DBMS > Badger vs. EventStoreDB vs. Microsoft Azure Cosmos DB vs. Qdrant

System Properties Comparison Badger vs. EventStoreDB vs. Microsoft Azure Cosmos DB vs. Qdrant

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Editorial information provided by DB-Engines

Name

Description

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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Globally distributed, horizontally scalable, multi-model database service

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

Primary database model

Key-value store

Event Store

Document store
Graph DBMS
Key-value store
Wide column store

Vector DBMS

Secondary database models

Spatial DBMS

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

Score	0.14
Rank	#331	Overall
	#49	Key-value stores

Score	1.10
Rank	#179	Overall
	#1	Event 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

Website

github.com/dgraph-io/badger

www.eventstore.com

azure.microsoft.com/services/cosmos-db

github.com/qdrant/qdrant
qdrant.tech

Technical documentation

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

developers.eventstore.com

learn.microsoft.com/azure/cosmos-db

qdrant.tech/documentation

Developer

DGraph Labs

Event Store Limited

Microsoft

Qdrant

Initial release

2017

2012

2014

2021

Current release

21.2, February 2021

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

commercial

Open Source

Apache Version 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.

Implementation language

Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Linux
Windows

hosted

Docker
Linux
macOS
Windows

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

Numbers, Strings, Geo, Boolean

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

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

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Collection-level replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Foreign keys

Referential integrity

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

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

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