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DBMS > Google Cloud Bigtable vs. Hive vs. Manticore Search vs. PouchDB

System Properties Comparison Google Cloud Bigtable vs. Hive vs. Manticore Search vs. PouchDB

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Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparisonManticore Search  Xexclude from comparisonPouchDB  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 HadoopMulti-storage database for search, including full-text search.JavaScript DBMS with an API inspired by CouchDB
Primary database modelKey-value store
Wide column store
Relational DBMSSearch engineDocument store
Secondary database modelsTime Series DBMS infousing the Manticore Columnar Library
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score59.76
Rank#18  Overall
#12  Relational DBMS
Score0.29
Rank#302  Overall
#21  Search engines
Score2.34
Rank#112  Overall
#21  Document stores
Websitecloud.google.com/­bigtablehive.apache.orgmanticoresearch.compouchdb.com
Technical documentationcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Homemanual.manticoresearch.compouchdb.com/­guides
DeveloperGoogleApache Software Foundation infoinitially developed by FacebookManticore SoftwareApache Software Foundation
Initial release2015201220172012
Current release3.1.3, April 20226.0, February 20237.1.1, June 2019
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2Open Source infoGPL version 2Open Source
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++JavaScript
Server operating systemshostedAll OS with a Java VMFreeBSD
Linux
macOS
Windows
server-less, requires a JavaScript environment (browser, Node.js)
Data schemeschema-freeyesFixed schemaschema-free
Typing infopredefined data types such as float or datenoyesInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanno
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.noCan index from XMLno
Secondary indexesnoyesyes infofull-text index on all search fieldsyes infovia views
SQL infoSupport of SQLnoSQL-like DML and DDL statementsSQL-like query languageno
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
HTTP REST infoonly for PouchDB Server
JavaScript API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
JavaScript
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reduceuser defined functionsView functions in JavaScript
Triggersnononoyes
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infowith a proxy-based framework, named couchdb-lounge
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesselectable replication factorSynchronous replication based on Galera libraryMulti-source replication infoalso with CouchDB databases
Source-replica replication infoalso with CouchDB databases
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 ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoyes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Manticore index.yes infoby using IndexedDB, WebSQL or LevelDB as backend
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes
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 rolesnono

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More resources
Google Cloud BigtableHiveManticore SearchPouchDB
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