DBMS > EXASOL vs. openGemini vs. Tarantool vs. TerminusDB vs. ToroDB

System Properties Comparison EXASOL vs. openGemini vs. Tarantool vs. TerminusDB vs. ToroDB

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

An open source distributed Time Series DBMS with high concurrency, high performance, and high scalability

In-memory computing platform with a flexible data schema for efficiently building high-performance applications

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

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Relational DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS

Document store

Secondary database models

Spatial DBMS

with Tarantool/GIS extension

Document store
RDF store

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

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	0.09
Rank	#361	Overall
	#37	Time Series DBMS

Score	1.67
Rank	#143	Overall
	#25	Document stores
	#25	Key-value stores
	#65	Relational DBMS

Score	0.23
Rank	#316	Overall
	#27	Graph DBMS

Website

www.exasol.com

www.opengemini.org
github.com/openGemini

www.tarantool.io

terminusdb.com

github.com/torodb/server

Technical documentation

www.exasol.com/resources

docs.opengemini.org/guide

www.tarantool.io/en/doc

terminusdb.github.io/terminusdb/#

Developer

Exasol

Huawei and openGemini community

DataChemist Ltd.

8Kdata

Initial release

2000

2022

2008

2018

2016

Current release

1.1, July 2023

2.10.0, May 2022

11.0.0, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

BSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise

Open Source

GPL V3

Open Source

AGPL-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 and C++

Prolog, Rust

Java

Server operating systems

Linux
Windows

BSD
Linux
macOS

Linux

All OS with a Java 7 VM

Data scheme

yes

schema-free

Flexible data schema: relational definition for tables with ability to store json-like documents in columns

yes

schema-free

Typing

predefined data types such as float or date

yes

Integer, Float, Boolean, String

string, double, decimal, uuid, integer, blob, boolean, datetime

yes

string, integer, double, boolean, date, object_id

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

Full-featured ANSI SQL support

SQL-like query language (WOQL)

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

HTTP REST

Open binary protocol

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

Supported programming languages

Java
Lua
Python
R

C
C++
Go
Java
JavaScript (Node.js)
Python
Rust

C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust

JavaScript
Python

Server-side scripts

Stored procedures

user defined functions

Lua, C and SQL stored procedures

yes

Triggers

yes

yes, before/after data modification events, on replication events, client session events

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.

Graph Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Asynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)

Journaling Streams

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Casual consistency across sharding partitions
Eventual consistency within replicaset partition

when using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition

when using Raft

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

ACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

yes

in-memory journaling

yes

In-memory capabilities

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

yes

yes, full featured in-memory storage engine with persistence

User concepts

Access control

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

Administrators and common users accounts

Access Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

Role-based access control

Access rights for users and roles

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More resources

DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021, Vladimir Perepelytsya, Tarantool (sponsor)

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