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DBMS > BaseX vs. Elasticsearch vs. Google Cloud Datastore vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer

System Properties Comparison BaseX vs. Elasticsearch vs. Google Cloud Datastore vs. Google Cloud Firestore vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameBaseX  Xexclude from comparisonElasticsearch  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonGoogle Cloud Firestore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionLight-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.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 metricAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformCloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.Fully managed big data interactive analytics platform
Primary database modelNative XML DBMSSearch engineDocument storeDocument storeRelational DBMS infocolumn oriented
Secondary database modelsDocument store
Spatial DBMS
Vector DBMS
Document store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.56
Rank#141  Overall
#4  Native XML DBMS
Score128.79
Rank#8  Overall
#1  Search engines
Score4.13
Rank#71  Overall
#12  Document stores
Score6.63
Rank#54  Overall
#9  Document stores
Score3.28
Rank#83  Overall
#45  Relational DBMS
Websitebasex.orgwww.elastic.co/­elasticsearchcloud.google.com/­datastorefirebase.google.com/­products/­firestoreazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.basex.orgwww.elastic.co/­guide/­en/­elasticsearch/­reference/­current/­index.htmlcloud.google.com/­datastore/­docsfirebase.google.com/­docs/­firestoredocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperBaseX GmbHElasticGoogleGoogleMicrosoft
Initial release20072010200820172019
Current release11.2, August 20248.6, January 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoBSD licenseOpen Source infoElastic Licensecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava
Server operating systemsLinux
OS X
Windows
All OS with a Java VMhostedhostedhosted
Data schemeschema-freeschema-free infoFlexible type definitions. Once a type is defined, it is persistentschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateno infoXQuery supports typesyesyes, details hereyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.nononoyes
Secondary indexesyesyes infoAll search fields are automatically indexedyesyesall fields are automatically indexed
SQL infoSupport of SQLnoSQL-like query languageSQL-like query language (GQL)noKusto Query Language (KQL), SQL subset
APIs and other access methodsJava API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ
Java API
RESTful HTTP/JSON API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesActionscript
C
C#
Haskell
Java
JavaScript infoNode.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic
.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresyesyesusing Google App Engineyes, Firebase Rules & Cloud FunctionsYes, possible languages: KQL, Python, R
Triggersyes infovia eventsyes infoby using the 'percolation' featureCallbacks using the Google Apps Engineyes, with Cloud Functionsyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesMulti-source replication using PaxosMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoES-Hadoop Connectoryes infousing Google Cloud DataflowUsing Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency infoSynchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, allImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datamultiple readers, single writernoACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsyesno
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.Memcached and Redis integrationnono
User concepts infoAccess controlUsers with fine-grained authorization concept on 4 levelsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.Azure Active Directory Authentication

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More resources
BaseXElasticsearchGoogle Cloud DatastoreGoogle Cloud FirestoreMicrosoft Azure Data Explorer
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