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DBMS > Apache Kylin vs. Faircom DB vs. Splice Machine

System Properties Comparison Apache Kylin vs. Faircom DB vs. Splice Machine

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Editorial information provided by DB-Engines
NameApache Kylin  Xexclude from comparisonFaircom DB infoformerly c-treeACE  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackNative high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.20
Rank#159  Overall
#78  Relational DBMS
Score0.46
Rank#244  Overall
#38  Key-value stores
#116  Relational DBMS
Score0.71
Rank#206  Overall
#102  Relational DBMS
Websitekylin.apache.orgwww.faircom.com/­products/­faircom-dbsplicemachine.com
Technical documentationkylin.apache.org/­docslearn.faircomcorp.com/­developers/­documentation_directorydoc.splicemachine.com
DeveloperApache Software Foundation, originally contributed from eBay IncFairCom CorporationSplice Machine
Initial release201519792014
Current release3.1.0, July 2020V12, November 20203.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoRestricted, free version availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaANSI C, C++Java
Server operating systemsLinuxAIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows infoeasily portable to other OSs
Linux
OS X
Solaris
Windows
Data schemeyesschema free, schema optional, schema required, partial schema,yes
Typing infopredefined data types such as float or dateyesyes, ANSI SQL Types, JSON, typed binary structuresyes
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.nono
Secondary indexesyesyesyes
SQL infoSupport of SQLANSI SQL for queries (using Apache Calcite)yes, ANSI SQL with proprietary extensionsyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes info.Net, JavaScript, C/C++yes infoJava
Triggersyesyes
Partitioning methods infoMethods for storing different data on different nodesFile partitioning, horizontal partitioning, sharding infoCustomizable business rules for table partitioningShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).Multi-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesnoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction
Immediate Consistency
Foreign keys infoReferential integrityyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datatunable from ACID to Eventually ConsistentACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesYes, tunable from durable to delayed durability to in-memoryyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes
User concepts infoAccess controlFine grained access rights according to SQL-standard with additional protections for filesAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Apache KylinFaircom DB infoformerly c-treeACESplice Machine
Specific characteristicsAchieve Hundreds of Thousands of Transactions Per Second on A Single Database Server...
» more
Competitive advantagesTop Features SQL Key-value store and navigational NoSQL API Indexed binary objects...
» more
Typical application scenariosReal-time transaction processing solutions leverage FairCom DB because its NoSQL...
» more
Key customersMicrosoft, UPS, Verizon, Motorola, Commvault, Thomson Reuters, ACI, BNY Mellon, Rockwell...
» more
Market metricsFrom banking and financial transactions, transportation, warehouse automation, package...
» more
Licensing and pricing modelsJust as the technical aspects of FairCom DB are flexible to meet operation requirements,...
» more
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Apache KylinFaircom DB infoformerly c-treeACESplice Machine
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