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DBMS > Ehcache vs. GigaSpaces vs. MarkLogic vs. Teradata Aster

System Properties Comparison Ehcache vs. GigaSpaces vs. MarkLogic vs. Teradata Aster

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Editorial information provided by DB-Engines
NameEhcache  Xexclude from comparisonGigaSpaces  Xexclude from comparisonMarkLogic  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionA widely adopted Java cache with tiered storage optionsHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsOperational and transactional Enterprise NoSQL databasePlatform for big data analytics on multistructured data sources and types
Primary database modelKey-value storeDocument store
Object oriented DBMS infoValues are user defined objects
Document store
Native XML DBMS
RDF store infoas of version 7
Search engine
Relational DBMS
Secondary database modelsGraph DBMS
Search engine
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.89
Rank#67  Overall
#8  Key-value stores
Score0.97
Rank#192  Overall
#32  Document stores
#6  Object oriented DBMS
Score5.92
Rank#58  Overall
#10  Document stores
#1  Native XML DBMS
#1  RDF stores
#6  Search engines
Websitewww.ehcache.orgwww.gigaspaces.comwww.marklogic.com
Technical documentationwww.ehcache.org/­documentationdocs.gigaspaces.com/­latest/­landing.htmldocs.marklogic.com
DeveloperTerracotta Inc, owned by Software AGGigaspaces TechnologiesMarkLogic Corp.Teradata
Initial release2009200020012005
Current release3.10.0, March 202215.5, September 202011.0, December 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache Version 2; Commercial licenses availablecommercial inforestricted free version is availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .NetC++
Server operating systemsAll OS with a Java VMLinux
macOS
Solaris
Windows
Linux
OS X
Windows
Linux
Data schemeschema-freeschema-freeschema-free infoSchema can be enforcedFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesyesyesyes
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 infoXML can be used for describing objects metadatayesyes infoin Aster File Store
Secondary indexesnoyesyesyes
SQL infoSupport of SQLnoSQL-99 for query and DML statementsyes infoSQL92yes
APIs and other access methodsJCacheGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
Java API
Node.js Client API
ODBC
proprietary Optic API infoProprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesJava.Net
C++
Java
Python
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnoyesyes infovia XQuery or JavaScriptR packages
Triggersyes infoCache Event Listenersyes, event driven architectureyesno
Partitioning methods infoMethods for storing different data on different nodesSharding infoby using Terracotta ServerShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoby using Terracotta ServerMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yesyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoMap-Reduce pattern can be built with XAP task executorsyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemTunable Consistency (Strong, Eventual, Weak)Immediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayes infosupports JTA and can work as an XA resourceACIDACID infocan act as a resource manager in an XA/JTA transactionACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing a tiered cache-storage approachyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes, with Range Indexesno
User concepts infoAccess controlnoRole-based access controlRole-based access control at the document and subdocument levelsfine grained access rights according to SQL-standard

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EhcacheGigaSpacesMarkLogicTeradata Aster
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