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DBMS > Apache IoTDB vs. KeyDB vs. Splunk vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. KeyDB vs. Splunk vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonKeyDB  Xexclude from comparisonSplunk  Xexclude from comparisonTimescaleDB  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 FlinkAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsAnalytics Platform for Big DataA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMSKey-value storeSearch engineTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score0.63
Rank#232  Overall
#32  Key-value stores
Score93.02
Rank#13  Overall
#2  Search engines
Score4.06
Rank#73  Overall
#5  Time Series DBMS
Websiteiotdb.apache.orggithub.com/­Snapchat/­KeyDB
keydb.dev
www.splunk.comwww.timescale.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.keydb.devdocs.splunk.com/­Documentation/­Splunkdocs.timescale.com
DeveloperApache Software FoundationEQ Alpha Technology Ltd.Splunk Inc.Timescale
Initial release2018201920032017
Current release1.1.0, April 20232.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoBSD-3commercial infoLimited free edition and free developer edition availableOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C
Server operating systemsAll OS with a Java VM (>= 1.8)LinuxLinux
OS X
Solaris
Windows
Linux
OS X
Windows
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyespartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types
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.nonoyesyes
Secondary indexesyesyes infoby using the Redis Search moduleyesyes
SQL infoSupport of SQLSQL-like query languagenono infoSplunk Search Processing Language for search commandsyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
Native API
Proprietary protocol infoRESP - REdis Serialization ProtocoHTTP RESTADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
C#
Java
JavaScript
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesLuayesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesnoyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingyes, across time and space (hash partitioning) attributes
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
Source-replica replication
Multi-source replicationSource-replica replication with hot standby and reads on replicas info
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
Eventual Consistency
Strong eventual consistency with CRDTs
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoOptimistic locking, atomic execution of commands blocks and scriptsno infoA 'Transaction' in Splunk has a different meaning: grouping related events into a single one for later searchingACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlyessimple password-based access control and ACLAccess rights for users and rolesfine grained access rights according to SQL-standard

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Apache IoTDBKeyDBSplunkTimescaleDB
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