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DBMS > QuestDB vs. RDF4J vs. Solr vs. Vitess

System Properties Comparison QuestDB vs. RDF4J vs. Solr vs. Vitess

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Editorial information provided by DB-Engines
NameQuestDB  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSolr  Xexclude from comparisonVitess  Xexclude from comparison
DescriptionA high performance open source SQL database for time series dataRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.A widely used distributed, scalable search engine based on Apache LuceneScalable, distributed, cloud-native DBMS, extending MySQL
Primary database modelTime Series DBMSRDF storeSearch engineRelational DBMS
Secondary database modelsRelational DBMSSpatial DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.70
Rank#105  Overall
#8  Time Series DBMS
Score0.74
Rank#222  Overall
#9  RDF stores
Score41.02
Rank#24  Overall
#3  Search engines
Score0.88
Rank#203  Overall
#95  Relational DBMS
Websitequestdb.iordf4j.orgsolr.apache.orgvitess.io
Technical documentationquestdb.io/­docsrdf4j.org/­documentationsolr.apache.org/­resources.htmlvitess.io/­docs
DeveloperQuestDB Technology IncSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Apache Software FoundationThe Linux Foundation, PlanetScale
Initial release2014200420062013
Current release9.6.1, May 202415.0.2, December 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0Open Source infoEclipse Distribution License (EDL), v1.0.Open Source infoApache Version 2Open Source infoApache Version 2.0, commercial licenses 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 languageJava (Zero-GC), C++, RustJavaJavaGo
Server operating systemsLinux
macOS
Windows
Linux
OS X
Unix
Windows
All OS with a Java VM inforuns as a servlet in servlet container (e.g. Tomcat, Jetty is included)Docker
Linux
macOS
Data schemeyes infoschema-free via InfluxDB Line Protocolyes infoRDF Schemasyes infoDynamic Fields enables on-the-fly addition of new fieldsyes
Typing infopredefined data types such as float or dateyesyesyes infosupports customizable data types and automatic typingyes
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 indexesnoyesyes infoAll search fields are automatically indexedyes
SQL infoSupport of SQLSQL with time-series extensionsnoSolr Parallel SQL Interfaceyes infowith proprietary extensions
APIs and other access methodsHTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Java API
RESTful HTTP/JSON API
ADO.NET
JDBC
MySQL protocol
ODBC
Supported programming languagesC infoPostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust infoover HTTP
Java
PHP
Python
.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala
Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresnoyesJava pluginsyes infoproprietary syntax
Triggersnoyesyes infoUser configurable commands triggered on index changesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by timestamps)noneShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replication with eventual consistencynoneyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonospark-solr: github.com/­lucidworks/­spark-solr and streaming expressions to reduceno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyEventual Consistency across shards
Immediate Consistency within a shard
Foreign keys infoReferential integritynonoyes infonot for MyISAM storage engine
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID for single-table writesACID infoIsolation support depends on the API usedoptimistic lockingACID at shard level
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infotable locks or row locks depending on storage engine
Durability infoSupport for making data persistentyesyes infoin-memory storage is supported as wellyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes infothrough memory mapped filesyesyes
User concepts infoAccess controlnoyesUsers with fine-grained authorization concept infono user groups or roles
More information provided by the system vendor
QuestDBRDF4J infoformerly known as SesameSolrVitess
Specific characteristicsRelational model with native time series support Column-based storage and time partitioned...
» more
Competitive advantagesHigh ingestion throughput: peak of 4M rows/sec (TSBS Benchmark) Code optimizations...
» more
Typical application scenariosFinancial tick data Industrial IoT Application Metrics Monitoring
» more
Key customersBanks & Hedge funds, Yahoo, OKX, Airbus, Aquis Exchange, Net App, Cloudera, Airtel,...
» more
Licensing and pricing modelsOpen source Apache 2.0 QuestDB Enterprise QuestDB Cloud
» more
News

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12 April 2024

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
QuestDBRDF4J infoformerly known as SesameSolrVitess
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Recent citations in the news

QuestDB snares $12M Series A with hosted version coming soon
3 November 2021, TechCrunch

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