DBMS > ClickHouse vs. FeatureBase vs. Graph Engine vs. Sphinx vs. SWC-DB

System Properties Comparison ClickHouse vs. FeatureBase vs. Graph Engine vs. Sphinx vs. SWC-DB

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.

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

A high performance, scalable Wide Column DBMS

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Search engine

Wide column store

Secondary database models

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	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	5.95
Rank	#55	Overall
	#5	Search engines

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

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

Technical documentation

clickhouse.com/docs

docs.featurebase.com

www.graphengine.io/docs/manual

sphinxsearch.com/docs

Developer

Clickhouse Inc.

Molecula and Pilosa Open Source Contributors

Microsoft

Sphinx Technologies Inc.

Alex Kashirin

Initial release

2016

2017

2010

2001

2020

Current release

v24.4.1.2088-stable, May 2024

2022, May 2022

3.5.1, February 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

MIT License

Open Source

GPL version 2, commercial licence available

Open Source

GPL V3

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.

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.
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.

Implementation language

C++

.NET and C

C++

Server operating systems

FreeBSD
Linux
macOS

Linux
macOS

.NET

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

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 query language (SphinxQL)

SQL-like query language

APIs and other access methods

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

gRPC
JDBC
Kafka Connector
ODBC

RESTful HTTP API

Proprietary protocol

Proprietary protocol
Thrift

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#
C++
F#
Visual Basic

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

C++

Server-side scripts

Stored procedures

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

key based and custom

Sharding

horizontal partitioning

Sharding

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

yes

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

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 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.

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