DBMS > BoltDB vs. Microsoft Azure Cosmos DB vs. Qdrant vs. RethinkDB vs. SwayDB

System Properties Comparison BoltDB vs. Microsoft Azure Cosmos DB vs. Qdrant vs. RethinkDB vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Globally distributed, horizontally scalable, multi-model database service

A high-performance vector database with neural network or semantic-based matching

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Key-value store

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Document store

Key-value store

Secondary database models

Spatial DBMS

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

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	2.74
Rank	#105	Overall
	#19	Document stores

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

github.com/boltdb/bolt

azure.microsoft.com/services/cosmos-db

github.com/qdrant/qdrant
qdrant.tech

rethinkdb.com

swaydb.simer.au

Technical documentation

learn.microsoft.com/azure/cosmos-db

qdrant.tech/documentation

rethinkdb.com/docs

Developer

Microsoft

Qdrant

The Linux Foundation

since July 2017

Simer Plaha

Initial release

2013

2014

2021

2009

2018

Current release

2.4.1, August 2020

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2.0

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

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.

Implementation language

Rust

C++

Scala

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Docker
Linux
macOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Numbers, Strings, Geo, Boolean

yes

string, binary, float, bool, date, geometry

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

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

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

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
Python
Rust

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Java
Kotlin
Scala

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

range based

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Collection-level replication

Source-replica replication

none

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

none

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Multi-item ACID transactions with snapshot isolation within a partition

Atomic single-document operations

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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 can be defined down to the item level

Key-based authentication

yes

users and table-level permissions

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