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DBMS > Databend vs. Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SingleStore

System Properties Comparison Databend vs. Heroic vs. LeanXcale vs. Microsoft Azure Data Explorer vs. SingleStore

Editorial information provided by DB-Engines
NameDatabend  Xexclude from comparisonHeroic  Xexclude from comparisonLeanXcale  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSingleStore infoformer name was MemSQL  Xexclude from comparison
DescriptionAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityTime 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 platformMySQL wire-compliant distributed RDBMS that combines an in-memory row-oriented and a disc-based column-oriented storage with patented universal storage to handle transactional and analytical workloads in one single table type
Primary database modelRelational DBMSTime Series DBMSKey-value store
Relational DBMS
Relational DBMS infocolumn orientedRelational DBMS
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
Document store
Spatial DBMS
Time Series DBMS
Vector DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.34
Rank#283  Overall
#130  Relational DBMS
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
Score5.38
Rank#62  Overall
#35  Relational DBMS
Websitegithub.com/­datafuselabs/­databend
www.databend.com
github.com/­spotify/­heroicwww.leanxcale.comazure.microsoft.com/­services/­data-explorerwww.singlestore.com
Technical documentationdocs.databend.comspotify.github.io/­heroicdocs.microsoft.com/­en-us/­azure/­data-explorerdocs.singlestore.com
DeveloperDatabend LabsSpotifyLeanXcaleMicrosoftSingleStore Inc.
Initial release20212014201520192013
Current release1.0.59, April 2023cloud service with continuous releases8.5, January 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache 2.0commercialcommercialcommercial infofree developer edition available
Cloud-based only infoOnly available as a cloud servicenononoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
SingleStoreDB Cloud: The world's fastest, modern cloud database for both operational (OLTP) and analytical (OLAP) workloads. Available instantly with multi-cloud and hybrid-cloud capabilities
Implementation languageRustJavaC++, Go
Server operating systemshosted
Linux
macOS
hostedLinux info64 bit version required
Data schemeyesschema-freeyesFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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.nonoyesno
Secondary indexesnoyes infovia Elasticsearchall fields are automatically indexedyes
SQL infoSupport of SQLyesnoyes infothrough Apache DerbyKusto Query Language (KQL), SQL subsetyes infobut no triggers and foreign keys
APIs and other access methodsCLI Client
JDBC
RESTful HTTP API
HQL (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
Cluster Management API infoas HTTP Rest and CLI
HTTP API
JDBC
MongoDB API
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
Python
Rust
C
Java
Scala
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Bash
C
C#
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresnonoYes, possible languages: KQL, Python, Ryes
Triggersnonoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesnoneShardingSharding infoImplicit feature of the cloud serviceSharding infohash partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnoneyesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Source-replica replication infostores two copies of each physical data partition on two separate nodes
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno infocan define user-defined aggregate functions for map-reduce-style calculations
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datayesnoACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyesyes infoAll updates are persistent, including those to disk-based columnstores and memory-based row stores. Transaction commits are supported via write-ahead log.
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesnoyes
User concepts infoAccess controlUsers with fine-grained authorization concept, user rolesAzure Active Directory AuthenticationFine grained access control via users, groups and roles
More information provided by the system vendor
DatabendHeroicLeanXcaleMicrosoft Azure Data ExplorerSingleStore infoformer name was MemSQL
Specific characteristicsSingleStore offers a fully-managed , distributed, highly-scalable SQL database designed...
» more
Competitive advantagesSingleStore’s competitive advantages include: Easy and Simplified Architecture with...
» more
Typical application scenariosDriving Fast Analytics: SingleStore delivers the fastest and most scalable reporting...
» more
Key customersIEX Cloud : Improves Financial Data Distribution Speed 15x with Singlestore DB Comcast,...
» more
Market metricsCustomers in various industries worldwide including US and International Industry...
» more
Licensing and pricing modelsF ree Tier and Enterprise Edition
» more

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources
DatabendHeroicLeanXcaleMicrosoft Azure Data ExplorerSingleStore infoformer name was MemSQL
DB-Engines blog posts

Turbocharge Your Application Development Using WebAssembly With SingleStoreDB
17 October 2022,  Akmal Chaudhri, SingleStore (sponsor) 

Cloud-Based Analytics With SingleStoreDB
9 June 2022,  Akmal Chaudhri, SingleStore (sponsor) 

SingleStore: The Increasing Momentum of Multi-Model Database Systems
14 February 2022,  Akmal Chaudhri, SingleStore (sponsor) 

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