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DBMS > Apache IoTDB vs. Oracle vs. QuestDB vs. Yanza

System Properties Comparison Apache IoTDB vs. Oracle vs. QuestDB vs. Yanza

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonOracle  Xexclude from comparisonQuestDB  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
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 FlinkWidely used RDBMSA high performance open source SQL database for time series dataTime Series DBMS for IoT Applications
Primary database modelTime Series DBMSRelational DBMSTime Series DBMSTime Series DBMS
Secondary database modelsDocument store
Graph DBMS infowith Oracle Spatial and Graph
RDF store infowith Oracle Spatial and Graph
Spatial DBMS infowith Oracle Spatial and Graph
Vector DBMS infosince Oracle 23
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1236.29
Rank#1  Overall
#1  Relational DBMS
Score2.52
Rank#109  Overall
#9  Time Series DBMS
Websiteiotdb.apache.orgwww.oracle.com/­databasequestdb.ioyanza.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.oracle.com/­en/­databasequestdb.io/­docs
DeveloperApache Software FoundationOracleQuestDB Technology IncYanza
Initial release2018198020142015
Current release1.1.0, April 202323c, September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial inforestricted free version is availableOpen Source infoApache 2.0commercial infofree version available
Cloud-based only infoOnly available as a cloud servicenononono infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC and C++Java (Zero-GC), C++, Rust
Server operating systemsAll OS with a Java VM (>= 1.8)AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS
Linux
macOS
Windows
Windows
Data schemeyesyes infoSchemaless in JSON and XML columnsyes infoschema-free via InfluxDB Line Protocolschema-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.noyesnono
Secondary indexesyesyesnono
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsSQL with time-series extensionsno
APIs and other access methodsJDBC
Native API
JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic
C infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
any language that supports HTTP calls
Server-side scripts infoStored proceduresyesPL/SQL infoalso stored procedures in Java possiblenono
Triggersyesyesnoyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding, horizontal partitioninghorizontal partitioning (by timestamps)none
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
Source-replica replication with eventual consistencynone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkno infocan be realized in PL/SQLnono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoisolation level can be parameterizedACID for single-table writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoVersion 12c introduced the new option 'Oracle Database In-Memory'yes infothrough memory mapped files
User concepts infoAccess controlyesfine grained access rights according to SQL-standardno
More information provided by the system vendor
Apache IoTDBOracleQuestDBYanza
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
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More resources
Apache IoTDBOracleQuestDBYanza
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