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DBMS > Amazon DocumentDB vs. Hawkular Metrics vs. HBase vs. Manticore Search vs. Microsoft Azure Data Explorer

System Properties Comparison Amazon DocumentDB vs. Hawkular Metrics vs. HBase vs. Manticore Search vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonHBase  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceHawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.Wide-column store based on Apache Hadoop and on concepts of BigTableMulti-storage database for search, including full-text search.Fully managed big data interactive analytics platform
Primary database modelDocument storeTime Series DBMSWide column storeSearch engineRelational DBMS infocolumn oriented
Secondary database modelsTime Series DBMS infousing the Manticore Columnar LibraryDocument 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
Score1.91
Rank#132  Overall
#24  Document stores
Score0.00
Rank#379  Overall
#40  Time Series DBMS
Score30.50
Rank#26  Overall
#2  Wide column stores
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websiteaws.amazon.com/­documentdbwww.hawkular.orghbase.apache.orgmanticoresearch.comazure.microsoft.com/­services/­data-explorer
Technical documentationaws.amazon.com/­documentdb/­resourceswww.hawkular.org/­hawkular-metrics/­docs/­user-guidehbase.apache.org/­book.htmlmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperCommunity supported by Red HatApache Software Foundation infoApache top-level project, originally developed by PowersetManticore SoftwareMicrosoft
Initial release20192014200820172019
Current release2.3.4, January 20216.0, February 2023cloud service with continuous releases
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoApache version 2Open Source infoGPL version 2commercial
Cloud-based only infoOnly available as a cloud serviceyesnononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC++
Server operating systemshostedLinux
OS X
Windows
Linux
Unix
Windows infousing Cygwin
FreeBSD
Linux
macOS
Windows
hosted
Data schemeschema-freeschema-freeschema-free, schema definition possibleFixed schemaFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyesoptions to bring your own types, AVROInt, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Booleanyes 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.nononoCan index from XMLyes
Secondary indexesyesnonoyes infofull-text index on all search fieldsall fields are automatically indexed
SQL infoSupport of SQLnononoSQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)HTTP RESTJava API
RESTful HTTP API
Thrift
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Go
Java
Python
Ruby
C
C#
C++
Groovy
Java
PHP
Python
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnonoyes infoCoprocessors in Javauser defined functionsYes, possible languages: KQL, Python, R
Triggersnoyes infovia Hawkular Alertingyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infobased on CassandraShardingSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasselectable replication factor infobased on CassandraMulti-source replication
Source-replica replication
Synchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Immediate Consistency or Eventual ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possiblenononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsnoSingle row ACID (across millions of columns)yes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Manticore index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesno
User concepts infoAccess controlAccess rights for users and rolesnoAccess Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABACnoAzure Active Directory Authentication

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More resources
Amazon DocumentDBHawkular MetricsHBaseManticore SearchMicrosoft Azure Data Explorer
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