DBMS > Badger vs. Microsoft Azure Table Storage vs. Milvus vs. Newts vs. SiteWhere

System Properties Comparison Badger vs. Microsoft Azure Table Storage vs. Milvus vs. Newts vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

A Wide Column Store for rapid development using massive semi-structured datasets

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

Time Series DBMS based on Cassandra

M2M integration platform for persisting/querying time series data

Primary database model

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	2.78
Rank	#103	Overall
	#3	Vector DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

github.com/dgraph-io/badger

azure.microsoft.com/en-us/services/storage/tables

milvus.io

opennms.github.io/newts

github.com/sitewhere/sitewhere

Technical documentation

godoc.org/github.com/dgraph-io/badger

milvus.io/docs/overview.md

github.com/OpenNMS/newts/wiki

sitewhere1.sitewhere.io/index.html

Developer

DGraph Labs

Microsoft

OpenNMS Group

SiteWhere

Initial release

2017

2012

2019

2014

2010

Current release

2.3.4, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Common Public Attribution License Version 1.0

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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++, Go

Java

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

Data scheme

schema-free

predefined scheme

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

SQL

Support of SQL

APIs and other access methods

RESTful HTTP API

HTTP REST
Java API

HTTP REST

Supported programming languages

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

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

Java

Server-side scripts

Stored procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

based on Cassandra

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

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

Access rights based on private key authentication or shared access signatures

Role based access control and fine grained access rights

Users with fine-grained authorization concept

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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

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More resources

DB-Engines blog posts

Vector databases
2 June 2023, Matthias Gelbmann

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