DBMS > Amazon Redshift vs. Brytlyt vs. Prometheus vs. Sequoiadb vs. Solr

System Properties Comparison Amazon Redshift vs. Brytlyt vs. Prometheus vs. Sequoiadb vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Open-source Time Series DBMS and monitoring system

NewSQL database with distributed OLTP and SQL

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

Primary database model

Relational DBMS

Time Series DBMS

Document store
Relational DBMS

Search engine

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	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.45
Rank	#261	Overall
	#41	Document stores
	#122	Relational DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

docs.brytlyt.io

prometheus.io/docs

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

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

Brytlyt

Sequoiadb Ltd.

Apache Software Foundation

Initial release

2012

2016

2015

2013

2006

Current release

5.0, August 2023

9.6.0, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

Open Source

Server: AGPL; Client: Apache V2

Open Source

Apache Version 2

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++ and CUDA

C++

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux
Windows

Linux

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

Numeric data only

yes

oid, date, timestamp, binary, regex

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

specific XML-type available, but no XML query functionality.

Import of XML data possible

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like query language

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP/JSON API

proprietary protocol using JSON

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

.Net
C++
Java
PHP
Python

.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

user defined functions

in PL/pgSQL

JavaScript

Java plugins

Triggers

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

by Federation

Source-replica replication

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

none

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

Document is locked during a transaction

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

simple password-based access control

yes

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