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DBMS > Blueflood vs. EsgynDB vs. Google Cloud Datastore vs. GridDB

System Properties Comparison Blueflood vs. EsgynDB vs. Google Cloud Datastore vs. GridDB

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Editorial information provided by DB-Engines
NameBlueflood  Xexclude from comparisonEsgynDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGridDB  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on CassandraEnterprise-class SQL-on-Hadoop solution, powered by Apache TrafodionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformScalable in-memory time series database optimized for IoT and Big Data
Primary database modelTime Series DBMSRelational DBMSDocument storeTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.13
Rank#346  Overall
#33  Time Series DBMS
Score0.25
Rank#312  Overall
#138  Relational DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score2.09
Rank#120  Overall
#10  Time Series DBMS
Websiteblueflood.iowww.esgyn.cncloud.google.com/­datastoregriddb.net
Technical documentationgithub.com/­rax-maas/­blueflood/­wikicloud.google.com/­datastore/­docsdocs.griddb.net
DeveloperRackspaceEsgynGoogleToshiba Corporation
Initial release2013201520082013
Current release5.1, August 2022
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++, JavaC++
Server operating systemsLinux
OS X
LinuxhostedLinux
Data schemepredefined schemeyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyes infonumerical, string, blob, geometry, boolean, timestamp
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 indexesnoyesyesyes
SQL infoSupport of SQLnoyesSQL-like query language (GQL)SQL92, SQL-like TQL (Toshiba Query Language)
APIs and other access methodsHTTP RESTADO.NET
JDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
Supported programming languagesAll languages supporting JDBC/ODBC/ADO.Net.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
Server-side scripts infoStored proceduresnoJava Stored Proceduresusing Google App Engineno
TriggersnonoCallbacks using the Google Apps Engineyes
Partitioning methods infoMethods for storing different data on different nodesSharding infobased on CassandraShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication factor infobased on CassandraMulti-source replication between multi datacentersMulti-source replication using PaxosSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infousing Google Cloud DataflowConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobs
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate consistency within container, eventual consistency across containers
Foreign keys infoReferential integritynoyesyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID at container level
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.nononoyes
User concepts infoAccess controlnofine grained access rights according to SQL-standardAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users can be defined per database
More information provided by the system vendor
BluefloodEsgynDBGoogle Cloud DatastoreGridDB
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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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
BluefloodEsgynDBGoogle Cloud DatastoreGridDB
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