DBMS > Apache Spark (SQL) vs. Vitess vs. VoltDB

System Properties Comparison Apache Spark (SQL) vs. Vitess vs. VoltDB

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Editorial information provided by DB-Engines

Name

Description

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

Scalable, distributed, cloud-native DBMS, extending MySQL

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	16.73
Rank	#35	Overall
	#20	Relational DBMS

Score	0.88
Rank	#200	Overall
	#92	Relational DBMS

Score	1.37
Rank	#156	Overall
	#72	Relational DBMS

Website

spark.apache.org/sql

vitess.io

www.voltdb.com

Technical documentation

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

vitess.io/docs

docs.voltdb.com

Developer

Apache Software Foundation

The Linux Foundation, PlanetScale

VoltDB Inc.

Initial release

2014

2013

2010

Current release

3.5.0 ( 2.13), September 2023

15.0.2, December 2022

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2.0, commercial licenses available

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Scala

Java, C++

Server operating systems

Linux
OS X
Windows

Docker
Linux
macOS

Linux
OS X

for development

Data scheme

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
MySQL protocol
ODBC

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Java
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

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency across shards
Immediate Consistency within a shard

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID at shard level

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

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

Users and roles with access to stored procedures

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More resources

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