DBMS > Kdb vs. MySQL vs. PlanetScale vs. Rockset vs. SpatiaLite

System Properties Comparison Kdb vs. MySQL vs. PlanetScale vs. Rockset vs. SpatiaLite

Editorial information provided by DB-Engines

Name

Description

High performance Time Series DBMS

Widely used open source RDBMS

Scalable, distributed, serverless MySQL database platform built on top of Vitess

A scalable, reliable search and analytics service in the cloud, built on RocksDB

Spatial extension of SQLite

Primary database model

Time Series DBMS
Vector DBMS

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store

Spatial DBMS

Secondary database models

Relational DBMS

Document store
Spatial DBMS

Relational DBMS
Search engine

Relational DBMS

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

Score	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1.59
Rank	#151	Overall
	#70	Relational DBMS

Score	0.79
Rank	#211	Overall
	#35	Document stores

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Kx Systems, a division of First Derivatives plc

Oracle

since 2010, originally MySQL AB, then Sun

PlanetScale

Rockset

Alessandro Furieri

Initial release

2000

kdb was released 2000, kdb+ in 2003

1995

2020

2019

2008

Current release

3.6, May 2018

8.4.0, April 2024

5.0.0, August 2020

License

Commercial or Open Source

commercial

free 32-bit version

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

Cloud-based only

Only available as a cloud service

yes

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

C and C++

C++

Server operating systems

Linux
OS X
Solaris
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Docker
Linux
macOS

hosted

server-less

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

dynamic typing

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

ingestion from XML files supported

Secondary indexes

yes

table attribute 'grouped'

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language (q)

yes

with proprietary extensions

yes

with proprietary extensions

Read-only SQL queries, including JOINs

yes

APIs and other access methods

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
JDBC
MySQL protocol
ODBC

HTTP REST

Supported programming languages

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

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

Go
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

yes

proprietary syntax

yes

proprietary syntax

Triggers

yes

with views

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency across shards
Immediate Consistency within a shard

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID at shard level

ACID

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

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

Users with fine-grained authorization concept

no user groups or roles

Access rights for users and organizations can be defined via Rockset console

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
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Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Licensing and pricing models

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