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DBMS > Hazelcast vs. Splice Machine vs. Tkrzw

System Properties Comparison Hazelcast vs. Splice Machine vs. Tkrzw

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Editorial information provided by DB-Engines
NameHazelcast  Xexclude from comparisonSplice Machine  Xexclude from comparisonTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet  Xexclude from comparison
DescriptionA widely adopted in-memory data gridOpen-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 Cabinet
Primary database modelKey-value storeRelational DBMSKey-value store
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score5.47
Rank#58  Overall
#6  Key-value stores
Score0.54
Rank#239  Overall
#112  Relational DBMS
Score0.01
Rank#388  Overall
#60  Key-value stores
Websitehazelcast.comsplicemachine.comdbmx.net/­tkrzw
Technical documentationhazelcast.org/­imdg/­docssplicemachine.com/­how-it-works
DeveloperHazelcastSplice MachineMikio Hirabayashi
Initial release200820142020
Current release5.3.6, November 20233.1, March 20210.9.3, August 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2; commercial licenses availableOpen Source infoAGPL 3.0, commercial license availableOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC++
Server operating systemsAll OS with a Java VMLinux
OS X
Solaris
Windows
Linux
macOS
Data schemeschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesyesno
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.yes infothe object must implement a serialization strategyno
Secondary indexesyesyes
SQL infoSupport of SQLSQL-like query languageyesno
APIs and other access methodsJCache
JPA
Memcached protocol
RESTful HTTP API
JDBC
Native Spark Datasource
ODBC
Supported programming languages.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
C++
Java
Python
Ruby
Server-side scripts infoStored proceduresyes infoEvent Listeners, Executor Servicesyes infoJavano
Triggersyes infoEventsyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShared Nothhing Auto-Sharding, Columnar Partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapMulti-source replication
Source-replica replication
none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesYes, via Full Spark Integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataone or two-phase-commit; repeatable reads; read commitedACID
Concurrency infoSupport for concurrent manipulation of datayesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes infousing specific database classes
User concepts infoAccess controlRole-based access controlAccess rights for users, groups and roles according to SQL-standardno

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More resources
HazelcastSplice MachineTkrzw infoSuccessor of Tokyo Cabinet and Kyoto Cabinet
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