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DBMS > Google Cloud Bigtable vs. Hive vs. ObjectBox vs. Riak KV

System Properties Comparison Google Cloud Bigtable vs. Hive vs. ObjectBox vs. Riak KV

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparisonObjectBox  Xexclude from comparisonRiak KV  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.data warehouse software for querying and managing large distributed datasets, built on HadoopExtremely fast embedded database for small devices, IoT and MobileDistributed, fault tolerant key-value store
Primary database modelKey-value store
Wide column store
Relational DBMSObject oriented DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
Secondary database modelsTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.58
Rank#92  Overall
#14  Key-value stores
#8  Wide column stores
Score62.59
Rank#18  Overall
#12  Relational DBMS
Score1.22
Rank#172  Overall
#5  Object oriented DBMS
Score4.44
Rank#80  Overall
#9  Key-value stores
Websitecloud.google.com/­bigtablehive.apache.orgobjectbox.io
Technical documentationcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Homedocs.objectbox.iowww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperGoogleApache Software Foundation infoinitially developed by FacebookObjectBox LimitedOpenSource, formerly Basho Technologies
Initial release2015201220172009
Current release3.1.3, April 20223.2.0, December 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoApache License 2.0Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC and C++Erlang
Server operating systemshostedAll OS with a Java VMAndroid
iOS
Linux
macOS
Windows
Linux
OS X
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or datenoyesyesno
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.nonono
Secondary indexesnoyesyesrestricted
SQL infoSupport of SQLnoSQL-like DML and DDL statementsnono
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
Proprietary native APIHTTP API
Native Erlang Interface
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
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 infouser defined functions and integration of map-reducenoErlang
Triggersnononoyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesShardingShardingnoneSharding infono "single point of failure"
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factoronline/offline synchronization between client and serverselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infoquery execution via MapReducenoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynonoyesno infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACIDno
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.nono
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesyesyes, using Riak Security
More information provided by the system vendor
Google Cloud BigtableHiveObjectBoxRiak KV
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13 June 2023

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