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DBMS > Google Cloud Bigtable vs. GridDB vs. NSDb vs. TDengine

System Properties Comparison Google Cloud Bigtable vs. GridDB vs. NSDb vs. TDengine

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonGridDB  Xexclude from comparisonNSDb  Xexclude from comparisonTDengine  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Scalable in-memory time series database optimized for IoT and Big DataScalable, High-performance Time Series DBMS designed for Real-time Analytics on top of KubernetesTime Series DBMS and big data platform
Primary database modelKey-value store
Wide column store
Time Series 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
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score0.00
Rank#383  Overall
#41  Time Series DBMS
Score2.60
Rank#107  Overall
#8  Time Series DBMS
Websitecloud.google.com/­bigtablegriddb.netnsdb.iogithub.com/­taosdata/­TDengine
tdengine.com
Technical documentationcloud.google.com/­bigtable/­docsdocs.griddb.netnsdb.io/­Architecturedocs.tdengine.com
DeveloperGoogleToshiba CorporationTDEngine, previously Taos Data
Initial release2015201320172019
Current release5.1, August 20223.0, August 2022
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2.0Open Source infoAGPL V3, also commercial editions available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++Java, ScalaC
Server operating systemshostedLinuxLinux
macOS
Linux
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or datenoyes infonumerical, string, blob, geometry, boolean, timestampyes: int, bigint, decimal, stringyes
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.nononono
Secondary indexesnoyesall fields are automatically indexedno
SQL infoSupport of SQLnoSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query languageStandard SQL with extensions for time-series applications
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
gRPC
HTTP REST
WebSocket
JDBC
RESTful HTTP API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Java
Scala
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
Rust
Server-side scripts infoStored proceduresnononono
Triggersnoyesyes, via alarm monitoring
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationyes
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate consistency within container, eventual consistency across containersEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID at container levelno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesUsing Apache Luceneyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per databaseyes
More information provided by the system vendor
Google Cloud BigtableGridDBNSDbTDengine
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
TDengine™ is a next generation data historian purpose-built for Industry 4.0 and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
High Performance at any Scale: TDengine is purpose-built for handling massive industrial...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
TDengine is designed for Industrial IoT scenarios, including: Manufacturing Connected...
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
TDengine has garnered over 22,500 stars on GitHub and is used in over 50 countries...
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more
TDengine OSS is an open source, cloud native time series database. It includes built-in...
» 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
Google Cloud BigtableGridDBNSDbTDengine
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