DB-EnginesInfluxDB: Focus on building software with an easy-to-use serverless, scalable time series platformEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Apache Pinot vs. Hawkular Metrics vs. InfluxDB vs. Linter

System Properties Comparison Apache Pinot vs. Hawkular Metrics vs. InfluxDB vs. Linter

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines
NameApache Pinot  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonInfluxDB  Xexclude from comparisonLinter  Xexclude from comparison
DescriptionRealtime distributed OLAP datastore, designed to answer OLAP queries with low latencyHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.DBMS for storing time series, events and metricsRDBMS for high security requirements
Primary database modelRelational DBMSTime Series DBMSTime Series DBMSRelational DBMS
Secondary database modelsSpatial DBMS infowith GEO packageSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.38
Rank#275  Overall
#126  Relational DBMS
Score0.08
Rank#366  Overall
#39  Time Series DBMS
Score24.39
Rank#28  Overall
#1  Time Series DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Websitepinot.apache.orgwww.hawkular.orgwww.influxdata.com/­products/­influxdb-overviewlinter.ru
Technical documentationdocs.pinot.apache.orgwww.hawkular.org/­hawkular-metrics/­docs/­user-guidedocs.influxdata.com/­influxdb
DeveloperApache Software Foundation and contributorsCommunity supported by Red Hatrelex.ru
Initial release2015201420131990
Current release1.0.0, September 20232.7.6, April 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0Open Source infoMIT-License; commercial enterprise version availablecommercial
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 languageJavaJavaGoC and C++
Server operating systemsAll OS with a Java JDK11 or higherLinux
OS X
Windows
Linux
OS X infothrough Homebrew
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesNumeric data and Stringsyes
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 indexesnonoyes
SQL infoSupport of SQLSQL-like query languagenoSQL-like query languageyes
APIs and other access methodsJDBCHTTP RESTHTTP API
JSON over UDP
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
Supported programming languagesGo
Java
Python
Go
Java
Python
Ruby
.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
Server-side scripts infoStored proceduresnonoyes infoproprietary syntax with the possibility to convert from PL/SQL
Triggersyes infovia Hawkular Alertingnoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infobased on CassandraSharding infoin enterprise version onlynone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on Cassandraselectable replication factor infoin enterprise version onlySource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency
Foreign keys infoReferential integritynonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoDepending on used storage engine
User concepts infoAccess controlnosimple rights management via user accountsfine grained access rights according to SQL-standard
More information provided by the system vendor
Apache PinotHawkular MetricsInfluxDBLinter
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...
» more
News

Scaling Data Collection: Solving Renewable Energy Challenges with InfluxDB
6 June 2024

Deadman Alerts with Grafana and InfluxDB Cloud 3.0
5 June 2024

Chasing the Skies: Monitoring Flights with InfluxDB
4 June 2024

Monitoring Your Cloud Environments and Applications with InfluxDB
30 May 2024

Webinar Recap: Unleash the Full Potential of Your Time Series Data with InfluxDB and AWS
29 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

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

More resources
Apache PinotHawkular MetricsInfluxDBLinter
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

show all

Recent citations in the news

StarTree broadly enhances Apache Pinot-based analytics platform
8 May 2024, SiliconANGLE News

Apache Pinot - SD Times Open Source Project of the Week
31 May 2024, SDTimes.com

Real-Time Analytics for Mobile App Crashes using Apache Pinot
2 November 2023, Uber

StarTree Finds Apache Pinot the Right Vintage for IT Observability
8 May 2024, Datanami

Open source Apache Pinot advances as StarTree boosts real-time analytics and observability
8 May 2024, VentureBeat

provided by Google News

Waiting for Red Hat OpenShift 4.0? Too late, 4.1 has already arrived… • DEVCLASS
5 June 2019, DevClass

provided by Google News

Amazon Timestream for InfluxDB is now generally available
15 March 2024, AWS Blog

Apache Doris for Log and Time Series Data Analysis in NetEase: Why Not Elasticsearch and InfluxDB?
5 June 2024, hackernoon.com

Amazon Timestream: Managed InfluxDB for Time Series Data
14 March 2024, The New Stack

InfluxData Collaborating with AWS to Bring InfluxDB and Time Series Analytics to Developers Around the World
14 March 2024, Business Wire

How the FDAP Stack Gives InfluxDB 3.0 Real-Time Speed, Efficiency
15 March 2024, Datanami

provided by Google News



Share this page

Featured Products

Datastax Astra logo

Bring all your data to Generative AI applications with vector search enabled by the most scalable
vector database available.
Try for Free

Milvus logo

Vector database designed for GenAI, fully equipped for enterprise implementation.
Try Managed Milvus for Free

Neo4j logo

See for yourself how a graph database can make your life easier.
Use Neo4j online for free.

Present your product here