DBMS > Databend vs. HEAVY.AI vs. Kinetica vs. MySQL vs. Quasardb

System Properties Comparison Databend vs. HEAVY.AI vs. Kinetica vs. MySQL vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

An open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexity

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

Fully vectorized database across both GPUs and CPUs

Widely used open source RDBMS

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

Document store
Spatial DBMS

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

Score	0.34
Rank	#283	Overall
	#130	Relational DBMS

Score	1.64
Rank	#145	Overall
	#67	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Website

github.com/datafuselabs/databend
www.databend.com

github.com/heavyai/heavydb
www.heavy.ai

www.kinetica.com

www.mysql.com

quasar.ai

Technical documentation

Developer

Databend Labs

HEAVY.AI, Inc.

Kinetica

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

Initial release

2021

2016

2012

1995

2009

Current release

1.0.59, April 2023

5.10, January 2022

7.1, August 2021

8.4.0, April 2024

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Rust

C++ and CUDA

C, C++

C and C++

C++

Server operating systems

hosted
Linux
macOS

Linux

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

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.

yes

Secondary indexes

yes

with tags

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

with proprietary extensions

SQL-like query language

APIs and other access methods

CLI Client
JDBC
RESTful HTTP API

JDBC
ODBC
Thrift
Vega

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

Supported programming languages

Go
Java
JavaScript (Node.js)
Python
Rust

All languages supporting JDBC/ODBC/Thrift
Python

C++
Java
JavaScript (Node.js)
Python

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

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

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Round robin

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication

Source-replica replication

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

yes

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

GPU vRAM or System RAM

yes

Transient mode

User concepts

Access control

Users with fine-grained authorization concept, user roles

fine grained access rights according to SQL-standard

Access rights for users and roles on table level

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

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