DBMS > BigObject vs. BoltDB vs. HBase vs. Microsoft Azure Cosmos DB vs. SWC-DB

System Properties Comparison BigObject vs. BoltDB vs. HBase vs. Microsoft Azure Cosmos DB vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

An embedded key-value store for Go.

Wide-column store based on Apache Hadoop and on concepts of BigTable

Globally distributed, horizontally scalable, multi-model database service

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Key-value store

Wide column store

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	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	0.74
Rank	#220	Overall
	#31	Key-value stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

bigobject.io

github.com/boltdb/bolt

hbase.apache.org

azure.microsoft.com/services/cosmos-db

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

Technical documentation

docs.bigobject.io

hbase.apache.org/book.html

learn.microsoft.com/azure/cosmos-db

Developer

BigObject, Inc.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Alex Kashirin

Initial release

2015

2013

2008

2014

2020

Current release

2.3.4, January 2021

0.5, April 2021

License

Commercial or Open Source

commercial

free community edition available

Open Source

MIT License

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

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

BSD
Linux
OS X
Solaris
Windows

Linux
Unix
Windows

using Cygwin

hosted

Linux

Data scheme

yes

schema-free

schema-free, schema definition possible

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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

fluentd
ODBC
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

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

Proprietary protocol
Thrift

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

C++

Server-side scripts

Stored procedures

Lua

yes

Coprocessors in Java

JavaScript

Triggers

yes

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

Multi-source replication
Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency or Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session 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

yes

Single row ACID (across millions of columns)

Multi-item ACID transactions with snapshot isolation within a partition

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

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights can be defined down to the item level

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