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DBMS > Badger vs. Google Cloud Datastore vs. Graph Engine vs. Kinetica

System Properties Comparison Badger vs. Google Cloud Datastore vs. Graph Engine vs. Kinetica

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Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGraph Engine infoformer name: Trinity  Xexclude from comparisonKinetica  Xexclude from comparison
DescriptionAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engineFully vectorized database across both GPUs and CPUs
Primary database modelKey-value storeDocument storeGraph DBMS
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.22
Rank#320  Overall
#47  Key-value stores
Score4.36
Rank#72  Overall
#12  Document stores
Score0.67
Rank#232  Overall
#21  Graph DBMS
#34  Key-value stores
Score0.66
Rank#234  Overall
#107  Relational DBMS
Websitegithub.com/­dgraph-io/­badgercloud.google.com/­datastorewww.graphengine.iowww.kinetica.com
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­datastore/­docswww.graphengine.io/­docs/­manualdocs.kinetica.com
DeveloperDGraph LabsGoogleMicrosoftKinetica
Initial release2017200820102012
Current release7.1, August 2021
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoMIT Licensecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGo.NET and CC, C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hosted.NETLinux
Data schemeschema-freeschema-freeyesyes
Typing infopredefined data types such as float or datenoyes, details hereyesyes
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 indexesnoyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)noSQL-like DML and DDL statements
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
RESTful HTTP APIJDBC
ODBC
RESTful HTTP API
Supported programming languagesGo.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
F#
Visual Basic
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnousing Google App Engineyesuser defined functions
TriggersnoCallbacks using the Google Apps Enginenoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesnoneShardinghorizontal partitioningSharding
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using PaxosSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflowno
Consistency concepts infoMethods to ensure consistency in a distributed systemnoneImmediate 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 or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesoptional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storageyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes infoGPU vRAM or System RAM
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table level

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More resources
BadgerGoogle Cloud DatastoreGraph Engine infoformer name: TrinityKinetica
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