DBMS > ClickHouse vs. FeatureBase vs. Kinetica vs. MaxDB vs. Sphinx

System Properties Comparison ClickHouse vs. FeatureBase vs. Kinetica vs. MaxDB vs. Sphinx

Editorial information provided by DB-Engines

Name

Description

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.

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

Fully vectorized database across both GPUs and CPUs

A robust and reliable RDBMS optimized to run all major SAP solutions

Open source search engine for searching in data from different sources, e.g. relational databases

Primary database model

Relational DBMS

Search engine

Secondary database models

Time Series DBMS

Spatial DBMS
Time Series DBMS

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

Score	15.55
Rank	#38	Overall
	#23	Relational DBMS

Score	0.31
Rank	#292	Overall
	#135	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	5.95
Rank	#55	Overall
	#5	Search engines

Website

Technical documentation

clickhouse.com/docs

docs.featurebase.com

docs.kinetica.com

maxdb.sap.com/documentation

sphinxsearch.com/docs

Developer

Clickhouse Inc.

Molecula and Pilosa Open Source Contributors

Kinetica

SAP, acquired from Software AG (Adabas-D) in 1997

Sphinx Technologies Inc.

Initial release

2016

2017

2012

1984

2001

Current release

v24.4.2.141-stable, June 2024

2022, May 2022

7.1, August 2021

7.9.10.12, February 2024

3.5.1, February 2023

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Limited community edition free

Open Source

GPL version 2, commercial licence available

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

ClickHouse Cloud: Get the performance you love from open source ClickHouse in a serverless offering that takes care of the details so you can spend more time getting insight out of the fastest database on earth.
DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.

Implementation language

C++

C, C++

C++

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS

Linux

AIX
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

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

full-text index on all search fields

SQL

Support of SQL

Close to ANSI SQL (SQL/JSON + extensions)

SQL queries

SQL-like DML and DDL statements

yes

SQL-like query language (SphinxQL)

APIs and other access methods

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

gRPC
JDBC
Kafka Connector
ODBC

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

Proprietary protocol

Supported programming languages

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

Java
Python

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
Perl
PHP
Python

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

none

Sharding

Partitioning is done manually, search queries against distributed index is supported

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

yes

The original contents of fields are not stored in the Sphinx index.

In-memory capabilities

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

yes

GPU vRAM or System RAM

User concepts

Access control

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 and roles on table level

fine grained access rights according to SQL-standard

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