DBMS > DataFS vs. LevelDB vs. Qdrant vs. TerarkDB

System Properties Comparison DataFS vs. LevelDB vs. Qdrant vs. TerarkDB

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Embeddable fast key-value storage library that provides an ordered mapping from string keys to string values

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

A key-value store forked from RocksDB with advanced compression algorithms. It can be used standalone or as a storage engine for MySQL and MongoDB

Primary database model

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	2.25
Rank	#115	Overall
	#19	Key-value stores

Score	1.28
Rank	#167	Overall
	#8	Vector DBMS

Score	0.08
Rank	#367	Overall
	#56	Key-value stores

Website

newdatabase.com

github.com/google/leveldb

github.com/qdrant/qdrant
qdrant.tech

github.com/bytedance/terarkdb

Technical documentation

dev.mobiland.com/Overview.xsp

github.com/google/leveldb/blob/main/doc/index.md

qdrant.tech/documentation

bytedance.larkoffice.com/docs/doccnZmYFqHBm06BbvYgjsHHcKc

Developer

Mobiland AG

Google

Qdrant

ByteDance, originally Terark

Initial release

2018

2011

2021

2016

Current release

1.1.263, October 2022

1.23, February 2021

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

Apache Version 2.0

commercial

restricted open source version available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Rust

C++

Server operating systems

Windows

Illumos
Linux
OS X
Windows

Docker
Linux
macOS
Windows

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

Typing

predefined data types such as float or date

yes

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

Keywords, numberic ranges, geo, full-text

SQL

Support of SQL

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

C++ API
Java API

Supported programming languages

.Net
C
C#
C++
VB.Net

C++
Go
Java

3rd party binding
JavaScript (Node.js)

3rd party binding
Python

3rd party binding

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

C++
Java

Server-side scripts

Stored procedures

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Collection-level replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable 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

with automatic compression on writes

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

Windows-Profile

Key-based authentication

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