DBMS > Amazon Redshift vs. KeyDB vs. Manticore Search vs. Microsoft Azure SQL Database vs. Tkrzw

System Properties Comparison Amazon Redshift vs. KeyDB vs. Manticore Search vs. Microsoft Azure SQL Database vs. Tkrzw

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

Multi-storage database for search, including full-text search.

Database as a Service offering with high compatibility to Microsoft SQL Server

A concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto Cabinet

Primary database model

Secondary database models

Time Series DBMS

using the Manticore Columnar Library

Document store
Graph DBMS
Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.71
Rank	#226	Overall
	#33	Key-value stores

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Website

aws.amazon.com/redshift

github.com/Snapchat/KeyDB
keydb.dev

manticoresearch.com

azure.microsoft.com/en-us/products/azure-sql/database

dbmx.net/tkrzw

Technical documentation

docs.aws.amazon.com/redshift

docs.keydb.dev

manual.manticoresearch.com

docs.microsoft.com/en-us/azure/azure-sql

Developer

Amazon (based on PostgreSQL)

EQ Alpha Technology Ltd.

Manticore Software

Microsoft

Mikio Hirabayashi

Initial release

2012

2019

2017

2010

2020

Current release

6.0, February 2023

V12

0.9.3, August 2020

License

Commercial or Open Source

commercial

Open Source

BSD-3

Open Source

GPL version 2

commercial

Open Source

Apache Version 2.0

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++

Server operating systems

hosted

Linux

FreeBSD
Linux
macOS
Windows

hosted

Linux
macOS

Data scheme

yes

schema-free

Fixed schema

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

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.

Can index from XML

yes

Secondary indexes

restricted

yes

by using the Redis Search module

yes

full-text index on all search fields

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

yes

APIs and other access methods

JDBC
ODBC

Proprietary protocol

RESP - REdis Serialization Protoco

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

ADO.NET
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

Synchronous replication based on Galera library

yes, with always 3 replicas available

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

yes

The original contents of fields are not stored in the Manticore index.

yes

In-memory capabilities

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

yes

using specific database classes

User concepts

Access control

fine grained access rights according to SQL-standard

simple password-based access control and ACL

fine grained access rights according to SQL-standard

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