DBMS > Bangdb vs. ClickHouse vs. CouchDB vs. Microsoft Azure Table Storage

System Properties Comparison Bangdb vs. ClickHouse vs. CouchDB vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

A high-performance, column-oriented SQL DBMS for online analytical processing (OLAP) that uses all available system resources to their full potential to process each analytical query as fast as possible. It is available as both an open-source software and a cloud offering.

A native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.

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

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Document store

Wide column store

Secondary database models

Spatial DBMS

Time Series DBMS

Spatial DBMS

using the Geocouch extension

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

bangdb.com

clickhouse.com

couchdb.apache.org

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

Technical documentation

docs.bangdb.com

clickhouse.com/docs

docs.couchdb.org/en/stable

Developer

Sachin Sinha, BangDB

Clickhouse Inc.

Apache Software Foundation

Apache top-level project, originally developed by Damien Katz, a former Lotus Notes developer

Microsoft

Initial release

2012

2016

2005

2012

Current release

BangDB 2.0, October 2021

v24.4.1.2088-stable, May 2024

3.3.3, December 2023

License

Commercial or Open Source

Open Source

BSD 3

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

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.

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Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C, C++

C++

Erlang

Server operating systems

Linux

FreeBSD
Linux
macOS

Android
BSD
Linux
OS X
Solaris
Windows

hosted

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

via views

SQL

Support of SQL

SQL like support with command line tool

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

Proprietary protocol
RESTful HTTP API

gRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol

RESTful HTTP/JSON API

RESTful HTTP API

Supported programming languages

C
C#
C++
Java
Python

3rd party library
C++
Elixir

3rd party library
Go

3rd party library
Java

3rd party library
JavaScript (Node.js)

3rd party library
Kotlin

3rd party library
Nim

3rd party library
Perl

3rd party library
PHP

3rd party library
Python

3rd party library
R

3rd party library
Ruby

3rd party library
Rust
Scala

3rd party library

C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk

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

Server-side scripts

Stored procedures

yes

View functions in JavaScript

Triggers

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

key based and custom

Sharding

improved architecture with release 2.0

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Asynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.

Multi-source replication
Source-replica replication

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

atomic operations within a single document possible

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

strategy: optimistic locking

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Access rights for users and roles. Column and row based policies. Quotas and resource limits. Pluggable authentication with LDAP and Kerberos. Password based, X.509 certificate, and SSH key authentication.

Access rights for users can be defined per database

Access rights based on private key authentication or shared access signatures

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