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DBMS > HyperSQL vs. InfluxDB vs. Sqrrl vs. TDengine

System Properties Comparison HyperSQL vs. InfluxDB vs. Sqrrl vs. TDengine

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Editorial information provided by DB-Engines
NameHyperSQL infoalso known as HSQLDB  Xexclude from comparisonInfluxDB  Xexclude from comparisonSqrrl  Xexclude from comparisonTDengine  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been removed from the DB-Engines ranking.
DescriptionMultithreaded, transactional RDBMS written in Java infoalso known as HSQLDBDBMS for storing time series, events and metricsAdaptable, secure NoSQL built on Apache AccumuloTime Series DBMS and big data platform
Primary database modelRelational DBMSTime Series DBMSDocument store
Graph DBMS
Key-value store
Wide column store
Time Series DBMS
Secondary database modelsSpatial DBMS infowith GEO packageRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.23
Rank#93  Overall
#48  Relational DBMS
Score24.39
Rank#28  Overall
#1  Time Series DBMS
Score2.68
Rank#106  Overall
#9  Time Series DBMS
Websitehsqldb.orgwww.influxdata.com/­products/­influxdb-overviewsqrrl.comgithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationhsqldb.org/­web/­hsqlDocsFrame.htmldocs.influxdata.com/­influxdbdocs.tdengine.com
DeveloperAmazon infooriginally Sqrrl Data, Inc.TDEngine, previously Taos Data
Initial release2001201320122019
Current release2.7.2, June 20232.7.6, April 20243.0, August 2022
License infoCommercial or Open SourceOpen Source infobased on BSD licenseOpen Source infoMIT-License; commercial enterprise version availablecommercialOpen Source infoAGPL V3, also commercial editions available
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaGoJavaC
Server operating systemsAll OS with a Java VM infoEmbedded (into Java applications) and Client-Server operating modesLinux
OS X infothrough Homebrew
LinuxLinux
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesNumeric data and Stringsyesyes
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 indexesyesnoyesno
SQL infoSupport of SQLyesSQL-like query languagenoStandard SQL with extensions for time-series applications
APIs and other access methodsHTTP API infoJDBC via HTTP
JDBC
ODBC
HTTP API
JSON over UDP
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
JDBC
RESTful HTTP API
Supported programming languagesAll languages supporting JDBC/ODBC
Java
.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresJava, SQLnonono
Triggersyesnonoyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoin enterprise version onlySharding infomaking use of HadoopSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor infoin enterprise version onlyselectable replication factor infomaking use of Hadoopyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoAtomic updates per row, document, or graph entity
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.yesyes infoDepending on used storage engine
User concepts infoAccess controlfine grained access rights according to SQL-standardsimple rights management via user accountsCell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)yes
More information provided by the system vendor
HyperSQL infoalso known as HSQLDBInfluxDBSqrrlTDengine
Specific characteristicsInfluxData is the creator of InfluxDB , the open source time series database. It...
» more
TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» more
TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Key customersInfluxData has more than 1,900 paying customers, including customers include MuleSoft,...
» more
Market metricsFastest-growing database to drive 27,500 GitHub stars Over 750,000 daily active instances
» more
TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
» more
TDengine OSS is an open source, cloud native time series database. It includes built-in...
» more
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More resources
HyperSQL infoalso known as HSQLDBInfluxDBSqrrlTDengine
DB-Engines blog posts

Why Build a Time Series Data Platform?
20 July 2017, Paul Dix (guest author)

Time Series DBMS are the database category with the fastest increase in popularity
4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
1 June 2015, Paul Andlinger

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