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

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

Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonHawkular Metrics  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonWeaviate  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.Multi-storage database for search, including full-text search.Fully managed big data interactive analytics platformAn AI-native realtime vector database engine that integrates scalable machine learning models.
Primary database modelDocument storeTime Series DBMSSearch engineRelational DBMS infocolumn orientedVector DBMS
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
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score1.73
Rank#143  Overall
#5  Vector DBMS
Websiteaws.amazon.com/­documentdbwww.hawkular.orgmanticoresearch.comazure.microsoft.com/­services/­data-explorergithub.com/­weaviate/­weaviate
weaviate.io
Technical documentationaws.amazon.com/­documentdb/­resourceswww.hawkular.org/­hawkular-metrics/­docs/­user-guidemanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorerweaviate.io/­developers/­weaviate
DeveloperCommunity supported by Red HatManticore SoftwareMicrosoftWeaviate B.V.
Initial release20192014201720192019
Current release6.0, February 2023cloud service with continuous releases1.19, May 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoGPL version 2commercialOpen Source infocommercial license available with Weaviate Enterprise
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 languageJavaC++Go
Server operating systemshostedLinux
OS X
Windows
FreeBSD
Linux
macOS
Windows
hosted
Data schemeschema-freeschema-freeFixed schemaFixed schema with schema-less datatypes (dynamic)yes, maps to GraphQL interface
Typing infopredefined data types such as float or dateyesyesInt, 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-typesyes infostring, int, float, geo point, date, cross reference, fuzzy references
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.nonoCan index from XMLyesno
Secondary indexesyesnoyes infofull-text index on all search fieldsall fields are automatically indexedyes infoall data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed
SQL infoSupport of SQLnonoSQL-like query languageKusto Query Language (KQL), SQL subsetGraphQL is used as query language
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible)HTTP RESTBinary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
GraphQL query language
RESTful HTTP/JSON API
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Go
Java
Python
Ruby
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
JavaScript / TypeScript
Python
Server-side scripts infoStored proceduresnonouser defined functionsYes, possible languages: KQL, Python, Rno
Triggersnoyes infovia Hawkular Alertingnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infobased on CassandraSharding infoPartitioning is done manually, search queries against distributed index is supportedSharding infoImplicit feature of the cloud serviceSharding
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 CassandraSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)nonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency infobased on Cassandra
Immediate Consistency infobased on Cassandra
Eventual Consistency
Immediate Consistency
Eventual 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 operationsnoyes infoisolated transactions for atomic changes and binary logging for safe writesnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infoThe original contents of fields are not stored in the Manticore index.yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlAccess rights for users and rolesnonoAzure Active Directory AuthenticationAPI Keys
OpenID Connect Discovery
More information provided by the system vendor
Amazon DocumentDBHawkular MetricsManticore SearchMicrosoft Azure Data ExplorerWeaviate
Specific characteristicsWeaviate is an open source vector database that is robust, scalable, cloud-native,...
» more
Competitive advantagesFlexible deployment - Free, open source or fully-managed cloud vector database service...
» more
Typical application scenariosAs a database supporting the development of generative AI and semantic search applications...
» more
Key customersAll companies that have data. ​
» more
Market metricsAs of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more
Licensing and pricing modelsWeaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» more

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More resources
Amazon DocumentDBHawkular MetricsManticore SearchMicrosoft Azure Data ExplorerWeaviate
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