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DBMS > Apache IoTDB vs. Apache Kylin vs. Google Cloud Datastore vs. IRONdb

System Properties Comparison Apache IoTDB vs. Apache Kylin vs. Google Cloud Datastore vs. IRONdb

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonApache Kylin  Xexclude from comparisonGoogle Cloud Datastore  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 FlinkA distributed analytics engine for big data, providing a SQL interface and multi-dimensional analysis (OLAP) and leveraging the Hadoop stackAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelTime Series DBMSRelational DBMSDocument storeTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.18
Rank#172  Overall
#79  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Websiteiotdb.apache.orgkylin.apache.orgcloud.google.com/­datastorewww.circonus.com/solutions/time-series-database/
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlkylin.apache.org/­docscloud.google.com/­datastore/­docsdocs.circonus.com/irondb/category/getting-started
DeveloperApache Software FoundationApache Software Foundation, originally contributed from eBay IncGoogleCirconus LLC.
Initial release2018201520082017
Current release1.1.0, April 20233.1.0, July 2020V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaC and C++
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxhostedLinux
Data schemeyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyes, details hereyes 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 indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageANSI SQL for queries (using Apache Calcite)SQL-like query language (GQL)SQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
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 proceduresyesusing Google App Engineyes, in Lua
TriggersyesCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingAutomatic, 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 replicasMulti-source replication using Paxosconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
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.yesnonono
User concepts infoAccess controlyesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)no

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More resources
Apache IoTDBApache KylinGoogle Cloud DatastoreIRONdb
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