DBMS > Couchbase vs. JanusGraph vs. SAP Adaptive Server vs. TigerGraph vs. TypeDB

System Properties Comparison Couchbase vs. JanusGraph vs. SAP Adaptive Server vs. TigerGraph vs. TypeDB

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

A Graph DBMS optimized for distributed clusters

It was forked from the latest code base of Titan in January 2017

The SAP (Sybase) Adaptive Server Enterprise (ASE) is an enterprise-class RDBMS

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

TypeDB is a strongly-typed database with a rich and logical type system and TypeQL as its query language

Primary database model

Graph DBMS
Relational DBMS

Often described as a 'hyper-relational' database, since it implements the 'Entity-Relationship Paradigm' to manage complex data structures and ontologies.

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS

with Boeing's Spatial Query Server

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	1.94
Rank	#129	Overall
	#12	Graph DBMS

Score	36.31
Rank	#25	Overall
	#17	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Score	0.65
Rank	#234	Overall
	#20	Graph DBMS
	#107	Relational DBMS

Website

www.couchbase.com

janusgraph.org

www.sap.com/products/technology-platform/sybase-ase.html

www.tigergraph.com

typedb.com

Technical documentation

docs.couchbase.com

docs.janusgraph.org

help.sap.com/docs/SAP_ASE

docs.tigergraph.com

typedb.com/docs

Developer

Couchbase, Inc.

Linux Foundation; originally developed as Titan by Aurelius

SAP, Sybase

Vaticle

Initial release

2011

2017

1987

2017

2016

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

0.6.3, February 2023

16.0

2.26.3, January 2024

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

Open Source

Apache 2.0

commercial

Open Source

GPL Version 3, commercial licenses 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, C++, Go and Erlang

Java

C and C++

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
OS X
Unix
Windows

AIX
HP-UX
Linux
Solaris
Unix
Windows

Linux

Linux
OS X
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

SQL-like query language (GSQL)

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

Java API
TinkerPop Blueprints
TinkerPop Frames
TinkerPop Gremlin
TinkerPop Rexster

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

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

gRPC protocol
TypeDB Console (shell)
TypeDB Studio (Visualisation software- previously TypeDB Workbase)

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

Clojure
Java
Python

C
C++
Cobol
Java
Java
Perl
PHP
Python

C++
Java

All JVM based languages
Groovy
Java
JavaScript (Node.js)
Python
Scala

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

Java and Transact-SQL

yes

Triggers

yes

via the TAP protocol

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

yes

depending on the used storage backend (e.g. Cassandra, HBase, BerkeleyDB)

horizontal partitioning

Sharding

by using Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

yes

Multi-source replication
Source-replica replication

Multi-source replication

by using Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Faunus, a graph analytics engine

yes

by using Apache Kafka and Apache Zookeeper

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Relationships in graphs

substituted by the relationship feature

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

Supports various storage backends: Cassandra, HBase, Berkeley DB, Akiban, Hazelcast

yes

In-memory capabilities

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

yes

Ephemeral buckets

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

User authentification and security via Rexster Graph Server

fine grained access rights according to SQL-standard

Role-based access control

yes

at REST API level; other APIs in progress

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Licensing and pricing models

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