DBMS > BaseX vs. Google Cloud Datastore vs. mSQL vs. Postgres-XL vs. SwayDB

System Properties Comparison BaseX vs. Google Cloud Datastore vs. mSQL vs. Postgres-XL vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

mSQL (Mini SQL) is a simple and lightweight RDBMS

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Secondary database models

Document store
Spatial DBMS

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

Score	1.73
Rank	#142	Overall
	#4	Native XML DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.49
Rank	#256	Overall
	#117	Relational DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

basex.org

cloud.google.com/datastore

hughestech.com.au/products/msql

www.postgres-xl.org

swaydb.simer.au

Technical documentation

docs.basex.org

cloud.google.com/datastore/docs

www.postgres-xl.org/documentation

Developer

BaseX GmbH

Google

Hughes Technologies

Simer Plaha

Initial release

2007

2008

1994

2014

since 2012, originally named StormDB

2018

Current release

10.7, August 2023

4.4, October 2021

10 R1, October 2018

License

Commercial or Open Source

Open Source

BSD license

commercial

free licenses can be provided

Open Source

Mozilla public license

Open Source

GNU Affero GPL V3.0

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

Java

Scala

Server operating systems

Linux
OS X
Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
macOS

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

XQuery supports types

yes, details here

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

XML type, but no XML query functionality

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

yes

distributed, parallel query execution

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC

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

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Kotlin
Scala

Server-side scripts

Stored procedures

yes

using Google App Engine

user defined functions

Triggers

yes

via events

Callbacks using the Google Apps Engine

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

none

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

multiple readers, single writer

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

MVCC

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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 on 4 levels

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

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