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DBMS > Google Cloud Bigtable vs. GridDB vs. InterSystems IRIS vs. LMDB

System Properties Comparison Google Cloud Bigtable vs. GridDB vs. InterSystems IRIS vs. LMDB

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonGridDB  Xexclude from comparisonInterSystems IRIS  Xexclude from comparisonLMDB  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 DataA containerised multi-model DBMS, interoperability and analytics data platform with wide capabilities for vertical and horizontal scalabilityA high performant, light-weight, embedded key-value database library
Primary database modelKey-value store
Wide column store
Time Series DBMSDocument store
Key-value store
Object oriented DBMS
Relational DBMS
Key-value store
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Score3.54
Rank#84  Overall
#14  Document stores
#10  Key-value stores
#1  Object oriented DBMS
#45  Relational DBMS
Score2.09
Rank#121  Overall
#20  Key-value stores
Websitecloud.google.com/­bigtablegriddb.netwww.intersystems.com/­products/­intersystems-iriswww.symas.com/­symas-embedded-database-lmdb
Technical documentationcloud.google.com/­bigtable/­docsdocs.griddb.netdocs.intersystems.com/­irislatest/­csp/­docbook/­DocBook.UI.Page.clswww.lmdb.tech/­doc
DeveloperGoogleToshiba CorporationInterSystemsSymas
Initial release2015201320182011
Current release5.1, August 20222023.3, June 20230.9.32, January 2024
License infoCommercial or Open SourcecommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availablecommercialOpen Source
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++C
Server operating systemshostedLinuxAIX
Linux
macOS
Ubuntu
Windows
Linux
Unix
Windows
Data schemeschema-freeyesdepending on used data modelschema-free
Typing infopredefined data types such as float or datenoyes infonumerical, string, blob, geometry, boolean, timestampyes
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.nonoyesno
Secondary indexesnoyesyesno
SQL infoSupport of SQLnoSQL92, SQL-like TQL (Toshiba Query Language)yesno
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JDBC
ODBC
RESTful HTTP API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C++
Java
JavaScript (Node.js)
Python
.Net
C
C++
Clojure
Go
Haskell
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Nim
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Rust
Swift
Tcl
Server-side scripts infoStored proceduresnonoyesno
Triggersnoyesyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesSource-replica replicationSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsnono
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 containersImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsACID at container levelACIDACID
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.noyesyesyes
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 databaseyesno
More information provided by the system vendor
Google Cloud BigtableGridDBInterSystems IRISLMDB
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
InterSystems IRIS is a complete cloud-first data platform which includes a multi-model...
» 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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
Google Cloud BigtableGridDBInterSystems IRISLMDB
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