DBMS > mSQL vs. SQLite vs. Vertica vs. XTDB

System Properties Comparison mSQL vs. SQLite vs. Vertica vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

mSQL (Mini SQL) is a simple and lightweight RDBMS

Widely used embeddable, in-process RDBMS

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Column oriented

Document store

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	1.26
Rank	#163	Overall
	#74	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Score	0.13
Rank	#330	Overall
	#45	Document stores

Website

hughestech.com.au/products/msql

www.sqlite.org

www.vertica.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

www.sqlite.org/docs.html

vertica.com/documentation

www.xtdb.com/docs

Developer

Hughes Technologies

Dwayne Richard Hipp

OpenText

previously Micro Focus and Hewlett Packard

Juxt Ltd.

Initial release

1994

2000

2005

2019

Current release

4.4, October 2021

3.46.1 (13 August 2024), August 2024

12.0.3, January 2023

1.19, September 2021

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

Public Domain

commercial

Limited community edition free

Open Source

MIT License

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Clojure

Server operating systems

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

server-less

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

dynamic column types

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

schema-free

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

yes

yes, extensible-data-notation format

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

No Indexes Required. Different internal optimization strategy, but same functionality included.

yes

SQL

Support of SQL

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

SQL-92 is not fully supported

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

HTTP REST
JDBC

Supported programming languages

C
C++
Delphi
Java
Perl
PHP
Tcl

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Clojure
Java

Server-side scripts

Stored procedures

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, hierarchical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

via file-system locks

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

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