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DBMS > HyperSQL vs. InfluxDB vs. RocksDB vs. Teradata Aster

System Properties Comparison HyperSQL vs. InfluxDB vs. RocksDB vs. Teradata Aster

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Editorial information provided by DB-Engines
NameHyperSQL infoalso known as HSQLDB  Xexclude from comparisonInfluxDB  Xexclude from comparisonRocksDB  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionMultithreaded, transactional RDBMS written in Java infoalso known as HSQLDBDBMS for storing time series, events and metricsEmbeddable persistent key-value store optimized for fast storage (flash and RAM)Platform for big data analytics on multistructured data sources and types
Primary database modelRelational DBMSTime Series DBMSKey-value storeRelational DBMS
Secondary database modelsSpatial DBMS infowith GEO package
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.49
Rank#87  Overall
#47  Relational DBMS
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Score3.65
Rank#85  Overall
#11  Key-value stores
Websitehsqldb.orgwww.influxdata.com/­products/­influxdb-overviewrocksdb.org
Technical documentationhsqldb.org/­web/­hsqlDocsFrame.htmldocs.influxdata.com/­influxdbgithub.com/­facebook/­rocksdb/­wiki
DeveloperFacebook, Inc.Teradata
Initial release2001201320132005
Current release2.7.2, June 20232.7.6, April 20248.11.4, April 2024
License infoCommercial or Open SourceOpen Source infobased on BSD licenseOpen Source infoMIT-License; commercial enterprise version availableOpen Source infoBSDcommercial
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 languageJavaGoC++
Server operating systemsAll OS with a Java VM infoEmbedded (into Java applications) and Client-Server operating modesLinux
OS X infothrough Homebrew
LinuxLinux
Data schemeyesschema-freeschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesNumeric data and Stringsnoyes
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.nononoyes infoin Aster File Store
Secondary indexesyesnonoyes
SQL infoSupport of SQLyesSQL-like query languagenoyes
APIs and other access methodsHTTP API infoJDBC via HTTP
JDBC
ODBC
HTTP API
JSON over UDP
C++ API
Java API
ADO.NET
JDBC
ODBC
OLE DB
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
C
C++
Go
Java
Perl
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresJava, SQLnonoR packages
Triggersyesnono
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoin enterprise version onlyhorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneselectable replication factor infoin enterprise version onlyyesyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyesnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoyesACID
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 engineyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardsimple rights management via user accountsnofine grained access rights according to SQL-standard
More information provided by the system vendor
HyperSQL infoalso known as HSQLDBInfluxDBRocksDBTeradata Aster
Specific characteristicsInfluxData is the creator of InfluxDB , the open source time series database. It...
» more
Competitive advantagesTime to Value InfluxDB is available in all the popular languages and frameworks,...
» more
Typical application scenariosIoT & Sensor Monitoring Developers are witnessing the instrumentation of every available...
» 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
Licensing and pricing modelsOpen source core with closed source clustering available either on-premise or on...
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News

What is DevRel at InfluxData
21 May 2024

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16 May 2024

What to Expect When You’re Expecting InfluxDB: A Guide
14 May 2024

Introduction to Apache Iceberg
9 May 2024

Converting Timestamp to Date in Java
7 May 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services
3rd partiesSpeedb: A high performance RocksDB-compliant key-value store optimized for write-intensive workloads.
» more

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
HyperSQL infoalso known as HSQLDBInfluxDBRocksDBTeradata Aster
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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Recent citations in the news

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