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DBMS > Graph Engine vs. InfluxDB vs. RavenDB vs. TimescaleDB

System Properties Comparison Graph Engine vs. InfluxDB vs. RavenDB vs. TimescaleDB

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Editorial information provided by DB-Engines
NameGraph Engine infoformer name: Trinity  Xexclude from comparisonInfluxDB  Xexclude from comparisonRavenDB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineDBMS for storing time series, events and metricsOpen Source Operational and Transactional Enterprise NoSQL Document DatabaseA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelGraph DBMS
Key-value store
Time Series DBMSDocument storeTime Series DBMS
Secondary database modelsSpatial DBMS infowith GEO packageGraph DBMS
Spatial DBMS
Time Series DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.61
Rank#240  Overall
#21  Graph DBMS
#35  Key-value stores
Score25.83
Rank#28  Overall
#1  Time Series DBMS
Score2.92
Rank#101  Overall
#18  Document stores
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitewww.graphengine.iowww.influxdata.com/­products/­influxdb-overviewravendb.netwww.timescale.com
Technical documentationwww.graphengine.io/­docs/­manualdocs.influxdata.com/­influxdbravendb.net/­docsdocs.timescale.com
DeveloperMicrosoftHibernating RhinosTimescale
Initial release2010201320102017
Current release2.7.6, April 20245.4, July 20222.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoMIT LicenseOpen Source infoMIT-License; commercial enterprise version availableOpen Source infoAGPL version 3, commercial license availableOpen Source infoApache 2.0
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 language.NET and CGoC#C
Server operating systems.NETLinux
OS X infothrough Homebrew
Linux
macOS
Raspberry Pi
Windows
Linux
OS X
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesNumeric data and Stringsnonumerics, 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.nonoyes
Secondary indexesnoyesyes
SQL infoSupport of SQLnoSQL-like query languageSQL-like query language (RQL)yes infofull PostgreSQL SQL syntax
APIs and other access methodsRESTful HTTP APIHTTP API
JSON over UDP
.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC#
C++
F#
Visual Basic
.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala
.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesnoyesuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersnonoyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningSharding infoin enterprise version onlyShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infoin enterprise version onlyMulti-source replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemDefault ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID, Cluster-wide transaction availableACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoDepending on used storage engineno
User concepts infoAccess controlsimple rights management via user accountsAuthorization levels configured per client per databasefine grained access rights according to SQL-standard
More information provided by the system vendor
Graph Engine infoformer name: TrinityInfluxDBRavenDBTimescaleDB
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
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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
Graph Engine infoformer name: TrinityInfluxDBRavenDBTimescaleDB
DB-Engines blog posts

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

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4 July 2016, Matthias Gelbmann

Time Series DBMS as a new trend?
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