DBMS > Dolt vs. Redis vs. TDengine

System Properties Comparison Dolt vs. Redis vs. TDengine

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Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

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.

Time Series DBMS and big data platform

Primary database model

Relational DBMS

Key-value store

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

Time Series DBMS

Secondary database models

Document store

with RedisJSON
Graph DBMS

with RedisGraph
Spatial DBMS
Search engine

with RediSearch
Time Series DBMS

with RedisTimeSeries
Vector DBMS

Relational DBMS

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

Score	0.69
Rank	#219	Overall
	#101	Relational DBMS

Score	153.36
Rank	#7	Overall
	#1	Key-value stores

Score	1.96
Rank	#117	Overall
	#9	Time Series DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

redis.com
redis.io

github.com/taosdata/TDengine
tdengine.com

Technical documentation

docs.dolthub.com

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

docs.tdengine.com

Developer

DoltHub Inc

Redis project core team, inspired by Salvatore Sanfilippo

Development sponsored by Redis Inc.

TDEngine, previously Taos Data

Initial release

2018

2009

2019

Current release

7.2.5, May 2024

3.0, August 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

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

Open Source

AGPL V3, also commercial editions 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

Server operating systems

Linux
macOS
Windows

BSD
Linux
OS X
Windows

ported and maintained by Microsoft Open Technologies, Inc.

Linux
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

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

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

with RediSearch module

SQL

Support of SQL

yes

with RediSQL module

Standard SQL with extensions for time-series applications

APIs and other access methods

CLI Client
HTTP REST

proprietary protocol

RESP - REdis Serialization Protocol

JDBC
RESTful HTTP API

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust

Server-side scripts

Stored procedures

yes

currently in alpha release

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

Triggers

yes

publish/subscribe channels provide some trigger functionality; RedisGears

yes, via alarm monitoring

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication

with Redis Enterprise Pack
Source-replica replication

Chained replication is supported

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

through RedisGears

Consistency concepts

Methods to ensure consistency in a distributed system

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

Data access is serialized by the server

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

Only one user is configurable, and must be specified in the config file at startup

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

yes

More information provided by the system vendor

Specific characteristics

TDengine™ is a time-series database designed to help traditional industries overcome...
» more

Competitive advantages

High Performance at Any Scale: With its distributed scalable architecture that grows...
» more

Typical application scenarios

TDengine is purpose-built for Industry 4.0 and the Industrial IoT (IIoT) and particularly...
» more

Market metrics

TDengine has garnered over 23,000 stars on GitHub and is used in over 50 countries...
» more

Licensing and pricing models

TDengine OSS is free, open-source software released under the AGPLv3. TDengine Enterprise...
» more

News

TDengine’s Stream Processing in IoT: Typical Applications in Connected Cars
20 January 2025

TDengine + Manufacturing: How to Achieve Efficient OPC UA Data Integration
17 January 2025

Accelerating Digital Transformation: The Critical Role of Time-Series Databases
10 January 2025

Time-Series Database: Cost-Effective Solution for Modernizing Data Operations in Manufacturing
9 January 2025

TDengine 3.3.5.0 Release Notes
8 January 2025

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