only mode

User concepts

Access control

yes (enterprise version only)

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

yes, based on authentication and database rules

Role-based access control

More information provided by the system vendor

Specific characteristics

Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more

Competitive advantages

GraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more

Typical application scenarios

Metadata enrichment and management, linked data publishing, semantic inferencing...
» more

Key customers

GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more

Market metrics

GraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more

Licensing and pricing models

GraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more

News

Riding the Databricks Wave with Hybrid Knowledge Graphs
6 June 2024

Matching Skills and Candidates with Graph RAG
31 May 2024

A Triple Store RAG Retriever
29 May 2024

Integrating GraphDB with Relational Database Systems
23 May 2024

Understanding the Graph Center of Excellence
17 May 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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