DBMS > Amazon Redshift vs. BigObject vs. Realm vs. Solr vs. SwayDB

System Properties Comparison Amazon Redshift vs. BigObject vs. Realm vs. Solr vs. SwayDB

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

A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Search engine

Key-value store

Secondary database models

Spatial DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	41.02
Rank	#24	Overall
	#3	Search engines

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

realm.io/docs

solr.apache.org/resources.html

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

Simer Plaha

Initial release

2012

2015

2014

2006

2018

Current release

9.6.1, May 2024

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache Version 2

Open Source

GNU Affero GPL V3.0

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

Scala

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)

Android
Backend: server-less
iOS
Windows

All OS with a Java VM

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

Data scheme

yes

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

schema-free

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

SQL-like DML and DDL statements

Solr Parallel SQL Interface

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

Java API
RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java

with Android only
Objective-C
React Native
Swift

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

runs within the applications so server-side scripts are unnecessary

Java plugins

Triggers

yes

Change Listeners

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

none

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

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

optimistic locking

Atomic execution of operations

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

In-Memory realm

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

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