DBMS > BaseX vs. Google Cloud Datastore vs. Hawkular Metrics vs. MaxDB vs. STSdb

System Properties Comparison BaseX vs. Google Cloud Datastore vs. Hawkular Metrics vs. MaxDB vs. STSdb

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

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

A robust and reliable RDBMS optimized to run all major SAP solutions

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

Primary database model

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	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	2.32
Rank	#112	Overall
	#55	Relational DBMS

Score	0.04
Rank	#360	Overall
	#52	Key-value stores

Website

basex.org

cloud.google.com/datastore

www.hawkular.org

maxdb.sap.com

github.com/STSSoft/STSdb4

Technical documentation

docs.basex.org

cloud.google.com/datastore/docs

www.hawkular.org/hawkular-metrics/docs/user-guide

maxdb.sap.com/documentation

Developer

BaseX GmbH

Google

Community supported by Red Hat

SAP, acquired from Software AG (Adabas-D) in 1997

STS Soft SC

Initial release

2007

2008

2014

1984

2011

Current release

10.7, August 2023

7.9.10.12, February 2024

4.0.8, September 2015

License

Commercial or Open Source

Open Source

BSD license

commercial

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

GPLv2, commercial license available

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

C++

Server operating systems

Linux
OS X
Windows

hosted

Linux
OS X
Windows

AIX
HP-UX
Linux
Solaris
Windows

Windows

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

yes, details here

yes

primitive types and user defined types (classes)

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

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

HTTP REST

ADO.NET
JDBC
ODBC
WebDAV

.NET Client API

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

Go
Java
Python
Ruby

.Net
C#
Java
Perl
PHP
Python

C#
Java

Server-side scripts

Stored procedures

yes

using Google App Engine

yes

Triggers

yes

via events

Callbacks using the Google Apps Engine

yes

via Hawkular Alerting

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Multi-source replication using Paxos

selectable replication factor

based on Cassandra

Source-replica replication

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.

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

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.

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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