DBMS > FatDB vs. InfluxDB vs. Machbase Neo vs. MariaDB vs. Spark SQL

System Properties Comparison FatDB vs. InfluxDB vs. Machbase Neo vs. MariaDB vs. Spark SQL

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

DBMS for storing time series, events and metrics

TimeSeries DBMS for AIoT and BigData

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Document store
Key-value store

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

Document store
Graph DBMS

with OQGraph storage engine
Spatial DBMS

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

Score	26.56
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.22
Rank	#324	Overall
	#29	Time Series DBMS

Score	93.81
Rank	#13	Overall
	#9	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.influxdata.com/products/influxdb-overview

machbase.com

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

spark.apache.org/sql

Technical documentation

docs.influxdata.com/influxdb

machbase.com/dbms

mariadb.com/kb/en/library

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

FatCloud

Machbase

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Apache Software Foundation

Initial release

2012

2013

2009

Fork of MySQL, which was first released in 1995

2014

Current release

2.7.5, January 2024

V8.0, August 2023

11.3.2, February 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

MIT-License; commercial enterprise version available

commercial

free test version available

Open Source

GPL version 2, commercial enterprise subscription available

Open Source

Apache 2.0

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++

Scala

Server operating systems

Windows

Linux
OS X

through Homebrew

Linux
macOS
Windows

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

Dynamic columns are supported

yes

Typing

predefined data types such as float or date

yes

Numeric data and Strings

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

Via inetgration in SQL Server

SQL-like query language

yes

with proprietary extensions

SQL-like DML and DDL statements

APIs and other access methods

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

HTTP API
JSON over UDP

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

via applications

yes

PL/SQL compatibility added with version 10.3

Triggers

yes

via applications

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

in enterprise version only

Sharding

several options for horizontal partitioning and Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

in enterprise version only

selectable replication factor

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

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

yes

Depending on used storage engine

yes

volatile and lookup table

yes

with MEMORY storage engine

User concepts

Access control

Can implement custom security layer via applications

simple rights management via user accounts

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

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Market metrics

Fastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
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Licensing and pricing models

Open source core with closed source clustering available either on-premise or on...
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