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DBMS > Apache IoTDB vs. Databricks vs. Kinetica vs. LevelDB vs. Riak TS

System Properties Comparison Apache IoTDB vs. Databricks vs. Kinetica vs. LevelDB vs. Riak TS

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabricks  Xexclude from comparisonKinetica  Xexclude from comparisonLevelDB  Xexclude from comparisonRiak TS  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 FlinkThe Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.Fully vectorized database across both GPUs and CPUsEmbeddable fast key-value storage library that provides an ordered mapping from string keys to string valuesRiak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelTime Series DBMSDocument store
Relational DBMS
Relational DBMSKey-value storeTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score78.61
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score2.35
Rank#111  Overall
#19  Key-value stores
Score0.20
Rank#319  Overall
#27  Time Series DBMS
Websiteiotdb.apache.orgwww.databricks.comwww.kinetica.comgithub.com/­google/­leveldb
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databricks.comdocs.kinetica.comgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software FoundationDatabricksKineticaGoogleOpen Source, formerly Basho Technologies
Initial release20182013201220112015
Current release1.1.0, April 20237.1, August 20211.23, February 20213.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoBSDOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC, C++C++Erlang
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinuxIllumos
Linux
OS X
Windows
Linux
OS X
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesnono
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.noyesnonono
Secondary indexesyesyesyesnorestricted
SQL infoSupport of SQLSQL-like query languagewith Databricks SQLSQL-like DML and DDL statementsnoyes, limited
APIs and other access methodsJDBC
Native API
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Python
R
Scala
C++
Java
JavaScript (Node.js)
Python
C++
Go
Java info3rd party binding
JavaScript (Node.js) info3rd party binding
Python info3rd party binding
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresyesuser defined functions and aggregatesuser defined functionsnoErlang
Triggersyesyes infotriggers when inserted values for one or more columns fall within a specified rangenoyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyesSource-replica replicationnoneselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyesnono infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infowith automatic compression on writesyes
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 controlyesAccess rights for users and roles on table levelnono
More information provided by the system vendor
Apache IoTDBDatabricksKineticaLevelDBRiak TS
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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More resources
Apache IoTDBDatabricksKineticaLevelDBRiak TS
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