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

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

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGridGain  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 FlinkGridGain is an in-memory computing platform, built on Apache IgniteFully 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 DBMSKey-value 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
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  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.gridgain.comwww.kinetica.comgithub.com/­google/­leveldb
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.gridgain.com/­docs/­index.htmldocs.kinetica.comgithub.com/­google/­leveldb/­blob/­main/­doc/­index.mdwww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software FoundationGridGain Systems, Inc.KineticaGoogleOpen Source, formerly Basho Technologies
Initial release20182007201220112015
Current release1.1.0, April 2023GridGain 8.5.17.1, 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 servicenonononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, C++, .NetC, C++C++Erlang
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Solaris
Windows
LinuxIllumos
Linux
OS X
Windows
Linux
OS X
Data schemeyesyesyesschema-freeschema-free
Typing infopredefined data types such as float or dateyesyesyesnono
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 languageANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsnoyes, limited
APIs and other access methodsJDBC
Native API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
RESTful HTTP API
HTTP API
Native Erlang Interface
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#
C++
Java
PHP
Python
Ruby
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 proceduresyesyes (compute grid and cache interceptors can be used instead)user defined functionsnoErlang
Triggersyesyes (cache interceptors and events)yes 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)ShardingShardingnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes (replicated cache)Source-replica replicationnoneselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes (compute grid and hadoop accelerator)nonoyes
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 integritynonoyesnono 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.yesyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlyesSecurity Hooks for custom implementationsAccess rights for users and roles on table levelnono

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More resources
Apache IoTDBGridGainKineticaLevelDBRiak TS
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