DBMS > Amazon Redshift vs. IBM Db2 Event Store vs. mSQL vs. SingleStore

System Properties Comparison Amazon Redshift vs. IBM Db2 Event Store vs. mSQL vs. SingleStore

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Distributed Event Store optimized for Internet of Things use cases

mSQL (Mini SQL) is a simple and lightweight RDBMS

MySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type

Primary database model

Relational DBMS

Event Store
Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS
Time Series DBMS
Vector DBMS

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

Score	15.25
Rank	#38	Overall
	#23	Relational DBMS

Score	0.18
Rank	#315	Overall
	#2	Event Stores
	#26	Time Series DBMS

Score	1.26
Rank	#163	Overall
	#74	Relational DBMS

Score	4.02
Rank	#74	Overall
	#39	Relational DBMS

Website

aws.amazon.com/redshift

www.ibm.com/products/db2-event-store

hughestech.com.au/products/msql

www.singlestore.com

Technical documentation

docs.aws.amazon.com/redshift

www.ibm.com/docs/en/db2-event-store

docs.singlestore.com

Developer

Amazon (based on PostgreSQL)

IBM

Hughes Technologies

SingleStore Inc.

Initial release

2012

2017

1994

2013

Current release

2.0

4.4, October 2021

8.5, January 2024

License

Commercial or Open Source

commercial

free developer edition available

commercial

free licenses can be provided

commercial

free developer edition available

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

C++, Go

Server operating systems

hosted

Linux

Linux, macOS, Windows for the developer addition

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

Linux

64 bit version required

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

through the embedded Spark runtime

A subset of ANSI SQL is implemented

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

yes

but no triggers and foreign keys

APIs and other access methods

JDBC
ODBC

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

Cluster Management API

as HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Delphi
Java
Perl
PHP
Tcl

Bash
C
C#
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

hash partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Active-active shard replication

none

Source-replica replication

stores two copies of each physical data partition on two separate nodes

MapReduce

Offers an API for user-defined Map/Reduce methods

can define user-defined aggregate functions for map-reduce-style calculations

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes, multi-version concurrency control (MVCC)

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

All updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.

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

Fine grained access control via users, groups and roles

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