DBMS > Amazon DynamoDB vs. Hive vs. Milvus vs. Prometheus vs. Quasardb

System Properties Comparison Amazon DynamoDB vs. Hive vs. Milvus vs. Prometheus vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

Primary database model

Document store
Key-value store

Relational DBMS

Vector DBMS

Time Series DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

aws.amazon.com/dynamodb

Technical documentation

docs.aws.amazon.com/dynamodb

cwiki.apache.org/confluence/display/Hive/Home

milvus.io/docs/overview.md

prometheus.io/docs

doc.quasar.ai/master

Developer

Amazon

Apache Software Foundation

initially developed by Facebook

quasardb

Initial release

2012

2019

2015

2009

Current release

3.1.3, April 2022

2.3.4, January 2024

3.14.1, January 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

Java

C++, Go

C++

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

Numeric data only

yes

integer and binary

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.

Import of XML data possible

Secondary indexes

yes

with tags

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
ODBC
Thrift

RESTful HTTP API

RESTful HTTP/JSON API

HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

C++
Java
PHP
Python

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

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

by integration with AWS Lambda

Partitioning methods

Methods for storing different data on different nodes

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

by Federation

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

query execution via MapReduce

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles

Role based access control and fine grained access rights

Cryptographically strong user authentication and audit trail

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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