DBMS > Apache Impala vs. mSQL vs. Prometheus vs. Transbase vs. Ultipa

System Properties Comparison Apache Impala vs. mSQL vs. Prometheus vs. Transbase vs. Ultipa

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

mSQL (Mini SQL) is a simple and lightweight RDBMS

Open-source Time Series DBMS and monitoring system

A resource-optimized, high-performance, universally applicable RDBMS

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.27
Rank	#169	Overall
	#76	Relational DBMS

Score	7.69
Rank	#50	Overall
	#3	Time Series DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

impala.apache.org

hughestech.com.au/products/msql

prometheus.io

www.transaction.de/en/products/transbase.html

www.ultipa.com

Technical documentation

impala.apache.org/impala-docs.html

prometheus.io/docs

www.transaction.de/en/products/transbase/features.html

www.ultipa.com/document

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Hughes Technologies

Transaction Software GmbH

Ultipa

Initial release

2013

1994

2015

1987

2019

Current release

4.1.0, June 2022

4.4, October 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free licenses can be provided

Open Source

Apache 2.0

commercial

free development license

commercial

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++

C and C++

Server operating systems

Linux

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

Linux
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

Numeric data only

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.

Import of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

A subset of ANSI SQL is implemented

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

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

C++
Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

yes

by Federation

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

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More resources

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