DBMS > Amazon Redshift vs. Derby vs. Hawkular Metrics vs. Solr vs. Transbase

System Properties Comparison Amazon Redshift vs. Derby vs. Hawkular Metrics vs. Solr vs. Transbase

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

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

A widely used distributed, scalable search engine based on Apache Lucene

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Secondary database models

Spatial DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

aws.amazon.com/redshift

db.apache.org/derby

www.hawkular.org

solr.apache.org

www.transaction.de/en/products/transbase.html

Technical documentation

docs.aws.amazon.com/redshift

db.apache.org/derby/manuals/index.html

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

solr.apache.org/resources.html

www.transaction.de/en/products/transbase/features.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Community supported by Red Hat

Apache Software Foundation

Transaction Software GmbH

Initial release

2012

1997

2014

2006

1987

Current release

10.17.1.0, November 2023

9.6.0, April 2024

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

Open Source

Apache Version 2

commercial

free development 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

Java

C and C++

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

yes

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

Secondary indexes

restricted

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Solr Parallel SQL Interface

yes

APIs and other access methods

JDBC
ODBC

JDBC

HTTP REST

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

All languages supporting JDBC/ODBC

Java

Go
Java
Python
Ruby

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

Java plugins

yes

Triggers

yes

via Hawkular Alerting

yes

User configurable commands triggered on index changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

based on Cassandra

yes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

optimistic locking

yes

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

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

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