DBMS > CrateDB vs. MySQL vs. Quasardb vs. Vitess

System Properties Comparison CrateDB vs. MySQL vs. Quasardb vs. Vitess

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Widely used open source RDBMS

Distributed, high-performance timeseries database

Scalable, distributed, cloud-native DBMS, extending MySQL

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Time Series DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

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

Score	0.69
Rank	#226	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#11	Vector DBMS

Score	1029.49
Rank	#2	Overall
	#2	Relational DBMS

Score	0.13
Rank	#332	Overall
	#28	Time Series DBMS

Score	0.86
Rank	#202	Overall
	#95	Relational DBMS

Website

Technical documentation

Developer

Crate

Oracle

since 2010, originally MySQL AB, then Sun

quasardb

The Linux Foundation, PlanetScale

Initial release

2013

1995

2009

2013

Current release

5.8.1, August 2024

9.0.0, July 2024

3.14.1, January 2024

15.0.2, December 2022

License

Commercial or Open Source

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

Open Source

Apache Version 2.0, commercial licenses 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

Java

C and C++

C++

Server operating systems

All Operating Systems, including Kubernetes with CrateDB Kubernetes Operator support

FreeBSD
Linux
OS X
Solaris
Windows

BSD
Linux
OS X
Windows

Docker
Linux
macOS

Data scheme

Flexible Schema (defined schema, partial schema, schema free)

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

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.

yes

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

with proprietary extensions

SQL-like query language

yes

with proprietary extensions

APIs and other access methods

ADO.NET
JDBC
ODBC
PostgreSQL wire protocol
Prometheus Remote Read/Write
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

HTTP API

ADO.NET
JDBC
MySQL protocol
ODBC

Supported programming languages

.NET
Erlang
Go

community maintained client
Java
JavaScript (Node.js)

community maintained client
Perl

community maintained client
PHP
Python
R
Ruby

community maintained client
Scala

community maintained client

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

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

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

proprietary syntax

yes

proprietary syntax

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Configurable replication on table/partition-level

Multi-source replication
Source-replica replication

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

not for MyISAM storage engine

Transaction concepts

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

unique row identifiers can be used for implementing an optimistic concurrency control strategy

ACID

not for MyISAM storage engine

ACID

ACID at shard level

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

table locks or row locks depending on storage engine

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

no user groups or roles

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