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

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

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.

A distributed reliable key-value store

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

Primary database model

Secondary database models

Spatial DBMS

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Trend Chart

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	0.00
Rank	#379	Overall
	#40	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

db.apache.org/derby

etcd.io
github.com/etcd-io/etcd

www.hawkular.org

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

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

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

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

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

Community supported by Red Hat

Apache Software Foundation

Initial release

2012

1997

2014

2006

Current release

10.17.1.0, November 2023

3.4, August 2019

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache Version 2

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

Server operating systems

hosted

All OS with a Java VM

FreeBSD
Linux
Windows

experimental

Linux
OS X
Windows

All OS with a Java VM

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

Data scheme

yes

schema-free

yes

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

Typing

predefined data types such as float or date

yes

supports customizable data types and automatic typing

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

JDBC

gRPC
JSON over HTTP

HTTP REST

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

Java

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

Go
Java
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in Python

Java Stored Procedures

Java plugins

Triggers

yes

yes, watching key changes

yes

via Hawkular Alerting

yes

User configurable commands triggered on index changes

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

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

selectable replication factor

based on Cassandra

yes

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

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

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

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