DBMS > Couchbase vs. Geode vs. Splice Machine vs. STSdb vs. TerminusDB

System Properties Comparison Couchbase vs. Geode vs. Splice Machine vs. STSdb vs. TerminusDB

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

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

Primary database model

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

Document store
RDF store

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

Score	16.59
Rank	#36	Overall
	#5	Document stores

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

www.couchbase.com

geode.apache.org

splicemachine.com

github.com/STSSoft/STSdb4

terminusdb.com

Technical documentation

docs.couchbase.com

geode.apache.org/docs

splicemachine.com/how-it-works

terminusdb.github.io/terminusdb/#

Developer

Couchbase, Inc.

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Splice Machine

STS Soft SC

DataChemist Ltd.

Initial release

2011

2002

2014

2011

2018

Current release

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

1.1, February 2017

3.1, March 2021

4.0.8, September 2015

11.0.0, January 2023

License

Commercial or Open Source

Open Source

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

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

Open Source

GPL V3

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

Prolog, Rust

Server operating systems

Linux
OS X
Windows

All OS with a Java VM

the JDK (8 or later) is also required

Linux
OS X
Solaris
Windows

Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

SQL-like query language (OQL)

yes

SQL-like query language (WOQL)

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 Client API
Memcached protocol
RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

.NET Client API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

C#
Java

JavaScript
Python

Server-side scripts

Stored procedures

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

user defined functions

yes

Java

yes

Triggers

yes

via the TAP protocol

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Graph Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Multi-source replication

Multi-source replication
Source-replica replication

none

Journaling Streams

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes, on a single node

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory journaling

In-memory capabilities

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

yes

Ephemeral buckets

yes

User concepts

Access control

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

Access rights per client and object definable

Access rights for users, groups and roles according to SQL-standard

Role-based access control

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