DBMS > Datomic vs. Microsoft SQL Server vs. Spark SQL vs. TimescaleDB vs. Transbase

System Properties Comparison Datomic vs. Microsoft SQL Server vs. Spark SQL vs. TimescaleDB vs. Transbase

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

Microsofts flagship relational DBMS

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

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	4.46
Rank	#71	Overall
	#5	Time Series DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

www.datomic.com

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

spark.apache.org/sql

www.timescale.com

www.transaction.de/en/products/transbase.html

Technical documentation

docs.datomic.com

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

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

docs.timescale.com

www.transaction.de/en/products/transbase/features.html

Developer

Cognitect

Microsoft

Apache Software Foundation

Timescale

Transaction Software GmbH

Initial release

2012

1989

2014

2017

1987

Current release

1.0.7075, December 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

2.15.0, May 2024

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

restricted free version is available

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

free development license

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

C++

Scala

C and C++

Server operating systems

All OS with a Java VM

Linux
Windows

Linux
OS X
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

SQL-like DML and DDL statements

yes

full PostgreSQL SQL syntax

yes

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Clojure
Java

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

Java
Python
R
Scala

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

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

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

yes

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

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

yes, utilizing Spark Core

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes, but depending on the SQL-Server Edition

none

Source-replica replication with hot standby and reads on replicas

Source-replica replication

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

Transaction concepts

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

ACID

yes

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

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

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