DBMS > MonetDB vs. Splice Machine vs. Tarantool vs. Transbase

System Properties Comparison MonetDB vs. Splice Machine vs. Tarantool vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

A relational database management system that stores data in columns

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

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

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Spatial DBMS

with Tarantool/GIS extension

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

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

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

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.monetdb.org

splicemachine.com

www.tarantool.io

www.transaction.de/en/products/transbase.html

Technical documentation

www.monetdb.org/Documentation

splicemachine.com/how-it-works

www.tarantool.io/en/doc

www.transaction.de/en/products/transbase/features.html

Developer

MonetDB BV

Splice Machine

Transaction Software GmbH

Initial release

2004

2014

2008

1987

Current release

Dec2023 (11.49), December 2023

3.1, March 2021

2.10.0, May 2022

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Mozilla Public License 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

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

commercial

free development 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

Java

C and C++

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Solaris
Windows

BSD
Linux
macOS

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

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

yes

Typing

predefined data types such as float or date

yes

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

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

yes

SQL 2003 with some extensions

yes

Full-featured ANSI SQL support

yes

APIs and other access methods

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

JDBC
Native Spark Datasource
ODBC

Open binary protocol

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

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

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

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes, in SQL, C, R

yes

Java

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 via remote tables

Shared Nothhing Auto-Sharding, Columnar Partitioning

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication available in experimental status

Multi-source replication
Source-replica replication

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark 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

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

yes

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes, cooperative multitasking

yes

Durability

Support for making data persistent

yes

yes, write ahead logging

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

fine grained access rights according to SQL-standard

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

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

fine grained access rights according to SQL-standard

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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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