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DBMS > AgensGraph vs. Databend vs. GridDB vs. TimescaleDB

System Properties Comparison AgensGraph vs. Databend vs. GridDB vs. TimescaleDB

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Editorial information provided by DB-Engines
NameAgensGraph  Xexclude from comparisonDatabend  Xexclude from comparisonGridDB  Xexclude from comparisonTimescaleDB  Xexclude from comparison
DescriptionMulti-model database supporting relational and graph data models and built upon PostgreSQLAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityScalable in-memory time series database optimized for IoT and Big DataA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelGraph DBMS
Relational DBMS
Relational DBMSTime Series DBMSTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.20
Rank#321  Overall
#27  Graph DBMS
#143  Relational DBMS
Score0.30
Rank#287  Overall
#130  Relational DBMS
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score4.64
Rank#71  Overall
#4  Time Series DBMS
Websitebitnine.net/­agensgraphgithub.com/­datafuselabs/­databend
www.databend.com
griddb.netwww.timescale.com
Technical documentationbitnine.net/­documentationdocs.databend.comdocs.griddb.netdocs.timescale.com
DeveloperBitnine Global Inc.Databend LabsToshiba CorporationTimescale
Initial release2016202120132017
Current release2.1, December 20181.0.59, April 20235.1, August 20222.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache License 2.0Open Source infoApache Version 2.0Open Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also 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 languageCRustC++C
Server operating systemsLinux
OS X
Windows
hosted
Linux
macOS
LinuxLinux
OS X
Windows
Data schemedepending on used data modelyesyesyes
Typing infopredefined data types such as float or dateyesyesyes infonumerical, string, blob, geometry, boolean, timestampnumerics, 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.nononoyes
Secondary indexesyesnoyesyes
SQL infoSupport of SQLyesyesSQL92, SQL-like TQL (Toshiba Query Language)yes infofull PostgreSQL SQL syntax
APIs and other access methodsCypher Query Language
JDBC
CLI Client
JDBC
RESTful HTTP API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
Java
JavaScript
Python
Go
Java
JavaScript (Node.js)
Python
Rust
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesnonouser 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 nodesno, but can be realized using table inheritancenoneShardingyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationnoneSource-replica replicationSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate consistency within container, eventual consistency across containersImmediate Consistency
Foreign keys infoReferential integrityyesnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDyesACID at container levelACID
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.noyesno
User concepts infoAccess controlfine grained access rights according to SQL-standardUsers with fine-grained authorization concept, user rolesAccess rights for users can be defined per databasefine grained access rights according to SQL-standard
More information provided by the system vendor
AgensGraphDatabendGridDBTimescaleDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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AgensGraphDatabendGridDBTimescaleDB
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