DBMS > Microsoft Azure Synapse Analytics vs. Milvus vs. Percona Server for MongoDB vs. Riak KV

System Properties Comparison Microsoft Azure Synapse Analytics vs. Milvus vs. Percona Server for MongoDB vs. Riak KV

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Editorial information provided by DB-Engines

Name

Description

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Distributed, fault tolerant key-value store

Primary database model

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

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

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.52
Rank	#254	Overall
	#39	Document stores

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

Website

azure.microsoft.com/services/synapse-analytics

milvus.io

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

docs.microsoft.com/azure/synapse-analytics

milvus.io/docs/overview.md

docs.percona.com/percona-distribution-for-mongodb

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

Developer

Microsoft

Percona

OpenSource, formerly Basho Technologies

Initial release

2016

2019

2015

2009

Current release

2.3.4, January 2024

3.4.10-2.10, November 2017

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

GPL Version 2

Open Source

Apache version 2, commercial enterprise edition

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

C++, Go

C++

Erlang

Server operating systems

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux

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.

Secondary indexes

yes

restricted

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

proprietary protocol using JSON

HTTP API
Native Erlang Interface

Supported programming languages

C#
Java
PHP

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

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

JavaScript

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

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

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

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

via In-Memory Engine

User concepts

Access control

yes

Role based access control and fine grained access rights

Access rights for users and roles

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...
» more

Competitive advantages

Highly available, versatile, and robust with millisecond latency. Supports batch...
» more

Typical application scenarios

RAG: retrieval augmented generation Video media : video understanding, video deduplication....
» more

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...
» more

Licensing and pricing models

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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