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DBMS > Amazon DocumentDB vs. Google Cloud Firestore vs. HBase vs. Riak KV

System Properties Comparison Amazon DocumentDB vs. Google Cloud Firestore vs. HBase vs. Riak KV

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonHBase  Xexclude from comparisonRiak KV  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Wide-column store based on Apache Hadoop and on concepts of BigTableDistributed, fault tolerant key-value store
Primary database modelDocument storeDocument storeWide column storeKey-value store infowith links between data sets and object tags for the creation of secondary indexes
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score7.85
Rank#51  Overall
#8  Document stores
Score30.50
Rank#26  Overall
#2  Wide column stores
Score4.10
Rank#82  Overall
#9  Key-value stores
Websiteaws.amazon.com/­documentdbfirebase.google.com/­products/­firestorehbase.apache.org
Technical documentationaws.amazon.com/­documentdb/­resourcesfirebase.google.com/­docs/­firestorehbase.apache.org/­book.htmlwww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperGoogleApache Software Foundation infoApache top-level project, originally developed by PowersetOpenSource, formerly Basho Technologies
Initial release2019201720082009
Current release2.3.4, January 20213.2.0, December 2022
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache version 2Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaErlang
Server operating systemshostedhostedLinux
Unix
Windows infousing Cygwin
Linux
OS X
Data schemeschema-freeschema-freeschema-free, schema definition possibleschema-free
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROno
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 indexesyesyesnorestricted
SQL infoSupport of SQLnononono
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Java API
RESTful HTTP API
Thrift
HTTP API
Native Erlang Interface
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
C
C#
C++
Groovy
Java
PHP
Python
Scala
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresnoyes, Firebase Rules & Cloud Functionsyes infoCoprocessors in JavaErlang
Triggersnoyes, with Cloud Functionsyesyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasMulti-source replicationMulti-source replication
Source-replica replication
selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)Using Cloud Dataflowyesyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenonono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsyesSingle row ACID (across millions of columns)no
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.yes
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACyes, using Riak Security

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More resources
Amazon DocumentDBGoogle Cloud FirestoreHBaseRiak KV
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