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DBMS > GridDB vs. Hazelcast vs. Manticore Search vs. Microsoft Azure Data Explorer vs. Prometheus

System Properties Comparison GridDB vs. Hazelcast vs. Manticore Search vs. Microsoft Azure Data Explorer vs. Prometheus

Editorial information provided by DB-Engines
NameGridDB  Xexclude from comparisonHazelcast  Xexclude from comparisonManticore Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPrometheus  Xexclude from comparison
DescriptionScalable in-memory time series database optimized for IoT and Big DataA widely adopted in-memory data gridMulti-storage database for search, including full-text search.Fully managed big data interactive analytics platformOpen-source Time Series DBMS and monitoring system
Primary database modelTime Series DBMSKey-value storeSearch engineRelational DBMS infocolumn orientedTime Series DBMS
Secondary database modelsKey-value store
Relational DBMS
Document store infoJSON support with IMDG 3.12Time 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
Score1.95
Rank#128  Overall
#10  Time Series DBMS
Score5.97
Rank#57  Overall
#6  Key-value stores
Score0.22
Rank#312  Overall
#21  Search engines
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score8.42
Rank#47  Overall
#2  Time Series DBMS
Websitegriddb.nethazelcast.commanticoresearch.comazure.microsoft.com/­services/­data-explorerprometheus.io
Technical documentationdocs.griddb.nethazelcast.org/­imdg/­docsmanual.manticoresearch.comdocs.microsoft.com/­en-us/­azure/­data-explorerprometheus.io/­docs
DeveloperToshiba CorporationHazelcastManticore SoftwareMicrosoft
Initial release20132008201720192015
Current release5.1, August 20225.3.6, November 20236.0, February 2023cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoAGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also availableOpen Source infoApache Version 2; commercial licenses availableOpen Source infoGPL version 2commercialOpen Source infoApache 2.0
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.
Implementation languageC++JavaC++Go
Server operating systemsLinuxAll OS with a Java VMFreeBSD
Linux
macOS
Windows
hostedLinux
Windows
Data schemeyesschema-freeFixed schemaFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyes infonumerical, string, blob, geometry, boolean, timestampyesInt, 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-typesNumeric data only
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.noyes infothe object must implement a serialization strategyCan index from XMLyesno infoImport of XML data possible
Secondary indexesyesyesyes infofull-text index on all search fieldsall fields are automatically indexedno
SQL infoSupport of SQLSQL92, SQL-like TQL (Toshiba Query Language)SQL-like query languageSQL-like query languageKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API
JCache
JPA
Memcached protocol
RESTful HTTP API
Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP/JSON API
Supported programming languagesC
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby
Server-side scripts infoStored proceduresnoyes infoEvent Listeners, Executor Servicesuser defined functionsYes, possible languages: KQL, Python, Rno
Triggersyesyes infoEventsnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding 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 nodesSource-replica replicationyes infoReplicated MapSynchronous replication based on Galera libraryyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infoby Federation
MapReduce infoOffers an API for user-defined Map/Reduce methodsConnector for using GridDB as an input source and output destination for Hadoop MapReduce jobsyesnoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency within container, eventual consistency across containersImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmEventual Consistency
Immediate Consistency
none
Foreign keys infoReferential integritynonononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID at container levelone or two-phase-commit; repeatable reads; read commitedyes 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.yesyesnono
User concepts infoAccess controlAccess rights for users can be defined per databaseRole-based access controlnoAzure Active Directory Authenticationno
More information provided by the system vendor
GridDBHazelcastManticore SearchMicrosoft Azure Data ExplorerPrometheus
Specific characteristicsGridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more
Competitive advantages1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more
Typical application scenariosFactory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more
Key customersDenso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more
Market metricsGitHub trending repository
» more
Licensing and pricing modelsOpen Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

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More resources
GridDBHazelcastManticore SearchMicrosoft Azure Data ExplorerPrometheus
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