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DBMS > GigaSpaces vs. Hazelcast vs. MarkLogic vs. Microsoft Azure Table Storage

System Properties Comparison GigaSpaces vs. Hazelcast vs. MarkLogic vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameGigaSpaces  Xexclude from comparisonHazelcast  Xexclude from comparisonMarkLogic  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionHigh performance in-memory data grid platform, powering three products: Smart Cache, Smart ODS (Operational Data Store), Smart Augmented TransactionsA widely adopted in-memory data gridOperational and transactional Enterprise NoSQL databaseA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelDocument store
Object oriented DBMS infoValues are user defined objects
Key-value storeDocument store
Native XML DBMS
RDF store infoas of version 7
Search engine
Wide column store
Secondary database modelsGraph DBMS
Search engine
Document store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.03
Rank#188  Overall
#32  Document stores
#6  Object oriented DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score5.18
Rank#63  Overall
#11  Document stores
#1  Native XML DBMS
#1  RDF stores
#7  Search engines
Score4.04
Rank#77  Overall
#6  Wide column stores
Websitewww.gigaspaces.comhazelcast.comwww.marklogic.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationdocs.gigaspaces.com/­latest/­landing.htmlhazelcast.org/­imdg/­docsdocs.marklogic.com
DeveloperGigaspaces TechnologiesHazelcastMarkLogic Corp.Microsoft
Initial release2000200820012012
Current release15.5, September 20205.3.6, November 202311.0, 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 servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .NetJavaC++
Server operating systemsLinux
macOS
Solaris
Windows
All OS with a Java VMLinux
OS X
Windows
hosted
Data schemeschema-freeschema-freeschema-free infoSchema can be enforcedschema-free
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.no infoXML can be used for describing objects metadatayes infothe object must implement a serialization strategyyesno
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-99 for query and DML statementsSQL-like query languageyes infoSQL92no
APIs and other access methodsGigaSpaces LRMI
Hibernate
JCache
JDBC
JPA
ODBC
RESTful HTTP API
Spring Data
JCache
JPA
Memcached protocol
RESTful HTTP API
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
RESTful HTTP API
Supported programming languages.Net
C++
Java
Python
Scala
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesyes infoEvent Listeners, Executor Servicesyes infovia XQuery or JavaScriptno
Triggersyes, event driven architectureyes infoEventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication infosynchronous or asynchronous
Source-replica replication infosynchronous or asynchronous
yes infoReplicated Mapyesyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoMap-Reduce pattern can be built with XAP task executorsyesyes infovia Hadoop Connector, HDFS Direct Access and in-database MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency infoConsistency level configurable: ALL, QUORUM, ANYImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedACID infocan act as a resource manager in an XA/JTA transactionoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
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, with Range Indexesno
User concepts infoAccess controlRole-based access controlRole-based access controlRole-based access control at the document and subdocument levelsAccess rights based on private key authentication or shared access signatures

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More resources
GigaSpacesHazelcastMarkLogicMicrosoft Azure Table Storage
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