DBMS > Blueflood vs. Splice Machine vs. SWC-DB vs. Vitess

System Properties Comparison Blueflood vs. Splice Machine vs. SWC-DB vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

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

A high performance, scalable Wide Column DBMS

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Secondary database models

Time Series DBMS

Document store
Spatial DBMS

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

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.54
Rank	#252	Overall
	#115	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.88
Rank	#203	Overall
	#95	Relational DBMS

Website

blueflood.io

splicemachine.com

github.com/kashirin-alex/swc-db
www.swcdb.org

vitess.io

Technical documentation

github.com/rax-maas/blueflood/wiki

splicemachine.com/how-it-works

vitess.io/docs

Developer

Rackspace

Splice Machine

Alex Kashirin

The Linux Foundation, PlanetScale

Initial release

2013

2014

2020

2013

Current release

3.1, March 2021

0.5, April 2021

15.0.2, December 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

AGPL 3.0, commercial license available

Open Source

GPL V3

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

Java

C++

Server operating systems

Linux
OS X

Linux
OS X
Solaris
Windows

Linux

Docker
Linux
macOS

Data scheme

predefined scheme

yes

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

yes

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

HTTP REST

JDBC
Native Spark Datasource
ODBC

Proprietary protocol
Thrift

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

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

C++

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

Sharding

based on Cassandra

Shared Nothhing Auto-Sharding, Columnar Partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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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