DBMS > CrateDB vs. MySQL vs. Sequoiadb vs. TimescaleDB

System Properties Comparison CrateDB vs. MySQL vs. Sequoiadb vs. TimescaleDB

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Editorial information provided by DB-Engines

Name

Description

Distributed Database based on Lucene

Widely used open source RDBMS

NewSQL database with distributed OLTP and SQL

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Spatial DBMS
Search engine
Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Document store
Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

Relational DBMS

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

Score	0.71
Rank	#227	Overall
	#37	Document stores
	#5	Spatial DBMS
	#16	Search engines
	#19	Time Series DBMS
	#10	Vector DBMS

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Website

Technical documentation

cratedb.com/docs

dev.mysql.com/doc

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.timescale.com

Developer

Crate

Oracle

since 2010, originally MySQL AB, then Sun

Sequoiadb Ltd.

Timescale

Initial release

2013

1995

2013

2017

Current release

8.4.0, April 2024

2.15.0, May 2024

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

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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

Linux

Linux
OS X
Windows

Data scheme

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

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL

Support of SQL

yes, but no triggers and constraints, and PostgreSQL compatibility

yes

with proprietary extensions

SQL-like query language

yes

full PostgreSQL SQL syntax

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

proprietary protocol using JSON

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

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++
Java
PHP
Python

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

user defined functions (Javascript)

yes

proprietary syntax

JavaScript

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, across time and space (hash partitioning) attributes

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

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Read-after-write consistency on record level

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

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

Document is locked during a transaction

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

In-memory capabilities

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

yes

User concepts

Access control

rights management via user accounts

Users with fine-grained authorization concept

no user groups or roles

simple password-based access control

fine grained access rights according to SQL-standard

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