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DBMS > Apache Doris vs. GridGain vs. NSDb vs. Spark SQL

System Properties Comparison Apache Doris vs. GridGain vs. NSDb vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache Doris  Xexclude from comparisonGridGain  Xexclude from comparisonNSDb  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionAn MPP-based analytics DBMS embracing the MySQL protocolGridGain is an in-memory computing platform, built on Apache IgniteScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelRelational DBMSKey-value store
Relational DBMS
Time Series DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.57
Rank#244  Overall
#113  Relational DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Websitedoris.apache.org
github.com/­apache/­doris
www.gridgain.comnsdb.iospark.apache.org/­sql
Technical documentationgithub.com/­apache/­doris/­wikiwww.gridgain.com/­docs/­index.htmlnsdb.io/­Architecturespark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software Foundation, originally contributed from BaiduGridGain Systems, Inc.Apache Software Foundation
Initial release2017200720172014
Current release1.2.2, February 2023GridGain 8.5.13.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialOpen Source infoApache Version 2.0Open Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, C++, .NetJava, ScalaScala
Server operating systemsLinuxLinux
OS X
Solaris
Windows
Linux
macOS
Linux
OS X
Windows
Data schemeyesyesyes
Typing infopredefined data types such as float or dateyesyesyes: int, bigint, decimal, stringyes
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.noyesnono
Secondary indexesyesyesall fields are automatically indexedno
SQL infoSupport of SQLyesANSI-99 for query and DML statements, subset of DDLSQL-like query languageSQL-like DML and DDL statements
APIs and other access methodsJDBC
MySQL client
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
gRPC
HTTP REST
WebSocket
JDBC
ODBC
Supported programming languagesJavaC#
C++
Java
PHP
Python
Ruby
Scala
Java
Scala
Java
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functionsyes (compute grid and cache interceptors can be used instead)nono
Triggersnoyes (cache interceptors and events)no
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingShardingyes, utilizing Spark Core
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyes (replicated cache)none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes (compute grid and hadoop accelerator)no
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardSecurity Hooks for custom implementationsno

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More resources
Apache DorisGridGainNSDbSpark SQL
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