DBMS > Amazon Redshift vs. BigObject vs. Brytlyt vs. KairosDB vs. Solr

System Properties Comparison Amazon Redshift vs. BigObject vs. Brytlyt vs. KairosDB vs. Solr

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

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

Distributed Time Series DBMS based on Cassandra or H2

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Time Series 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	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Score	42.91
Rank	#24	Overall
	#3	Search engines

Website

aws.amazon.com/redshift

bigobject.io

brytlyt.io

github.com/kairosdb/kairosdb

solr.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

docs.brytlyt.io

kairosdb.github.io

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Brytlyt

Apache Software Foundation

Initial release

2012

2015

2016

2013

2006

Current release

5.0, August 2023

1.2.2, November 2018

9.6.0, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

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

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Implementation language

C, C++ and CUDA

Java

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

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

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

yes

Secondary indexes

restricted

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

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

Graphite protocol
HTTP REST
Telnet API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Java
JavaScript

Node.js
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

Lua

user defined functions

in PL/pgSQL

Java plugins

Triggers

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

none

Source-replica replication

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

none

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

automatically between fact table and dimension tables

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

Read/write lock on objects (tables, trees)

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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