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DBMS > Apache IoTDB vs. EJDB vs. Google Cloud Bigtable vs. Kinetica

System Properties Comparison Apache IoTDB vs. EJDB vs. Google Cloud Bigtable vs. Kinetica

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonEJDB  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonKinetica  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 FlinkEmbeddable document-store database library with JSON representation of queries (in MongoDB style)Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully vectorized database across both GPUs and CPUs
Primary database modelTime Series DBMSDocument storeKey-value store
Wide column store
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.31
Rank#296  Overall
#44  Document stores
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Websiteiotdb.apache.orggithub.com/­Softmotions/­ejdbcloud.google.com/­bigtablewww.kinetica.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlgithub.com/­Softmotions/­ejdb/­blob/­master/­README.mdcloud.google.com/­bigtable/­docsdocs.kinetica.com
DeveloperApache Software FoundationSoftmotionsGoogleKinetica
Initial release2018201220152012
Current release1.1.0, April 20237.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGPLv2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaCC, C++
Server operating systemsAll OS with a Java VM (>= 1.8)server-lesshostedLinux
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyes infostring, integer, double, bool, date, object_idnoyes
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 indexesyesnonoyes
SQL infoSupport of SQLSQL-like query languagenonoSQL-like DML and DDL statements
APIs and other access methodsJDBC
Native API
in-process shared librarygRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Actionscript
C
C#
C++
Go
Java
JavaScript (Node.js)
Lua
Objective-C
Pike
Python
Ruby
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresyesnonouser defined functions
Triggersyesnonoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynono infotypically not needed, however similar functionality with collection joins possiblenoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoAtomic single-row operationsno
Concurrency infoSupport for concurrent manipulation of datayesyes infoRead/Write Lockingyesyes
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.yesnoyes infoGPU vRAM or System RAM
User concepts infoAccess controlyesnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table level

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More resources
Apache IoTDBEJDBGoogle Cloud BigtableKinetica
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