DBMS > atoti vs. Citus vs. Qdrant vs. Redis

System Properties Comparison atoti vs. Citus vs. Qdrant vs. Redis

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

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

Popular in-memory data platform used as a cache, message broker, and database that can be deployed on-premises, across clouds, and hybrid environments

Redis focuses on performance so most of its design decisions prioritize high performance and very low latencies.

Primary database model

Multiple data types and a rich set of operations, as well as configurable data expiration, eviction and persistence

Secondary database models

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

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

Score	0.45
Rank	#253	Overall
	#13	Object oriented DBMS

Score	2.13
Rank	#117	Overall
	#56	Relational DBMS

Score	1.53
Rank	#145	Overall
	#7	Vector DBMS

Score	149.43
Rank	#6	Overall
	#1	Key-value stores

Website

atoti.io

www.citusdata.com

github.com/qdrant/qdrant
qdrant.tech

redis.com
redis.io

Technical documentation

docs.atoti.io

docs.citusdata.com

qdrant.tech/documentation

docs.redis.com/latest/index.html
redis.io/docs

Developer

ActiveViam

Qdrant

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

Initial release

2010

2021

2009

Current release

8.1, December 2018

7.2.5, May 2024

License

Commercial or Open Source

commercial

free versions available

Open Source

AGPL, commercial license also available

Open Source

Apache Version 2.0

Open Source

source-available extensions (modules), commercial licenses for Redis Enterprise

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

Java

Rust

Server operating systems

Linux

Docker
Linux
macOS
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

Numbers, Strings, Geo, Boolean

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

yes

Keywords, numberic ranges, geo, full-text

yes

with RediSearch module

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

standard, with numerous extensions

with RediSQL module

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

gRPC
OpenAPI 3.0
RESTful HTTP/JSON API

OpenAPI 3.0

proprietary protocol

RESP - REdis Serialization Protocol

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

Server-side scripts

Stored procedures

Python

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Lua; Redis Functions coming in Redis 7 (slides and Github)

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Automatic hash-based sharding with support for hash-tags for manual sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

Collection-level replication

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

MapReduce

Offers an API for user-defined Map/Reduce methods

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency, tunable consistency

Eventual Consistency
Causal consistency can be enabled in Active-Active databases
Strong consistency with Redis Raft
Strong eventual consistency with Active-Active

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Atomic execution of command blocks and scripts and optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Key-based authentication

Access Control Lists (ACLs): redis.io/docs/management/security/acl
LDAP and Role-Based Access Control (RBAC) for Redis Enterprise
Mutual TLS authentication: redis.io/docs/management/security/encryption
Password-based authentication

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