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DBMS > GigaSpaces vs. LeanXcale vs. Microsoft Azure Data Explorer vs. MonetDB vs. TimesTen

System Properties Comparison GigaSpaces vs. LeanXcale vs. Microsoft Azure Data Explorer vs. MonetDB vs. TimesTen

Editorial information provided by DB-Engines
NameGigaSpaces  Xexclude from comparisonLeanXcale  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonMonetDB  Xexclude from comparisonTimesTen  Xexclude from comparison
DescriptionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesFully managed big data interactive analytics platformA relational database management system that stores data in columnsIn-Memory RDBMS compatible to Oracle
Primary database modelDocument store
Object oriented DBMS infoValues are user defined objects
Key-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMSRelational DBMS
Secondary database modelsGraph DBMS
Search engine
Document 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
Document store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.72
Rank#145  Overall
#67  Relational DBMS
Score1.31
Rank#163  Overall
#74  Relational DBMS
Websitewww.gigaspaces.comwww.leanxcale.comazure.microsoft.com/­services/­data-explorerwww.monetdb.orgwww.oracle.com/­database/­technologies/­related/­timesten.html
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwww.monetdb.org/­Documentationdocs.oracle.com/­database/­timesten-18.1
DeveloperGigaspaces TechnologiesLeanXcaleMicrosoftMonetDB BVOracle, TimesTen Performance Software, HP infooriginally founded in HP Labs it was acquired by Oracle in 2005
Initial release20002015201920041998
Current release15.5, September 2020cloud service with continuous releasesDec2023 (11.49), December 202311 Release 2 (11.2.2.8.0)
License infoCommercial or Open SourceOpen Source infoApache Version 2; Commercial licenses availablecommercialcommercialOpen Source infoMozilla Public License 2.0commercial
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetC
Server operating systemsLinux
macOS
Solaris
Windows
hostedFreeBSD
Linux
OS X
Solaris
Windows
AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows
Data schemeschema-freeyesFixed 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-typesyesyes
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.no infoXML can be used for describing objects metadatayesno
Secondary indexesyesall fields are automatically indexedyesyes
SQL infoSupport of SQLSQL-99 for query and DML statementsyes infothrough Apache DerbyKusto Query Language (KQL), SQL subsetyes infoSQL 2003 with some extensionsyes
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
JDBC
native C library infoMAPI library (MonetDB application programming interface)
ODBC
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Supported programming languages.Net
C++
Java
Python
Scala
C
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
C
C++
Java
PL/SQL
Server-side scripts infoStored proceduresyesYes, possible languages: KQL, Python, Ryes, in SQL, C, RPL/SQL
Triggersyes, event driven architectureyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding via remote tablesnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none infoSource-replica replication available in experimental statusMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes infoby means of logfiles and checkpoints
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes
User concepts infoAccess controlRole-based access controlAzure Active Directory Authenticationfine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
GigaSpacesLeanXcaleMicrosoft Azure Data ExplorerMonetDBTimesTen
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