DBMS > BigchainDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SWC-DB vs. XTDB

System Properties Comparison BigchainDB vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Table Storage vs. SWC-DB vs. XTDB

Editorial information provided by DB-Engines

Name

Description

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Globally distributed, horizontally scalable, multi-model database service

A Wide Column Store for rapid development using massive semi-structured datasets

A high performance, scalable Wide Column DBMS

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Document store

Document store
Graph DBMS
Key-value store
Wide column store

Wide column store

Document store

Secondary database models

Spatial DBMS

Time Series DBMS

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

Score	0.85
Rank	#208	Overall
	#35	Document stores

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

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

www.bigchaindb.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/en-us/services/storage/tables

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

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

bigchaindb.readthedocs.io/en/latest

learn.microsoft.com/azure/cosmos-db

www.xtdb.com/docs

Developer

Microsoft

Alex Kashirin

Juxt Ltd.

Initial release

2016

2014

2012

2020

2019

Current release

0.5, April 2021

1.19, September 2021

License

Commercial or Open Source

Open Source

AGPL v3

commercial

Open Source

GPL V3

Open Source

MIT License

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Python

C++

Clojure

Server operating systems

Linux

hosted

Linux

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

yes, extensible-data-notation format

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

yes

SQL

Support of SQL

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

CLI Client
RESTful HTTP API

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

RESTful HTTP API

Proprietary protocol
Thrift

HTTP REST
JDBC

Supported programming languages

Go
Haskell
Java
JavaScript
Python
Ruby

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

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++

Clojure
Java

Server-side scripts

Stored procedures

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Implicit feature of the cloud service

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes,with MongoDB ord RethinkDB

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

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

User concepts

Access control

yes

Access rights can be defined down to the item level

Access rights based on private key authentication or shared access signatures

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