DBMS > MySQL vs. Spark SQL vs. SQream DB vs. XTDB

System Properties Comparison MySQL vs. Spark SQL vs. SQream DB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Widely used open source RDBMS

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

a GPU-based, columnar RDBMS for big data analytics workloads

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

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

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

www.mysql.com

spark.apache.org/sql

sqream.com

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

dev.mysql.com/doc

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

docs.sqream.com

www.xtdb.com/docs

Developer

Oracle

since 2010, originally MySQL AB, then Sun

Apache Software Foundation

SQream Technologies

Juxt Ltd.

Initial release

1995

2014

2017

2019

Current release

8.4.0, April 2024

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

1.19, September 2021

License

Commercial or Open Source

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

Apache 2.0

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

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Implementation language

C and C++

Scala

C++, CUDA, Haskell, Java, Scala

Clojure

Server operating systems

FreeBSD
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes, extensible-data-notation format

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

with proprietary extensions

SQL-like DML and DDL statements

yes

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

.Net
JDBC
ODBC

HTTP REST
JDBC

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

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Clojure
Java

Server-side scripts

Stored procedures

yes

proprietary syntax

user defined functions in Python

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes, utilizing Spark Core

horizontal and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

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

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

yes

User concepts

Access control

Users with fine-grained authorization concept

no user groups or roles

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