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DBMS > Apache IoTDB vs. BigchainDB vs. GridDB vs. IRONdb

System Properties Comparison Apache IoTDB vs. BigchainDB vs. GridDB vs. IRONdb

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonBigchainDB  Xexclude from comparisonGridDB  Xexclude from comparisonIRONdb  Xexclude from comparison
IRONdb 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 FlinkBigchainDB is scalable blockchain database offering decentralization, immutability and native assetsScalable in-memory time series database optimized for IoT and Big DataA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelTime Series DBMSDocument storeTime Series DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.85
Rank#208  Overall
#35  Document stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Websiteiotdb.apache.orgwww.bigchaindb.comgriddb.netwww.circonus.com/solutions/time-series-database/
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlbigchaindb.readthedocs.io/­en/­latestdocs.griddb.netdocs.circonus.com/irondb/category/getting-started
DeveloperApache Software FoundationToshiba CorporationCirconus LLC.
Initial release2018201620132017
Current release1.1.0, April 20235.1, August 2022V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoAGPL v3Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercial
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaPythonC++C and C++
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinuxLinux
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnoyes infonumerical, string, blob, geometry, boolean, timestampyes infotext, numeric, histograms
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.nononono
Secondary indexesyesyesno
SQL infoSupport of SQLSQL-like query languagenoSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsJDBC
Native API
CLI Client
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Go
Haskell
Java
JavaScript
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Lua
Perl
PHP
Python
R
Ruby
Rust
Scala
Server-side scripts infoStored proceduresyesnoyes, in Lua
Triggersyesyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingAutomatic, metric affinity per node
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasselectable replication factorSource-replica replicationconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency within container, eventual consistency across containersImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID at container levelno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes,with MongoDB ord RethinkDByesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesno
User concepts infoAccess controlyesyesAccess rights for users can be defined per databaseno
More information provided by the system vendor
Apache IoTDBBigchainDBGridDBIRONdb
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
Apache IoTDBBigchainDBGridDBIRONdb
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