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DBMS > Apache IoTDB vs. LeanXcale vs. Microsoft Access vs. Microsoft Azure Table Storage vs. RocksDB

System Properties Comparison Apache IoTDB vs. LeanXcale vs. Microsoft Access vs. Microsoft Azure Table Storage vs. RocksDB

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonLeanXcale  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonRocksDB  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 FlinkA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesMicrosoft 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 Wide Column Store for rapid development using massive semi-structured datasetsEmbeddable persistent key-value store optimized for fast storage (flash and RAM)
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Relational DBMSWide column storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score0.29
Rank#291  Overall
#41  Key-value stores
#132  Relational DBMS
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score3.65
Rank#85  Overall
#11  Key-value stores
Websiteiotdb.apache.orgwww.leanxcale.comwww.microsoft.com/­en-us/­microsoft-365/­accessazure.microsoft.com/­en-us/­services/­storage/­tablesrocksdb.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldeveloper.microsoft.com/­en-us/­accessgithub.com/­facebook/­rocksdb/­wiki
DeveloperApache Software FoundationLeanXcaleMicrosoftMicrosoftFacebook, Inc.
Initial release20182015199220122013
Current release1.1.0, April 20231902 (16.0.11328.20222), March 20199.2.1, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoBundled with Microsoft OfficecommercialOpen Source infoBSD
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++
Server operating systemsAll OS with a Java VM (>= 1.8)Windows infoNot a real database server, but making use of DLLshostedLinux
Data schemeyesyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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.nonono
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageyes infothrough Apache Derbyyes infobut not compliant to any SQL standardnono
APIs and other access methodsJDBC
Native API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
ADO.NET
DAO
ODBC
OLE DB
RESTful HTTP APIC++ API
Java API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
Java
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Go
Java
Perl
Python
Ruby
Server-side scripts infoStored proceduresyesyes infosince Access 2010 using the ACE-enginenono
Triggersyesyes infosince Access 2010 using the ACE-engineno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding infoImplicit feature of the cloud servicehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infobut no files for transaction loggingoptimistic lockingyes
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnoyes
User concepts infoAccess controlyesno infoa simple user-level security was built in till version Access 2003Access rights based on private key authentication or shared access signaturesno

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Apache IoTDBLeanXcaleMicrosoft AccessMicrosoft Azure Table StorageRocksDB
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