DBMS > Hawkular Metrics vs. Microsoft Access vs. Oracle Berkeley DB vs. RocksDB vs. Teradata Aster

System Properties Comparison Hawkular Metrics vs. Microsoft Access vs. Oracle Berkeley DB vs. RocksDB vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Widely used in-process key-value store

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Platform for big data analytics on multistructured data sources and types

Primary database model

Time Series DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Key-value store

Relational DBMS

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

Score	0.08
Rank	#366	Overall
	#39	Time Series DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

www.hawkular.org

www.microsoft.com/en-us/microsoft-365/access

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

rocksdb.org

Technical documentation

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

developer.microsoft.com/en-us/access

docs.oracle.com/cd/E17076_05/html/index.html

github.com/facebook/rocksdb/wiki

Developer

Community supported by Red Hat

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Facebook, Inc.

Teradata

Initial release

2014

1992

1994

2013

2005

Current release

1902 (16.0.11328.20222), March 2019

18.1.40, May 2020

9.2.1, May 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

commercial license available

Open Source

BSD

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

C, Java, C++ (depending on the Berkeley DB edition)

C++

Server operating systems

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

Linux

Data scheme

schema-free

yes

schema-free

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

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

only with the Berkeley DB XML edition

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

yes

SQL interfaced based on SQLite is available

yes

APIs and other access methods

HTTP REST

ADO.NET
DAO
ODBC
OLE DB

C++ API
Java API

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

Go
Java
Python
Ruby

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

C
C++
Go
Java
Perl
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

R packages

Triggers

yes

via Hawkular Alerting

yes

since Access 2010 using the ACE-engine

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

based on Cassandra

none

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

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

ACID

but no files for transaction logging

ACID

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

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