DBMS > Amazon Redshift vs. Apache Impala vs. CouchDB vs. Milvus vs. Newts

System Properties Comparison Amazon Redshift vs. Apache Impala vs. CouchDB vs. Milvus vs. Newts

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for Hadoop

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

Time Series DBMS based on Cassandra

Primary database model

Secondary database models

Document store

Spatial DBMS

using the Geocouch extension

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

Score	9.30
Rank	#45	Overall
	#7	Document stores

Score	2.31
Rank	#113	Overall
	#3	Vector DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

aws.amazon.com/redshift

impala.apache.org

couchdb.apache.org

milvus.io

opennms.github.io/newts

Technical documentation

docs.aws.amazon.com/redshift

impala.apache.org/impala-docs.html

docs.couchdb.org/en/stable

milvus.io/docs/overview.md

github.com/OpenNMS/newts/wiki

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

OpenNMS Group

Initial release

2012

2013

2005

2019

2014

Current release

4.1.0, June 2022

3.3.3, December 2023

2.3.4, January 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

Open Source

Apache version 2

Open Source

Apache Version 2.0

Open Source

Apache 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.

Zilliz Cloud – Cloud-native service for Milvus

Implementation language

C++

Erlang

C++, Go

Java

Server operating systems

hosted

Linux

Android
BSD
Linux
OS X
Solaris
Windows

Linux
macOS

10.14 or later
Windows

with WSL 2 enabled

Linux
OS X
Windows

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

restricted

yes

via views

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

RESTful HTTP/JSON API

RESTful HTTP API

HTTP REST
Java API

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

Java

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

improved architecture with release 2.0

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

Multi-source replication
Source-replica replication

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Eventual Consistency
Immediate Consistency
Session Consistency
Tunable Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

atomic operations within a single document possible

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights for users can be defined per database

Role based access control and fine grained access rights

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...
» more

Licensing and pricing models

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