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DBMS > Amazon DocumentDB vs. Badger vs. Elasticsearch vs. Microsoft Azure AI Search vs. Splice Machine

System Properties Comparison Amazon DocumentDB vs. Badger vs. Elasticsearch vs. Microsoft Azure AI Search vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonBadger  Xexclude from comparisonElasticsearch  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.A distributed, RESTful modern search and analytics engine based on Apache Lucene infoElasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metricSearch-as-a-service for web and mobile app developmentOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeKey-value storeSearch engineSearch engineRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#131  Overall
#24  Document stores
Score0.22
Rank#320  Overall
#47  Key-value stores
Score132.83
Rank#7  Overall
#1  Search engines
Score5.52
Rank#59  Overall
#6  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteaws.amazon.com/­documentdbgithub.com/­dgraph-io/­badgerwww.elastic.co/­elasticsearchazure.microsoft.com/­en-us/­services/­searchsplicemachine.com
Technical documentationaws.amazon.com/­documentdb/­resourcesgodoc.org/­github.com/­dgraph-io/­badgerwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmllearn.microsoft.com/­en-us/­azure/­searchsplicemachine.com/­how-it-works
DeveloperDGraph LabsElasticMicrosoftSplice Machine
Initial release20192017201020152014
Current release8.6, January 2023V13.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoElastic LicensecommercialOpen Source infoAGPL 3.0, commercial license available
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 languageGoJavaJava
Server operating systemshostedBSD
Linux
OS X
Solaris
Windows
All OS with a Java VMhostedLinux
OS X
Solaris
Windows
Data schemeschema-freeschema-freeschema-free infoFlexible type definitions. Once a type is defined, it is persistentyesyes
Typing infopredefined data types such as float or dateyesnoyesyesyes
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 indexesyesnoyes infoAll search fields are automatically indexedyesyes
SQL infoSupport of SQLnonoSQL-like query languagenoyes
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)Java API
RESTful HTTP/JSON API
RESTful HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Go.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
C#
Java
JavaScript
Python
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnonoyesnoyes infoJava
Triggersnonoyes infoby using the 'percolation' featurenoyes
Partitioning methods infoMethods for storing different data on different nodesnonenoneShardingSharding infoImplicit feature of the cloud serviceShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasnoneyesyes infoImplicit feature of the cloud serviceMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noES-Hadoop ConnectornoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencynoneEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate 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 operationsnononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
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.noMemcached and Redis integrationnoyes
User concepts infoAccess controlAccess rights for users and rolesnoyes infousing Azure authenticationAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon DocumentDBBadgerElasticsearchMicrosoft Azure AI SearchSplice Machine
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