DBMS > Microsoft Azure SQL Database vs. SpaceTime vs. Spark SQL vs. XTDB

System Properties Comparison Microsoft Azure SQL Database vs. SpaceTime vs. Spark SQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering with high compatibility to Microsoft SQL Server

SpaceTime is a spatio-temporal DBMS with a focus on performance.

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

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

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational DBMS

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

Score	76.78
Rank	#16	Overall
	#11	Relational DBMS

Score	0.03
Rank	#392	Overall
	#8	Spatial DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

azure.microsoft.com/en-us/products/azure-sql/database

www.mireo.com/spacetime

spark.apache.org/sql

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

Technical documentation

docs.microsoft.com/en-us/azure/azure-sql

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

www.xtdb.com/docs

Developer

Microsoft

Mireo

Apache Software Foundation

Juxt Ltd.

Initial release

2010

2020

2014

2019

Current release

V12

3.5.0 ( 2.13), September 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Scala

Clojure

Server operating systems

hosted

Linux

Linux
OS X
Windows

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, 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

A subset of ANSI SQL is implemented

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP API

JDBC
ODBC

HTTP REST
JDBC

Supported programming languages

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

C#
C++
Python

Java
Python
R
Scala

Clojure
Java

Server-side scripts

Stored procedures

Transact SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Fixed-grid hypercubes

yes, utilizing Spark Core

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes, with always 3 replicas available

Real-time block device replication (DRBD)

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

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

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.

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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