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DBMS > NSDb vs. OceanBase vs. Spark SQL vs. Splice Machine

System Properties Comparison NSDb vs. OceanBase vs. Spark SQL vs. Splice Machine

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Editorial information provided by DB-Engines
NameNSDb  Xexclude from comparisonOceanBase  Xexclude from comparisonSpark SQL  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesA distributed, high available RDBMS compatible with Oracle and MySQLSpark SQL is a component on top of 'Spark Core' for structured data processingOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Wide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.08
Rank#369  Overall
#40  Time Series DBMS
Score1.57
Rank#149  Overall
#69  Relational DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitensdb.ioen.oceanbase.comspark.apache.org/­sqlsplicemachine.com
Technical documentationnsdb.io/­Architectureen.oceanbase.com/­docs/­oceanbase-databasespark.apache.org/­docs/­latest/­sql-programming-guide.htmlsplicemachine.com/­how-it-works
DeveloperOceanBase infopreviously Alibaba and Ant GroupApache Software FoundationSplice Machine
Initial release2017201020142014
Current release4.3.0, April 20243.5.0 ( 2.13), September 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoCommercial license availableOpen Source infoApache 2.0Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ScalaC++ScalaJava
Server operating systemsLinux
macOS
LinuxLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyes: int, bigint, decimal, stringyesyesyes
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.noyesno
Secondary indexesall fields are automatically indexedyesnoyes
SQL infoSupport of SQLSQL-like query languageyesSQL-like DML and DDL statementsyes
APIs and other access methodsgRPC
HTTP REST
WebSocket
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesJava
Scala
Ada infoin MySQL-compatible model
C infoin Oracle- and MySQL- compatible models
C++ infoin Oracle- and MySQL- compatible models
D infoin MySQL-compatible model
Delphi infoin MySQL-compatible model
Eiffel infoin MySQL-compatible model
Erlang infoin MySQL-compatible model
Haskell infoin MySQL-compatible model
Java infoin Oracle- and MySQL- compatible models
JavaScript (Node.js) infoin MySQL-compatible model
Objective-C infoin MySQL-compatible model
OCaml infoin MySQL-compatible model
Perl infoin MySQL-compatible model
PHP infoin MySQL-compatible model
Python infoin MySQL-compatible model
Ruby infoin MySQL-compatible model
Scheme infoin MySQL-compatible model
Tcl infoin MySQL-compatible model
Java
Python
R
Scala
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoPL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible modenoyes infoJava
Triggersyesnoyes
Partitioning methods infoMethods for storing different data on different nodesShardinghorizontal partitioning (by hash, key, range, range columns, list, and list columns)yes, utilizing Spark CoreShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosnoneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentUsing Apache Luceneyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles according to SQL-standardnoAccess rights for users, groups and roles according to SQL-standard

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More resources
NSDbOceanBaseSpark SQLSplice Machine
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