DBMS > Badger vs. Qdrant vs. Yaacomo

System Properties Comparison Badger vs. Qdrant vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

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

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Key-value store

Vector DBMS

Relational DBMS

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

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

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Website

github.com/dgraph-io/badger

github.com/qdrant/qdrant
qdrant.tech

yaacomo.com

Technical documentation

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

qdrant.tech/documentation

Developer

DGraph Labs

Qdrant

Q2WEB GmbH

Initial release

2017

2021

2009

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Rust

Server operating systems

BSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS
Windows

Android
Linux
Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

Numbers, Strings, Geo, Boolean

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

yes

Keywords, numberic ranges, geo, full-text

yes

SQL

Support of SQL

yes

APIs and other access methods

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

JDBC
ODBC

Supported programming languages

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

Server-side scripts

Stored procedures

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

Collection-level replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency, tunable consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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

Key-based authentication

fine grained access rights according to SQL-standard

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More resources

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