DBMS > Apache Phoenix vs. ClickHouse vs. CouchDB vs. InfinityDB vs. Microsoft Azure Table Storage

System Properties Comparison Apache Phoenix vs. ClickHouse vs. CouchDB vs. InfinityDB vs. Microsoft Azure Table Storage

Editorial information provided by DB-Engines

Name

Description

A scale-out RDBMS with evolutionary schema built on Apache HBase

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 Java embedded Key-Value Store which extends the Java Map interface

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

Primary database model

Secondary database models

Time Series DBMS

Spatial DBMS

using the Geocouch extension

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

Score	2.06
Rank	#123	Overall
	#58	Relational DBMS

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	8.30
Rank	#47	Overall
	#7	Document stores

Score	0.08
Rank	#365	Overall
	#55	Key-value stores

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Website

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

Technical documentation

phoenix.apache.org

clickhouse.com/docs

docs.couchdb.org/en/stable

boilerbay.com/infinitydb/manual

Developer

Apache Software Foundation

Clickhouse Inc.

Apache Software Foundation

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

Boiler Bay Inc.

Microsoft

Initial release

2014

2016

2005

2002

2012

Current release

5.0-HBase2, July 2018 and 4.15-HBase1, December 2019

v24.4.2.141-stable, June 2024

3.3.3, December 2023

4.0

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

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Database as a Service

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Implementation language

Java

C++

Erlang

Java

Server operating systems

Linux
Unix
Windows

FreeBSD
Linux
macOS

Android
BSD
Linux
OS X
Solaris
Windows

All OS with a Java VM

hosted

Data scheme

yes

late-bound, schema-on-read capabilities

yes

schema-free

yes

nested virtual Java Maps, multi-value, logical â€˜tuple spaceâ€™ runtime Schema upgrade

schema-free

Typing

predefined data types such as float or date

yes

all Java primitives, Date, CLOB, BLOB, huge sparse arrays

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

via views

manual creation possible, using inversions based on multi-value capability

SQL

Support of SQL

yes

Close to ANSI SQL (SQL/JSON + extensions)

APIs and other access methods

JDBC

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

RESTful HTTP/JSON API

Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/docs/ItemSpaceDataStructures.htm)

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Groovy
Java
PHP
Python
Scala

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

Java

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

Server-side scripts

Stored procedures

user defined functions

yes

View functions in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

key based and custom

Sharding

improved architecture with release 2.0

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

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

Multi-source replication
Source-replica replication

none

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

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency

Eventual Consistency

Immediate Consistency

READ-COMMITTED or SERIALIZED

Immediate Consistency

Foreign keys

Referential integrity

manual creation possible, using inversions based on multi-value capability

Transaction concepts

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

ACID

atomic operations within a single document possible

ACID

Optimistic locking for transactions; no isolation for bulk loads

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes

strategy: optimistic locking

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 (using HBase ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC, multi-tenancy

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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