DBMS > Datomic vs. Faircom DB vs. Microsoft SQL Server vs. Spark SQL

System Properties Comparison Datomic vs. Faircom DB vs. Microsoft SQL Server vs. Spark SQL

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Microsofts flagship relational DBMS

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

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	0.24
Rank	#311	Overall
	#44	Key-value stores
	#140	Relational DBMS

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.datomic.com

www.faircom.com/products/faircom-db

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

spark.apache.org/sql

Technical documentation

docs.datomic.com

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

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

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

Developer

Cognitect

FairCom Corporation

Microsoft

Apache Software Foundation

Initial release

2012

1979

1989

2014

Current release

1.0.6735, June 2023

V12, November 2020

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

Restricted, free version available

commercial

restricted free version is available

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

ANSI C, C++

C++

Scala

Server operating systems

All OS with a Java VM

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

Linux
Windows

Linux
OS X
Windows

Data scheme

yes

schema free, schema optional, schema required, partial schema,

yes

Typing

predefined data types such as float or date

yes

yes, ANSI SQL Types, JSON, typed binary structures

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

yes, ANSI SQL with proprietary extensions

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

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

JDBC
ODBC

Supported programming languages

Clojure
Java

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

yes

.Net, JavaScript, C/C++

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

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

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

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes, but depending on the SQL-Server Edition

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
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable from ACID to Eventually Consistent

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

Yes, tunable from durable to delayed durability to in-memory

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Fine grained access rights according to SQL-standard with additional protections for files

fine grained access rights according to SQL-standard

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