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DBMS > Amazon DocumentDB vs. HarperDB vs. Hive

System Properties Comparison Amazon DocumentDB vs. HarperDB vs. Hive

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHarperDB  Xexclude from comparisonHive  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceUltra-low latency distributed database with an intuitive REST API supporting NoSQL and SQL (including joins). Deployment of functions and databases simultaneously with a consolidated node-level architecture.data warehouse software for querying and managing large distributed datasets, built on Hadoop
Primary database modelDocument storeDocument storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score0.60
Rank#244  Overall
#38  Document stores
Score59.76
Rank#18  Overall
#12  Relational DBMS
Websiteaws.amazon.com/­documentdbwww.harperdb.iohive.apache.org
Technical documentationaws.amazon.com/­documentdb/­resourcesdocs.harperdb.io/­docscwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperHarperDBApache Software Foundation infoinitially developed by Facebook
Initial release201920172012
Current release3.1, August 20213.1.3, April 2022
License infoCommercial or Open Sourcecommercialcommercial infofree community edition availableOpen Source infoApache Version 2
Cloud-based only infoOnly available as a cloud serviceyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageNode.jsJava
Server operating systemshostedLinux
OS X
All OS with a Java VM
Data schemeschema-freedynamic schemayes
Typing infopredefined data types such as float or dateyesyes infoJSON data typesyes
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.nono
Secondary indexesyesyesyes
SQL infoSupport of SQLnoSQL-like data manipulation statementsSQL-like DML and DDL statements
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
React Hooks
RESTful HTTP/JSON API
WebSocket
JDBC
ODBC
Thrift
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
.Net
C
C#
C++
ColdFusion
D
Dart
Delphi
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Lisp
MatLab
Objective C
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Rust
Scala
Swift
C++
Java
PHP
Python
Server-side scripts infoStored proceduresnoCustom Functions infosince release 3.1yes infouser defined functions and integration of map-reduce
Triggersnonono
Partitioning methods infoMethods for storing different data on different nodesnoneA table resides as a whole on one (or more) nodes in a clusterSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyes infothe nodes on which a table resides can be definedselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noyes infoquery execution via MapReduce
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsAtomic execution of specific operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes, using LMDByes
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 and rolesAccess rights for users, groups and roles

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Amazon DocumentDBHarperDBHive
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