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DBMS > Apache IoTDB vs. Drizzle vs. Hive vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. Drizzle vs. Hive vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDrizzle  Xexclude from comparisonHive  Xexclude from comparisonTimescaleDB  Xexclude from comparison
Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle 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 FlinkMySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.data warehouse software for querying and managing large distributed datasets, built on HadoopA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMSRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websiteiotdb.apache.orghive.apache.orgwww.timescale.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcwiki.apache.org/­confluence/­display/­Hive/­Homedocs.timescale.com
DeveloperApache Software FoundationDrizzle project, originally started by Brian AkerApache Software Foundation infoinitially developed by FacebookTimescale
Initial release2018200820122017
Current release1.1.0, April 20237.2.4, September 20123.1.3, April 20222.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoGNU GPLOpen Source infoApache Version 2Open 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++JavaC
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
All OS with a Java VMLinux
OS X
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, 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.noyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyes infowith proprietary extensionsSQL-like DML and DDL statementsyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
Native API
JDBCJDBC
ODBC
Thrift
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C++
Java
PHP
C++
Java
PHP
Python
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesnoyes infouser defined functions and integration of map-reduceuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesno infohooks for callbacks inside the server can be used.noyes
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
selectable replication factorSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyes infoquery execution via MapReduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACID
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.yesno
User concepts infoAccess controlyesPluggable authentication mechanisms infoe.g. LDAP, HTTPAccess rights for users, groups and rolesfine grained access rights according to SQL-standard

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More resources
Apache IoTDBDrizzleHiveTimescaleDB
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