DBMS > Axibase vs. Blueflood vs. Google Cloud Spanner vs. Microsoft SQL Server vs. SWC-DB

System Properties Comparison Axibase vs. Blueflood vs. Google Cloud Spanner vs. Microsoft SQL Server vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Scalable TimeSeries DBMS based on Cassandra

A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

Primary database model

Time Series DBMS

Relational DBMS

Wide column store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

Score	0.29
Rank	#292	Overall
	#25	Time Series DBMS

Score	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	2.89
Rank	#103	Overall
	#52	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

axibase.com/docs/atsd/finance

blueflood.io

cloud.google.com/spanner

www.microsoft.com/en-us/sql-server

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

Technical documentation

github.com/rax-maas/blueflood/wiki

cloud.google.com/spanner/docs

learn.microsoft.com/en-US/sql/sql-server

Developer

Axibase Corporation

Rackspace

Google

Microsoft

Alex Kashirin

Initial release

2013

2017

1989

2020

Current release

15585

SQL Server 2022, November 2022

0.5, April 2021

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Apache 2.0

commercial

restricted free version is available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

Linux

Linux
OS X

hosted

Linux
Windows

Linux

Data scheme

yes

predefined scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

Query statements complying to ANSI 2011

yes

SQL-like query language

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

HTTP REST

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

Proprietary protocol
Thrift

Supported programming languages

Go
Java
PHP
Python
R
Ruby

Go
Java
JavaScript (Node.js)
Python

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C++

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

Sharding

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

selectable replication factor

based on Cassandra

Multi-source replication with 3 replicas for regional instances.

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

yes

Transaction concepts

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

ACID

Strict serializable isolation

ACID

Concurrency

Support for concurrent manipulation of data

yes

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 based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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