DBMS > BoltDB vs. Firebase Realtime Database vs. Hive vs. Microsoft Azure Cosmos DB vs. SWC-DB

System Properties Comparison BoltDB vs. Firebase Realtime Database vs. Hive vs. Microsoft Azure Cosmos DB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Globally distributed, horizontally scalable, multi-model database service

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	0.80
Rank	#215	Overall
	#31	Key-value stores

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

github.com/boltdb/bolt

firebase.google.com/products/realtime-database

hive.apache.org

azure.microsoft.com/services/cosmos-db

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

firebase.google.com/docs/database

cwiki.apache.org/confluence/display/Hive/Home

learn.microsoft.com/azure/cosmos-db

Developer

Google

acquired by Google 2014

Apache Software Foundation

initially developed by Facebook

Microsoft

Alex Kashirin

Initial release

2013

2012

2014

2020

Current release

3.1.3, April 2022

0.5, April 2021

License

Commercial or Open Source

Open Source

MIT License

commercial

Open Source

Apache Version 2

commercial

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

All OS with a Java VM

hosted

Linux

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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 properties auto-indexed by default

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

JDBC
ODBC
Thrift

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

Proprietary protocol
Thrift

Supported programming languages

Java
JavaScript
Objective-C

C++
Java
PHP
Python

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C++

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

user defined functions and integration of map-reduce

JavaScript

Triggers

Callbacks are triggered when data changes

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

Multi-item ACID transactions with snapshot isolation within a partition

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.

User concepts

Access control

yes, based on authentication and database rules

Access rights for users, groups and roles

Access rights can be defined down to the item level

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