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DBMS > Apache IoTDB vs. GridDB vs. Microsoft Access vs. SurrealDB

System Properties Comparison Apache IoTDB vs. GridDB vs. Microsoft Access vs. SurrealDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGridDB  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonSurrealDB  Xexclude from comparison
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkScalable in-memory time series database optimized for IoT and Big DataMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)A fully ACID transactional, developer-friendly, multi-model DBMS
Primary database modelTime Series DBMSTime Series DBMSRelational DBMSDocument store
Graph DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score1.91
Rank#123  Overall
#10  Time Series DBMS
Score93.76
Rank#12  Overall
#8  Relational DBMS
Score1.11
Rank#174  Overall
#30  Document stores
#17  Graph DBMS
Websiteiotdb.apache.orggriddb.netwww.microsoft.com/­en-us/­microsoft-365/­accesssurrealdb.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.griddb.netdeveloper.microsoft.com/­en-us/­accesssurrealdb.com/­docs
DeveloperApache Software FoundationToshiba CorporationMicrosoftSurrealDB Ltd
Initial release2018201319922022
Current release1.1.0, April 20235.1, August 20221902 (16.0.11328.20222), March 2019v1.5.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial infoBundled with Microsoft OfficeOpen Source
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++Rust
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxWindows infoNot a real database server, but making use of DLLsLinux
macOS
Windows
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyes infonumerical, string, blob, geometry, boolean, timestampyesyes
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.nono
Secondary indexesyesyesyes
SQL infoSupport of SQLSQL-like query languageSQL92, SQL-like TQL (Toshiba Query Language)yes infobut not compliant to any SQL standardSQL-like query language
APIs and other access methodsJDBC
Native API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ADO.NET
DAO
ODBC
OLE DB
GraphQL
RESTful HTTP API
WebSocket
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Deno
Go
JavaScript (Node.js)
Rust
Server-side scripts infoStored proceduresyesnoyes infosince Access 2010 using the ACE-engine
Triggersyesyesyes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Shardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparkConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelACID infobut no files for transaction loggingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlyesAccess rights for users can be defined per databaseno infoa simple user-level security was built in till version Access 2003yes, based on authentication and database rules

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More resources
Apache IoTDBGridDBMicrosoft AccessSurrealDB
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