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DBMS > Heroic vs. LeanXcale vs. Manticore Search vs. Microsoft Azure Data Explorer

System Properties Comparison Heroic vs. LeanXcale vs. Manticore Search vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonLeanXcale  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionTime Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearchA highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilitiesMulti-storage database for search, including full-text search.Fully managed big data interactive analytics platform
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Search 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
Score0.57
Rank#250  Overall
#21  Time Series DBMS
Score0.35
Rank#283  Overall
#41  Key-value stores
#128  Relational DBMS
Score0.23
Rank#317  Overall
#21  Search engines
Score5.16
Rank#69  Overall
#37  Relational DBMS
Websitegithub.com/­spotify/­heroicwww.leanxcale.commanticoresearch.comazure.microsoft.com/­services/­data-explorer
Technical documentationspotify.github.io/­heroicmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperSpotifyLeanXcaleManticore SoftwareMicrosoft
Initial release2014201520172019
Current release6.0, February 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialOpen Source infoGPL version 2commercial
Cloud-based only infoOnly available as a cloud servicenononoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaC++
Server operating systemsFreeBSD
Linux
macOS
Windows
hosted
Data schemeschema-freeyesFixed schemaFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesInt, 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.noCan index from XMLyes
Secondary indexesyes infovia Elasticsearchyes infofull-text index on all search fieldsall fields are automatically indexed
SQL infoSupport of SQLnoyes infothrough Apache DerbySQL-like query languageKusto Query Language (KQL), SQL subset
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
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 languagesC
Java
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresnouser defined functionsYes, possible languages: KQL, Python, R
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesShardingSharding 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 nodesyesSynchronous 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 methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDyes infoisolated transactions for atomic changes and binary logging for safe writesno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes 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 controlnoAzure Active Directory Authentication

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More resources
HeroicLeanXcaleManticore SearchMicrosoft Azure Data Explorer
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