DBMS > Microsoft Azure Cosmos DB vs. NSDb vs. Riak KV vs. Weaviate

System Properties Comparison Microsoft Azure Cosmos DB vs. NSDb vs. Riak KV vs. Weaviate

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Distributed, fault tolerant key-value store

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Time Series DBMS

Key-value store

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

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	24.97
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Score	1.48
Rank	#149	Overall
	#8	Vector DBMS

Website

azure.microsoft.com/services/cosmos-db

nsdb.io

github.com/weaviate/weaviate
weaviate.io

Technical documentation

learn.microsoft.com/azure/cosmos-db

nsdb.io/Architecture

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

weaviate.io/developers/weaviate

Developer

Microsoft

OpenSource, formerly Basho Technologies

Weaviate B.V.

Initial release

2014

2017

2009

2019

Current release

3.2.0, December 2022

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2, commercial enterprise edition

Open Source

commercial license available with Weaviate Enterprise

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.

Implementation language

Java, Scala

Erlang

Server operating systems

hosted

Linux
macOS

Linux
OS X

Data scheme

schema-free

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

JSON types

yes: int, bigint, decimal, string

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

All properties auto-indexed by default

all fields are automatically indexed

restricted

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

SQL-like query language

GraphQL is used as query language

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

gRPC
HTTP REST
WebSocket

HTTP API
Native Erlang Interface

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

Java
Scala

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

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

JavaScript

Erlang

Triggers

JavaScript

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

no "single point of failure"

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

Foreign keys

Referential integrity

links between data sets can be stored

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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 can be defined down to the item level

yes, using Riak Security

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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