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DBMS > atoti vs. Hazelcast vs. Splice Machine

System Properties Comparison atoti vs. Hazelcast vs. Splice Machine

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Editorial information provided by DB-Engines
Nameatoti  Xexclude from comparisonHazelcast  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAn in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.A widely adopted in-memory data gridOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelObject oriented DBMSKey-value storeRelational DBMS
Secondary database modelsDocument store infoJSON support with IMDG 3.12
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.61
Rank#243  Overall
#11  Object oriented DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteatoti.iohazelcast.comsplicemachine.com
Technical documentationdocs.atoti.iohazelcast.org/­imdg/­docssplicemachine.com/­how-it-works
DeveloperActiveViamHazelcastSplice Machine
Initial release20082014
Current release5.3.6, November 20233.1, March 2021
License infoCommercial or Open Sourcecommercial infofree versions availableOpen Source infoApache Version 2; commercial licenses availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemsAll OS with a Java VMLinux
OS X
Solaris
Windows
Data schemeschema-freeyes
Typing infopredefined data types such as float or dateyesyes
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 strategy
Secondary indexesyesyes
SQL infoSupport of SQLMultidimensional Expressions (MDX)SQL-like query languageyes
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
Server-side scripts infoStored proceduresPythonyes infoEvent Listeners, Executor Servicesyes infoJava
Triggersyes infoEventsyes
Partitioning methods infoMethods for storing different data on different nodesSharding, horizontal partitioningShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyes infoReplicated MapMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate Consistency
Foreign keys infoReferential integritynoyes
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 datayes, multi-version concurrency control (MVCC)yesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesyes
User concepts infoAccess controlRole-based access controlAccess rights for users, groups and roles according to SQL-standard

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More resources
atotiHazelcastSplice Machine
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