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DBMS > Badger vs. Google Cloud Datastore vs. KeyDB vs. Kinetica vs. searchxml

System Properties Comparison Badger vs. Google Cloud Datastore vs. KeyDB vs. Kinetica vs. searchxml

Editorial information provided by DB-Engines
NameBadger  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKeyDB  Xexclude from comparisonKinetica  Xexclude from comparisonsearchxml  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 PlatformAn ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocolsFully vectorized database across both GPUs and CPUsDBMS for structured and unstructured content wrapped with an application server
Primary database modelKey-value storeDocument storeKey-value storeRelational DBMSNative XML DBMS
Search engine
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.14
Rank#331  Overall
#49  Key-value stores
Score4.47
Rank#76  Overall
#12  Document stores
Score0.71
Rank#226  Overall
#33  Key-value stores
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.00
Rank#383  Overall
#7  Native XML DBMS
#25  Search engines
Websitegithub.com/­dgraph-io/­badgercloud.google.com/­datastoregithub.com/­Snapchat/­KeyDB
keydb.dev
www.kinetica.comwww.searchxml.net/­category/­products
Technical documentationgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­datastore/­docsdocs.keydb.devdocs.kinetica.comwww.searchxml.net/­support/­handouts
DeveloperDGraph LabsGoogleEQ Alpha Technology Ltd.Kineticainformationpartners gmbh
Initial release20172008201920122015
Current release7.1, August 20211.0
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoBSD-3commercialcommercial
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageGoC++C, C++C++
Server operating systemsBSD
Linux
OS X
Solaris
Windows
hostedLinuxLinuxWindows
Data schemeschema-freeschema-freeschema-freeyesschema-free
Typing infopredefined data types such as float or datenoyes, details herepartial infoSupported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexesyesyes
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.nonononoyes
Secondary indexesnoyesyes infoby using the Redis Search moduleyesyes
SQL infoSupport of SQLnoSQL-like query language (GQL)noSQL-like DML and DDL statementsno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Proprietary protocol infoRESP - REdis Serialization ProtocoJDBC
ODBC
RESTful HTTP API
RESTful HTTP API
WebDAV
XQuery
XSLT
Supported programming languagesGo.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic
C++
Java
JavaScript (Node.js)
Python
C++ infomost other programming languages supported via APIs
Server-side scripts infoStored proceduresnousing Google App EngineLuauser defined functionsyes infoon the application server
TriggersnoCallbacks using the Google Apps Enginenoyes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesnoneMulti-source replication using PaxosMulti-source replication
Source-replica replication
Source-replica replicationyes infosychronisation to multiple collections
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownonono
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.Eventual Consistency
Strong eventual consistency with CRDTs
Immediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsOptimistic locking, atomic execution of commands blocks and scriptsnomultiple readers, single writer
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoConfigurable mechanisms for persistency via snapshots and/or operations logsyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)simple password-based access control and ACLAccess rights for users and roles on table levelDomain, group and role-based access control at the document level and for application services

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More resources
BadgerGoogle Cloud DatastoreKeyDBKineticasearchxml
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