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DBMS > ClickHouse vs. Drizzle vs. Faircom EDGE vs. Splice Machine

System Properties Comparison ClickHouse vs. Drizzle vs. Faircom EDGE vs. Splice Machine

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Editorial information provided by DB-Engines
NameClickHouse  Xexclude from comparisonDrizzle  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonSplice Machine  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.
DescriptionColumn-oriented Relational DBMS powering YandexMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSKey-value store
Relational DBMS
Relational DBMS
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score12.32
Rank#46  Overall
#30  Relational DBMS
Score0.13
Rank#304  Overall
#44  Key-value stores
#139  Relational DBMS
Score0.80
Rank#193  Overall
#91  Relational DBMS
Websiteclickhouse.techwww.faircom.com/­products/­faircomedge-iot-databasesplicemachine.com
Technical documentationclickhouse.tech/­docslearn.faircomcorp.com/­developers/­documentation_directorydoc.splicemachine.com
DeveloperClickhouse Inc.Drizzle project, originally started by Brian AkerFairCom CorporationSplice Machine
Initial release2016200819792014
Current releasev22.3.3.44-lts, April 20227.2.4, September 2012V3, October 20203.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoGNU GPLcommercial infoRestricted, free version availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC++C++ANSI C, C++Java
Server operating systemsFreeBSD
Linux
macOS
FreeBSD
Linux
OS X
Android
Linux infoARM, x86
Raspbian
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesFlexible Schema (defined schema, partial schema, schema free)yes
Typing infopredefined data types such as float or dateyesyesyes, ANSI Standard SQL Typesyes
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.noyes
Secondary indexesnoyesyesyes
SQL infoSupport of SQLClose to ANSI SQLyes infowith proprietary extensionsyes infoANSI SQL queriesyes
APIs and other access methodsHTTP REST
JDBC
ODBC
Proprietary protocol
JDBCADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
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
Scala info3rd party library
C
C++
Java
PHP
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnonoyes info.Net, JavaScript, C/C++yes infoJava
Triggersnono infohooks for callbacks inside the server can be used.yesyes
Partitioning methods infoMethods for storing different data on different nodesShardingFile partitioning infoCustomizable business rules for partitioningShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
yes infoSynchronous and asynchronous realtime replication based on transaction logsMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency
Tunable Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesyes infowhen using SQLyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoacross SQL and NoSQLyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlAccess rights for users and rolesPluggable authentication mechanisms infoe.g. LDAP, HTTPFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.Access rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
ClickHouseDrizzleFaircom EDGE infoformerly c-treeEDGESplice Machine
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ClickHouseDrizzleFaircom EDGE infoformerly c-treeEDGESplice Machine
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