DBMS > LeanXcale vs. Microsoft SQL Server vs. Milvus vs. mSQL vs. Riak KV

System Properties Comparison LeanXcale vs. Microsoft SQL Server vs. Milvus vs. mSQL vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Microsofts flagship relational DBMS

A DBMS designed for efficient storage of vector data and vector similarity searches

mSQL (Mini SQL) is a simple and lightweight RDBMS

Distributed, fault tolerant key-value store

Primary database model

Key-value store
Relational DBMS

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.29
Rank	#291	Overall
	#41	Key-value stores
	#132	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

www.leanxcale.com

www.microsoft.com/en-us/sql-server

milvus.io

hughestech.com.au/products/msql

Technical documentation

learn.microsoft.com/en-US/sql/sql-server

milvus.io/docs/overview.md

www.tiot.jp/riak-docs/riak/kv/latest

Developer

LeanXcale

Microsoft

Hughes Technologies

OpenSource, formerly Basho Technologies

Initial release

2015

1989

2019

1994

2009

Current release

SQL Server 2022, November 2022

2.3.4, January 2024

4.4, October 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache Version 2.0

commercial

free licenses can be provided

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

Erlang

Server operating systems

Linux
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

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

Linux
OS X

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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.

yes

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

through Apache Derby

yes

A subset of ANSI SQL is implemented

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

APIs and other access methods

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

RESTful HTTP API

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

C
Java
Scala

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

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

C
C++
Delphi
Java
Perl
PHP
Tcl

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

none

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

none

Eventual Consistency

Foreign keys

Referential integrity

yes

links between data sets can be stored

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

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

yes, using Riak Security

More information provided by the system vendor

Specific characteristics

Milvus is an open-source and cloud-native vector database built for production-ready...
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Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
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Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
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Key customers

Milvus is trusted by thousands of enterprises, including PayPal, eBay, IKEA, LINE,...
» more

Market metrics

As of January 2024, 25k+ GitHub stars 10M+ downloads and installations 3k+ enterprise...
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Licensing and pricing models

Milvus was released under the open-source Apache License 2.0 in October 2019. Fully-managed...
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