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Recommended Use Cases

General Scenario

ByConity uses a large number of mature OLAP technologies, such as column storage engine, MPP execution, intelligent query optimization, vectorized execution, Codegen, indexing, data compression, mainly used in OLAP query and computing scenarios. It has very good performance in real-time data access, aggregation query of large and wide tables, complex analysis and calculation under massive data, and multi-table associated query scenarios.

Scenario CategoryScenarioDescriptionFeatures
Interactive queryUser-defined queryData application that supports multi-dimensional query analysisFree dimension, multi-table association, fast response
Self-service reportsSupport BI tools such as TableauFree dimension, multi-table association, fast response
User portrait analysisSupport DMP and other circle person portrait platformsFree dimension, multi-table association, fast response
Marketing effect analysisSupport traffic effect funnel analysisMulti-table association, real-time
Behavior log analysisSupport log exploration and analysisLog retrieval, large amount of data
Real-time data dashboardReal-time business monitoring large screenSupport DataV and other visual large screensReal-time
Live data statistics dashboardSupport real-time reportsReal-time
Business DashboardSupport Report ToolStatistics, Fast Response
System link monitoringSupport real-time monitoring applicationsReal-time
Real-time data warehouseReal-time data accessSupport real-time data writing and updatingReal-time data writing, immediately visible
Quasi-real-time ETL calculationSupport complex calculation, data cleaningMixed load

Multi-tenant isolation and resource sharing

In ByConity, users can specify a computing group for querying SQL to achieve physical resource isolation and avoid query interference between different tenants. Of course, in order to improve resource utilization, ByConity also supports resource leasing between computing groups to achieve resource sharing.

Separation of reading and writing calculations

ByConity's storage-computing separation architecture makes it natively support storage-computing separation. insert uses a computing group dedicated to writing, and select uses a computing group dedicated to reading. The read and write jobs will not affect each other.

Real-time scaling

ByConity's storage and computing separation architecture design makes it perfectly suitable for scenarios with dynamic expansion and contraction requirements, maximizing resource utilization and reducing costs according to actual resource requirements. ByConity's metadata and data are stored at the remote end. The stateless computing nodes make expansion and contraction very lightweight. You only need to wait for the computing instance to start up, and the service can be served immediately without additional data migration overhead, realizing real-time expansion. Shrinkage.