DBMS > Cachelot.io vs. DolphinDB vs. FatDB vs. GreptimeDB vs. Quasardb

System Properties Comparison Cachelot.io vs. DolphinDB vs. FatDB vs. GreptimeDB vs. Quasardb

Editorial information provided by DB-Engines

Name

FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the ranking.

Description

In-memory caching system

DolphinDB is a high performance Time Series DBMS. It is integrated with an easy-to-use fully featured programming language and a high-volume high-velocity streaming analytics system. It offers operational simplicity, scalability, fault tolerance, and concurrency.

A .NET NoSQL DBMS that can integrate with and extend SQL Server.

An open source Time Series DBMS built for increased scalability, high performance and efficiency

Distributed, high-performance timeseries database

Primary database model

Key-value store

Time Series DBMS

Document store
Key-value store

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	0.00
Rank	#383	Overall
	#60	Key-value stores

Score	4.13
Rank	#80	Overall
	#6	Time Series DBMS

Score	0.06
Rank	#352	Overall
	#33	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

Technical documentation

docs.dolphindb.cn/en/help200/index.html

docs.greptime.com

doc.quasar.ai/master

Developer

DolphinDB, Inc

FatCloud

Greptime Inc.

quasardb

Initial release

2015

2018

2012

2022

2009

Current release

v2.00.4, January 2022

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Simplified BSD License

commercial

free community version available

commercial

Open Source

Apache Version 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

FreeBSD
Linux
OS X

Linux
Windows

Windows

Android
Docker
FreeBSD
Linux
macOS
Windows

BSD
Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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 tags

SQL

Support of SQL

SQL-like query language

Via inetgration in SQL Server

yes

SQL-like query language

APIs and other access methods

Memcached protocol

JDBC
JSON over HTTP
Kafka
MQTT (Message Queue Telemetry Transport)
ODBC
OPC DA
OPC UA
RabbitMQ
WebSocket

.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings

gRPC
HTTP API
JDBC

HTTP API

Supported programming languages

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby

C#
C++
Go
Java
JavaScript
MatLab
Python
R
Rust

C++
Erlang
Go
Java
JavaScript

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

Server-side scripts

Stored procedures

yes

via applications

Python

Triggers

yes

via applications

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

selectable replication factor

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Administrators, Users, Groups

Can implement custom security layer via applications

Simple rights management via user accounts

Cryptographically strong user authentication and audit trail

More information provided by the system vendor

Specific characteristics

GreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more

Competitive advantages

- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more

Typical application scenarios

For IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more

Key customers

Greptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more

Market metrics

GreptimeDB has garnered global recognition by topping GitHub trends following its...
» more

Licensing and pricing models

GreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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