DBMS > BaseX vs. Hawkular Metrics vs. Microsoft Azure Table Storage vs. Sequoiadb vs. TimesTen

System Properties Comparison BaseX vs. Hawkular Metrics vs. Microsoft Azure Table Storage vs. Sequoiadb vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

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

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

A Wide Column Store for rapid development using massive semi-structured datasets

NewSQL database with distributed OLTP and SQL

In-Memory RDBMS compatible to Oracle

Primary database model

Native XML DBMS

Time Series DBMS

Wide column store

Document store
Relational DBMS

Relational DBMS

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

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.50
Rank	#258	Overall
	#41	Document stores
	#120	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

basex.org

www.hawkular.org

azure.microsoft.com/en-us/services/storage/tables

www.sequoiadb.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.basex.org

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

www.sequoiadb.com/en/index.php?m=Files&a=index

docs.oracle.com/database/timesten-18.1

Developer

BaseX GmbH

Community supported by Red Hat

Microsoft

Sequoiadb Ltd.

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2007

2014

2012

2013

1998

Current release

11.0, June 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

Open Source

BSD license

Open Source

Apache 2.0

commercial

Open Source

Server: AGPL; Client: Apache V2

commercial

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

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

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

XQuery supports types

yes

oid, date, timestamp, binary, regex

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

HTTP REST

RESTful HTTP API

proprietary protocol using JSON

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

Actionscript
C
C#
Haskell
Java
JavaScript

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

Go
Java
Python
Ruby

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

.Net
C++
Java
PHP
Python

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

JavaScript

PL/SQL

Triggers

yes

via events

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

multiple readers, single writer

optimistic locking

Document is locked during a transaction

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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 based on private key authentication or shared access signatures

simple password-based access control

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

XML Injection Attacks: What to Know About XPath, XQuery, XXE & More
18 May 2022, Hashed Out by The SSL Store™

9 Skills You Need to Become a Data Engineer
2 November 2022, KDnuggets

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Working with Azure to Use and Manage Data Lakes
7 March 2024, Simplilearn

How to use Azure Table storage in .Net
14 January 2019, InfoWorld

How to Use C# Azure.Data.Tables SDK with Azure Cosmos DB
9 July 2021, hackernoon.com

Inside Azure File Storage
7 October 2015, Microsoft

How to write data to Azure Table Store with an Azure Function
14 April 2017, Experts Exchange

provided by Google News

Share this page