DB-EnginesExtremeDB: mitigate connectivity issues in a DBMSEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > ClickHouse vs. Microsoft Azure Data Explorer vs. RRDtool vs. Sphinx

System Properties Comparison ClickHouse vs. Microsoft Azure Data Explorer vs. RRDtool vs. Sphinx

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonRRDtool  Xexclude from comparisonSphinx  Xexclude from comparison
DescriptionA 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.Fully managed big data interactive analytics platformIndustry standard data logging and graphing tool for time series data. RRD is an acronym for round-robin database. infoThe data is stored in a circular buffer, thus the system storage footprint remains constant over time.Open source search engine for searching in data from different sources, e.g. relational databases
Primary database modelRelational DBMSRelational DBMS infocolumn orientedTime Series DBMSSearch engine
Secondary database modelsTime Series DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score16.31
Rank#37  Overall
#23  Relational DBMS
Score5.82
Rank#65  Overall
#37  Relational DBMS
Score2.47
Rank#120  Overall
#10  Time Series DBMS
Score6.06
Rank#61  Overall
#6  Search engines
Websiteclickhouse.comazure.microsoft.com/­services/­data-exploreross.oetiker.ch/­rrdtoolsphinxsearch.com
Technical documentationclickhouse.com/­docsdocs.microsoft.com/­en-us/­azure/­data-exploreross.oetiker.ch/­rrdtool/­docsphinxsearch.com/­docs
DeveloperClickhouse Inc.MicrosoftTobias OetikerSphinx Technologies Inc.
Initial release2016201919992001
Current releasev23.12.1.1368-stable, December 2023cloud service with continuous releases1.8.0, 20223.5.1, February 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoGPL V2 and FLOSSOpen Source infoGPL version 2, commercial licence available
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
  • Aiven for Clickhouse: Managed cloud data warehousing with high-speed analytics.
  • DoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
  • 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 languageC++C infoImplementations in Java (e.g. RRD4J) and C# availableC++
Server operating systemsFreeBSD
Linux
macOS
hostedHP-UX
Linux
FreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Data schemeyesFixed schema with schema-less datatypes (dynamic)yesyes
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesNumeric data onlyno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.noyesno infoExporting into and restoring from XML files possible
Secondary indexesyesall fields are automatically indexednoyes infofull-text index on all search fields
SQL infoSupport of SQLClose to ANSI SQL (SQL/JSON + extensions)Kusto Query Language (KQL), SQL subsetnoSQL-like query language (SphinxQL)
APIs and other access methodsgRPC
HTTP REST
JDBC
MySQL wire protocol
ODBC
PostgreSQL wire protocol
Proprietary protocol
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
in-process shared library
Pipes
Proprietary protocol
Supported programming languagesC# info3rd party library
C++
Elixir info3rd party library
Go info3rd party library
Java info3rd party library
JavaScript (Node.js) info3rd party library
Kotlin info3rd party library
Nim info3rd party library
Perl info3rd party library
PHP info3rd party library
Python info3rd party library
R info3rd party library
Ruby info3rd party library
Rust
Scala info3rd party library
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C infowith librrd library
C# infowith a different implementation of RRDTool
Java infowith a different implementation of RRDTool
JavaScript (Node.js) infowith a different implementation of RRDTool
Lua
Perl
PHP infowith a wrapper library
Python
Ruby
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, Rnono
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynono
Partitioning methods infoMethods for storing different data on different nodeskey based and customSharding infoImplicit feature of the cloud servicenoneSharding infoPartitioning is done manually, search queries against distributed index is supported
Replication methods infoMethods for redundantly storing data on multiple nodesAsynchronous and synchronous physical replication; geographically distributed replicas; support for object storages.yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.nonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
none
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononono
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoby using the rrdcached daemonyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlAccess 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.Azure Active Directory Authenticationnono

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services
3rd partiesDoubleCloud: Fully managed ClickHouse alongside best-in-class managed open-source services to build analytics at scale.
» more

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

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
ClickHouseMicrosoft Azure Data ExplorerRRDtoolSphinx
DB-Engines blog posts

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

show all

The DB-Engines ranking includes now search engines
4 February 2013, Paul Andlinger

show all

Recent citations in the news

Why Clickhouse Should Be Your Next Database
6 July 2023, The New Stack

ClickHouse Cloud & Amazon S3 Express One Zone: Making a blazing fast analytical database even faster | Amazon ...
28 November 2023, AWS Blog

What the Heck is Proton?
31 December 2023, hackernoon.com

ClickHouse Announces Launch of ClickPipes
26 September 2023, Datanami

Can LLMs Replace Data Analysts? Getting Answers Using SQL
22 December 2023, Towards Data Science

provided by Google News

Announcing General Availability to migrate Virtual Network injected Azure Data Explorer Cluster to Private Endpoints ...
5 February 2024, azure.microsoft.com

Microsoft Introduces Azure Integration Environments and Business Process Tracking in Public Preview
23 November 2023, InfoQ.com

What is Microsoft Fabric? A big tech stack for big data
9 February 2024, InfoWorld

Why the Azure community should start planning for Microsoft Fabric today
20 June 2023, MSDynamicsWorld

Data Explorer processes unlabeled visual data, boosting creation of production-ready AI models
19 April 2023, VentureBeat

provided by Google News

SQLi vulnerability in Cacti could lead to RCE (CVE-2023-51448)
9 January 2024, Help Net Security

Cacti Cross-Site-Scripting Vulnerability Let Attacker Poison Database
7 September 2023, CybersecurityNews

Critical IP spoofing bug patched in Cacti
15 December 2022, The Daily Swig

Cacti: Using Graphs to Monitor Networks and Devices
16 March 2011, Packt Hub

How to install Cacti SNMP Monitor on Ubuntu
24 November 2017, TechRepublic

provided by Google News

Switching From Sphinx to MkDocs Documentation — What Did I Gain and Lose
2 February 2024, Towards Data Science

Manticore is a Faster Alternative to Elasticsearch in C++
25 July 2022, hackernoon.com

Perplexity AI: From Its Use To Operation, Everything You Need To Know About Googles Newest Challenger
11 January 2024, Free Press Journal

The Pirate Bay was recently down for over a week due to a DDoS attack
29 October 2019, The Hacker News

How to Build 600+ Links in One Month
4 September 2020, Search Engine Journal

provided by Google News



Share this page

Featured Products

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

AllegroGraph logo

Graph Database Leader for AI Knowledge Graph Applications - The Most Secure Graph Database Available.
Free Download

Ontotext logo

GraphDB allows you to link diverse data, index it for semantic search and enrich it via text analysis to build big knowledge graphs. Get it free.

Present your product here