DBMS > Datomic vs. Qdrant vs. RocksDB

System Properties Comparison Datomic vs. Qdrant vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Vector DBMS

Key-value store

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	1.16
Rank	#175	Overall
	#6	Vector DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

www.datomic.com

github.com/qdrant/qdrant
qdrant.tech

rocksdb.org

Technical documentation

docs.datomic.com

qdrant.tech/documentation

github.com/facebook/rocksdb/wiki

Developer

Cognitect

Qdrant

Facebook, Inc.

Initial release

2012

2021

2013

Current release

1.0.6735, June 2023

8.11.4, April 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2.0

Open Source

BSD

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Rust

C++

Server operating systems

All OS with a Java VM

Docker
Linux
macOS
Windows

Linux

Data scheme

yes

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

RESTful HTTP API

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

C++ API
Java API

Supported programming languages

Clojure
Java

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

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

yes

Transaction Functions

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Collection-level replication

yes

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

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

Key-based authentication

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