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DBMS > GreptimeDB vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Sphinx vs. Splice Machine

System Properties Comparison GreptimeDB vs. HEAVY.AI vs. Microsoft Azure Data Explorer vs. Sphinx vs. Splice Machine

Editorial information provided by DB-Engines
NameGreptimeDB  Xexclude from comparisonHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSphinx  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionAn open source Time Series DBMS built for increased scalability, high performance and efficiencyA high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardwareFully managed big data interactive analytics platformOpen source search engine for searching in data from different sources, e.g. relational databasesOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelTime Series DBMSRelational DBMSRelational DBMS infocolumn orientedSearch engineRelational DBMS
Secondary database modelsSpatial DBMSDocument 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
Score0.12
Rank#351  Overall
#34  Time Series DBMS
Score1.64
Rank#145  Overall
#67  Relational DBMS
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score5.95
Rank#55  Overall
#5  Search engines
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitegreptime.comgithub.com/­heavyai/­heavydb
www.heavy.ai
azure.microsoft.com/­services/­data-explorersphinxsearch.comsplicemachine.com
Technical documentationdocs.greptime.comdocs.heavy.aidocs.microsoft.com/­en-us/­azure/­data-explorersphinxsearch.com/­docssplicemachine.com/­how-it-works
DeveloperGreptime Inc.HEAVY.AI, Inc.MicrosoftSphinx Technologies Inc.Splice Machine
Initial release20222016201920012014
Current release5.10, January 2022cloud service with continuous releases3.5.1, February 20233.1, March 2021
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2; enterprise edition availablecommercialOpen Source infoGPL version 2, commercial licence availableOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageRustC++ and CUDAC++Java
Server operating systemsAndroid
Docker
FreeBSD
Linux
macOS
Windows
LinuxhostedFreeBSD
Linux
NetBSD
OS X
Solaris
Windows
Linux
OS X
Solaris
Windows
Data schemeschema-free, schema definition possibleyesFixed schema with schema-less datatypes (dynamic)yesyes
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-typesnoyes
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.nonoyes
Secondary indexesyesnoall fields are automatically indexedyes infofull-text index on all search fieldsyes
SQL infoSupport of SQLyesyesKusto Query Language (KQL), SQL subsetSQL-like query language (SphinxQL)yes
APIs and other access methodsgRPC
HTTP API
JDBC
JDBC
ODBC
Thrift
Vega
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Proprietary protocolJDBC
Native Spark Datasource
ODBC
Supported programming languagesC++
Erlang
Go
Java
JavaScript
All languages supporting JDBC/ODBC/Thrift
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C++ infounofficial client library
Java
Perl infounofficial client library
PHP
Python
Ruby infounofficial client library
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresPythonnoYes, possible languages: KQL, Python, Rnoyes infoJava
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicynoyes
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoRound robinSharding infoImplicit feature of the cloud serviceSharding infoPartitioning is done manually, search queries against distributed index is supportedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replicationyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyesyes infoThe original contents of fields are not stored in the Sphinx index.yes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlSimple rights management via user accountsfine grained access rights according to SQL-standardAzure Active Directory AuthenticationnoAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
GreptimeDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerSphinxSplice Machine
Specific characteristicsGreptimeDB is a SQL & Python-enabled timeseries database system built from scratch...
» more
Competitive advantages- Inherits advantages of Rust, such as excellent performance, memory safe, resource...
» more
Typical application scenariosFor IoT industries, GreptimeDB can seamless integrate with message queues and other...
» more
Key customersGreptime's clients span multiple sectors including IoT, connected vehicles, and energy...
» more
Market metricsGreptimeDB has garnered global recognition by topping GitHub trends following its...
» more
Licensing and pricing modelsGreptimeDB: open source, distributed, cloud-native TSDB; supports Hybrid Time-series...
» more

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GreptimeDBHEAVY.AI infoFormerly named 'OmniSci', rebranded to 'HEAVY.AI' in March 2022Microsoft Azure Data ExplorerSphinxSplice Machine
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