DBMS > Dolt vs. Infobright vs. Linter vs. Spark SQL

System Properties Comparison Dolt vs. Infobright vs. Linter vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

RDBMS for high security requirements

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

Primary database model

Relational DBMS

Secondary database models

Document store

Spatial DBMS

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

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	1.02
Rank	#192	Overall
	#90	Relational DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

ignitetech.com/softwarelibrary/infobrightdb

linter.ru

spark.apache.org/sql

Technical documentation

docs.dolthub.com

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

Developer

DoltHub Inc

Ignite Technologies Inc.; formerly InfoBright Inc.

relex.ru

Apache Software Foundation

Initial release

2018

2005

1990

2014

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

commercial

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

Linux
macOS
Windows

Linux
Windows

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
OS X
Windows

Data scheme

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

Knowledge Grid Technology used instead

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
HTTP REST

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

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

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

Only one user is configurable, and must be specified in the config file at startup

fine grained access rights according to SQL-standard

exploiting MySQL or PostgreSQL frontend capabilities

fine grained access rights according to SQL-standard

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More resources

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