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DBMS > Informix vs. Lovefield vs. Splice Machine vs. Tkrzw vs. Yaacomo

System Properties Comparison Informix vs. Lovefield vs. Splice Machine vs. Tkrzw vs. Yaacomo

Editorial information provided by DB-Engines
NameInformix  Xexclude from comparisonLovefield  Xexclude from comparisonSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed from the DB-Engines ranking
DescriptionA secure embeddable database from IBM, positioned besides IBM Db2 as a relatively low-cost product optimized for OLTP and Internet of Things dataEmbeddable relational database for web apps written in pure JavaScriptOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and SparkA concept of libraries, allowing an application program to store and query key-value pairs in a file. Successor of Tokyo Cabinet and Kyoto CabinetOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelRelational DBMS infoSince Version 12.10 support for JSON/BSON datatypes compatible with MongoDBRelational DBMSRelational DBMSKey-value storeRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Time Series DBMS infowith Informix TimeSeries Extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score17.87
Rank#35  Overall
#22  Relational DBMS
Score0.29
Rank#293  Overall
#133  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Score0.00
Rank#383  Overall
#60  Key-value stores
Websitewww.ibm.com/­products/­informixgoogle.github.io/­lovefieldsplicemachine.comdbmx.net/­tkrzwyaacomo.com
Technical documentationinformix.hcldoc.com
www.ibm.com/­support/­knowledgecenter/­SSGU8G/­welcomeIfxServers.html
github.com/­google/­lovefield/­blob/­master/­docs/­spec_index.mdsplicemachine.com/­how-it-works
DeveloperIBM, HCL Technologies infoEffective May 1st, 2017, HCL took on development, technical support, and product management teams, and works jointly with IBM on product strategy, marketing, and sales.GoogleSplice MachineMikio HirabayashiQ2WEB GmbH
Initial release19842014201420202009
Current release14.10.FC5, November 20202.1.12, February 20173.1, March 20210.9.3, August 2020
License infoCommercial or Open Sourcecommercial infofree developer edition availableOpen Source infoApache 2.0Open Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++ and JavaJavaScriptJavaC++
Server operating systemsAIX
HP-UX
Linux
macOS
Solaris
Windows
server-less, requires a JavaScript environment (browser, Node.js) infotested with Chrome, Firefox, IE, SafariLinux
OS X
Solaris
Windows
Linux
macOS
Android
Linux
Windows
Data schemeyesyesyesschema-freeyes
Typing infopredefined data types such as float or dateyes infoSince Version 12.10 support for JSON/BSON datatypesyesyesnoyes
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.nonono
Secondary indexesyesyesyesyes
SQL infoSupport of SQLyesSQL-like query language infovia JavaScript builder patternyesnoyes
APIs and other access methodsJDBC
JSON API infoMongoDB compatible
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
JDBC
ODBC
Supported programming languages.Net
C
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
JavaScriptC#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyesnoyes infoJavano
TriggersyesUsing read-only observersyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardingnoneShared Nothhing Auto-Sharding, Columnar Partitioningnonehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication
Source-replica replication
noneMulti-source replication
Source-replica replication
noneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integrationnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesyesyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yesyes
Durability infoSupport for making data persistentyesyes, by using IndexedDB or the cloud service Firebase Realtime Databaseyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infousing MemoryDByesyes infousing specific database classesyes
User concepts infoAccess controlUsers with fine-grained authentication, authorization, and auditing controlsnoAccess rights for users, groups and roles according to SQL-standardnofine grained access rights according to SQL-standard

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More resources
InformixLovefieldSplice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto CabinetYaacomo
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