DBMS > Dolt vs. MySQL vs. ScyllaDB vs. Sequoiadb vs. Spark SQL

System Properties Comparison Dolt vs. MySQL vs. ScyllaDB vs. Sequoiadb vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

Widely used open source RDBMS

Cassandra and DynamoDB compatible wide column store

NewSQL database with distributed OLTP and SQL

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Key/Value like access via memcached API

Wide column store

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store

Document store
Spatial DBMS

Key-value store

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

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	4.75
Rank	#68	Overall
	#5	Wide column stores

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

Technical documentation

docs.dolthub.com

dev.mysql.com/doc

docs.scylladb.com

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

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

DoltHub Inc

Oracle

since 2010, originally MySQL AB, then Sun

ScyllaDB

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2018

1995

2015

2013

2014

Current release

8.4.0, April 2024

ScyllaDB Open Source 5.4.1, January 2024

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Open Source (AGPL), commercial license 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)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

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Implementation language

C and C++

C++

Scala

Server operating systems

Linux
macOS
Windows

FreeBSD
Linux
OS X
Solaris
Windows

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

oid, date, timestamp, binary, regex

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

cluster global secondary indices

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements (CQL)

SQL-like query language

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
HTTP REST

ADO.NET
JDBC
ODBC
Proprietary native API

Proprietary protocol (CQL)

compatible with CQL (Cassandra Query Language, an SQL-like language)
RESTful HTTP API (DynamoDB compatible)
Thrift

proprietary protocol using JSON

JDBC
ODBC

Supported programming languages

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

For CQL interface: C#, C++, Clojure, Erlang, Go, Haskell, Java, JavaScript, Node.js, Perl, PHP, Python, Ruby, Rust, Scala
For DynamoDB interface: .Net, ColdFusion, Erlang, Groovy, Java, JavaScript, Perl, PHP, Python, Ruby

.Net
C++
Java
PHP
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

currently in alpha release

yes

proprietary syntax

yes, Lua

JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication
Source-replica replication

selectable replication factor

Representation of geographical distribution of servers is possible

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Tunable Consistency

can be individually decided for each write operation

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID

not for MyISAM storage engine

Atomicity and isolation are supported for single operations

Document is locked during a transaction

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

in-memory tables

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

Users with fine-grained authorization concept

no user groups or roles

Access rights for users can be defined per object

simple password-based access control

More information provided by the system vendor

Specific characteristics

ScyllaDB is engineered to deliver predictable performance at scale. It’s adopted...
» more

Competitive advantages

Highly-performant (efficiently utilizes full resources of a node and network; millions...
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Typical application scenarios

ScyllaDB is ideal for applications that require high throughput and low latency at...
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Key customers

Discord, Epic Games, Expedia, Zillow, Comcast, Disney+ Hotstar, Samsung, ShareChat,...
» more

Market metrics

ScyllaDB typically offers ~75% total cost of ownership savings, with ~5X higher throughput...
» more

Licensing and pricing models

ScyllaDB Open Source - free open source software (AGPL) ScyllaDB Enterprise - subscription-based...
» more

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