DBMS > BigObject vs. Blueflood vs. NSDb vs. Realm vs. SiteWhere

System Properties Comparison BigObject vs. Blueflood vs. NSDb vs. Realm vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Scalable TimeSeries DBMS based on Cassandra

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

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

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Document store

Time Series DBMS

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

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.08
Rank	#369	Overall
	#40	Time Series DBMS

Score	7.41
Rank	#52	Overall
	#8	Document stores

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

github.com/sitewhere/sitewhere

Technical documentation

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

nsdb.io/Architecture

realm.io/docs

sitewhere1.sitewhere.io/index.html

Developer

BigObject, Inc.

Rackspace

Realm, acquired by MongoDB in May 2019

SiteWhere

Initial release

2015

2013

2017

2014

2010

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

Apache Version 2.0

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

Java, Scala

Java

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

Linux
OS X

Linux
macOS

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

yes

predefined scheme

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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.

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

HTTP REST

gRPC
HTTP REST
WebSocket

HTTP REST

Supported programming languages

Java
Scala

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

Lua

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

Sharding

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

In-Memory realm

User concepts

Access control

yes

Users with fine-grained authorization concept

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