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

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

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonInfinityDB  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 FlinkAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA Java embedded Key-Value Store which extends the Java Map interfaceA distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity
Primary database modelTime Series DBMSDocument storeKey-value 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
Score4.47
Rank#76  Overall
#12  Document stores
Score0.00
Rank#378  Overall
#57  Key-value stores
Websiteiotdb.apache.orgcloud.google.com/­datastoreboilerbay.comwww.circonus.com/solutions/time-series-database/
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­datastore/­docsboilerbay.com/­infinitydb/­manualdocs.circonus.com/irondb/category/getting-started
DeveloperApache Software FoundationGoogleBoiler Bay Inc.Circonus LLC.
Initial release2018200820022017
Current release1.1.0, April 20234.0V0.10.20, January 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
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)hostedAll OS with a Java VMLinux
Data schemeyesschema-freeyes infonested virtual Java Maps, multi-value, logical ‘tuple space’ runtime Schema upgradeschema-free
Typing infopredefined data types such as float or dateyesyes, details hereyes infoall Java primitives, Date, CLOB, BLOB, huge sparse arraysyes 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 infomanual creation possible, using inversions based on multi-value capabilityno
SQL infoSupport of SQLSQL-like query languageSQL-like query language (GQL)noSQL-like query language (Circonus Analytics Query Language: CAQL)
APIs and other access methodsJDBC
Native API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Access via java.util.concurrent.ConcurrentNavigableMap Interface
Proprietary API to InfinityDB ItemSpace (boilerbay.com/­docs/­ItemSpaceDataStructures.htm)
HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java.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 Enginenoyes, in Lua
TriggersyesCallbacks using the Google Apps Enginenono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingnoneAutomatic, 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 Paxosnoneconfigurable replication factor, datacenter aware
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate 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 infoREAD-COMMITTED or SERIALIZEDImmediate consistency per node, eventual consistency across nodes
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsno infomanual creation possible, using inversions based on multi-value capabilityno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infoOptimistic locking for transactions; no isolation for bulk loadsno
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)nono

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More resources
Apache IoTDBGoogle Cloud DatastoreInfinityDBIRONdb
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