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DBMS > Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SpatiaLite vs. Vertica

System Properties Comparison Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SpatiaLite vs. Vertica

Editorial information provided by DB-Engines
NameHeroic  Xexclude from comparisonLeanXcale  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSpatiaLite  Xexclude from comparisonVertica infoOpenText™ Vertica™  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 capabilitiesFully managed big data interactive analytics platformSpatial extension of SQLiteCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Relational DBMS infocolumn orientedSpatial DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument 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
Relational DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.46
Rank#265  Overall
#22  Time Series DBMS
Score0.36
Rank#280  Overall
#40  Key-value stores
#129  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score1.63
Rank#146  Overall
#3  Spatial DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitegithub.com/­spotify/­heroicwww.leanxcale.comazure.microsoft.com/­services/­data-explorerwww.gaia-gis.it/­fossil/­libspatialite/­indexwww.vertica.com
Technical documentationspotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorerwww.gaia-gis.it/­gaia-sins/­spatialite_topics.htmlvertica.com/­documentation
DeveloperSpotifyLeanXcaleMicrosoftAlessandro FurieriOpenText infopreviously Micro Focus and Hewlett Packard
Initial release20142015201920082005
Current releasecloud service with continuous releases5.0.0, August 202012.0.3, January 2023
License infoCommercial or Open SourceOpen Source infoApache 2.0commercialcommercialOpen Source infoMPL 1.1, GPL v2.0 or LGPL v2.1commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenonoyesnono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++
Server operating systemshostedserver-lessLinux
Data schemeschema-freeyesFixed schema with schema-less datatypes (dynamic)yesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes
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.noyesnono
Secondary indexesyes infovia Elasticsearchall fields are automatically indexedyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoyes infothrough Apache DerbyKusto Query Language (KQL), SQL subsetyesFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsHQL (Heroic Query Language, a JSON-based language)
HTTP API
JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesC
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, Rnoyes, PostgreSQL PL/pgSQL, with minor differences
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud servicenonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynoyesnoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnoACIDACID
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.noyesnoyesno
User concepts infoAccess controlAzure Active Directory Authenticationnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
HeroicLeanXcaleMicrosoft Azure Data ExplorerSpatiaLiteVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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HeroicLeanXcaleMicrosoft Azure Data ExplorerSpatiaLiteVertica infoOpenText™ Vertica™
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