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DBMS > Amazon DocumentDB vs. Apache Impala vs. Atos Standard Common Repository vs. Microsoft Azure AI Search vs. Postgres-XL

System Properties Comparison Amazon DocumentDB vs. Apache Impala vs. Atos Standard Common Repository vs. Microsoft Azure AI Search vs. Postgres-XL

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonApache Impala  Xexclude from comparisonAtos Standard Common Repository  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonPostgres-XL  Xexclude from comparison
This system has been discontinued and will be removed from the DB-Engines ranking.
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAnalytic DBMS for HadoopHighly scalable database system, designed for managing session and subscriber data in modern mobile communication networksSearch-as-a-service for web and mobile app developmentBased on PostgreSQL enhanced with MPP and write-scale-out cluster features
Primary database modelDocument storeRelational DBMSDocument store
Key-value store
Search engineRelational DBMS
Secondary database modelsDocument storeVector DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score5.59
Rank#63  Overall
#7  Search engines
Score0.49
Rank#256  Overall
#117  Relational DBMS
Websiteaws.amazon.com/­documentdbimpala.apache.orgatos.net/en/convergence-creators/portfolio/standard-common-repositoryazure.microsoft.com/­en-us/­services/­searchwww.postgres-xl.org
Technical documentationaws.amazon.com/­documentdb/­resourcesimpala.apache.org/­impala-docs.htmllearn.microsoft.com/­en-us/­azure/­searchwww.postgres-xl.org/­documentation
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaAtos Convergence CreatorsMicrosoft
Initial release20192013201620152014 infosince 2012, originally named StormDB
Current release4.1.0, June 20221703V110 R1, October 2018
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialcommercialOpen Source infoMozilla public license
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++JavaC
Server operating systemshostedLinuxLinuxhostedLinux
macOS
Data schemeschema-freeyesSchema and schema-less with LDAP viewsyesyes
Typing infopredefined data types such as float or dateyesyesoptionalyesyes
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.nonoyesnoyes infoXML type, but no XML query functionality
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLnoSQL-like DML and DDL statementsnonoyes infodistributed, parallel query execution
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)JDBC
ODBC
LDAPRESTful HTTP APIADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
All languages supporting JDBC/ODBCAll languages with LDAP bindingsC#
Java
JavaScript
Python
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresnoyes infouser defined functions and integration of map-reducenonouser defined functions
Triggersnonoyesnoyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infocell divisionSharding infoImplicit feature of the cloud servicehorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication factoryesyes infoImplicit feature of the cloud service
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)yes infoquery execution via MapReducenono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnoAtomic execution of specific operationsnoACID infoMVCC
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnono
User concepts infoAccess controlAccess rights for users and rolesAccess rights for users, groups and roles infobased on Apache Sentry and KerberosLDAP bind authenticationyes infousing Azure authenticationfine grained access rights according to SQL-standard

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More resources
Amazon DocumentDBApache ImpalaAtos Standard Common RepositoryMicrosoft Azure AI SearchPostgres-XL
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