DBMS > SpaceTime vs. Splice Machine vs. STSdb vs. Vitess

System Properties Comparison SpaceTime vs. Splice Machine vs. STSdb vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Score	0.82
Rank	#209	Overall
	#97	Relational DBMS

Website

www.mireo.com/spacetime

splicemachine.com

github.com/STSSoft/STSdb4

vitess.io

Technical documentation

splicemachine.com/how-it-works

vitess.io/docs

Developer

Mireo

Splice Machine

STS Soft SC

The Linux Foundation, PlanetScale

Initial release

2020

2014

2011

2013

Current release

3.1, March 2021

4.0.8, September 2015

15.0.2, December 2022

License

Commercial or Open Source

commercial

Open Source

AGPL 3.0, commercial license available

Open Source

GPLv2, commercial license available

Open Source

Apache Version 2.0, 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++

Java

Server operating systems

Linux

Linux
OS X
Solaris
Windows

Windows

Docker
Linux
macOS

Data scheme

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

A subset of ANSI SQL is implemented

yes

with proprietary extensions

APIs and other access methods

RESTful HTTP API

JDBC
Native Spark Datasource
ODBC

.NET Client API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

C#
C++
Python

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

C#
Java

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

Java

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Fixed-grid hypercubes

Shared Nothhing Auto-Sharding, Columnar Partitioning

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Real-time block device replication (DRBD)

Multi-source replication
Source-replica replication

none

Multi-source replication
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

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

In-memory capabilities

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

yes

User concepts

Access control

yes

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

Users with fine-grained authorization concept

no user groups or roles

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