DBMS > Apache Spark (SQL) vs. Microsoft SQL Server vs. Oracle Coherence

System Properties Comparison Apache Spark (SQL) vs. Microsoft SQL Server vs. Oracle Coherence

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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

Microsofts flagship relational DBMS

Oracles in-memory data grid solution

Primary database model

Relational DBMS

Key-value store

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	20.40
Rank	#29	Overall
	#18	Relational DBMS

Score	785.01
Rank	#3	Overall
	#3	Relational DBMS

Score	1.66
Rank	#132	Overall
	#22	Key-value stores

Website

spark.apache.org/sql

www.microsoft.com/en-us/sql-server

www.oracle.com/java/coherence

Technical documentation

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

learn.microsoft.com/en-US/sql/sql-server

docs.oracle.com/en/middleware/standalone/coherence

Developer

Apache Software Foundation

Microsoft

Oracle

Initial release

2014

1989

2007

Current release

3.5.0 ( 2.13), September 2023

SQL Server 2022, November 2022

14.1, August 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Scala

C++

Java

Server operating systems

Linux
OS X
Windows

Linux
Windows

All OS with a Java VM

Data scheme

yes

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JCache
JPA
RESTful HTTP API

Supported programming languages

Java
Python
R
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

.Net
C++
Java

Server-side scripts

Stored procedures

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Triggers

yes

Live Events

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

yes, with selectable consistency level

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optionally

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

authentification to access the cache via certificates or http basic authentication

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