Skip to content

on_boarding.md

Latest commit

 

History

History
102 lines (61 loc) · 6.85 KB

on_boarding.md

File metadata and controls

102 lines (61 loc) · 6.85 KB
graph LR
    CLI_IPython_Orchestrator["CLI & IPython Orchestrator"]
    Profile_Data_Acquisition_Layer["Profile Data Acquisition Layer"]
    Runtime_Profile_Processor["Runtime Profile Processor"]
    Import_Profile_Processor["Import Profile Processor"]
    Visualization_Generation_Engine_Backend_["Visualization Generation Engine (Backend)"]
    Local_Web_Server["Local Web Server"]
    Frontend_Visualization_Client_side_["Frontend Visualization (Client-side)"]
    CLI_IPython_Orchestrator -- "initiates" --> Profile_Data_Acquisition_Layer
    CLI_IPython_Orchestrator -- "triggers" --> Local_Web_Server
    Profile_Data_Acquisition_Layer -- "delegates to" --> Runtime_Profile_Processor
    Profile_Data_Acquisition_Layer -- "delegates to" --> Import_Profile_Processor
    Runtime_Profile_Processor -- "provides processed data to" --> Visualization_Generation_Engine_Backend_
    Import_Profile_Processor -- "provides processed data to" --> Visualization_Generation_Engine_Backend_
    Visualization_Generation_Engine_Backend_ -- "provides rendered output to" --> Local_Web_Server
    Local_Web_Server -- "serves web assets to" --> Frontend_Visualization_Client_side_
    Frontend_Visualization_Client_side_ -- "requests data from" --> Local_Web_Server
    Frontend_Visualization_Client_side_ -- "renders data received from" --> Local_Web_Server
    click CLI_IPython_Orchestrator href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/tuna/CLI_IPython_Orchestrator.md" "Details"
    click Profile_Data_Acquisition_Layer href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/tuna/Profile_Data_Acquisition_Layer.md" "Details"
    click Runtime_Profile_Processor href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/tuna/Runtime_Profile_Processor.md" "Details"
    click Frontend_Visualization_Client_side_ href "https://github.com/CodeBoarding/GeneratedOnBoardings/blob/main/tuna/Frontend_Visualization_Client_side_.md" "Details"
Loading

CodeBoardingDemoContact

Details

The tuna architecture is designed around a clear data flow pipeline, starting with user interaction through a CLI & IPython Orchestrator. This orchestrator initiates the profiling process, directing raw performance data through a Profile Data Acquisition Layer to specialized Runtime or Import Profile Processors. The processed data then feeds into a Visualization Generation Engine (Backend) which prepares the interactive web content. A Local Web Server serves this content to the user's browser, where the Frontend Visualization (Client-side) renders the interactive performance graphs, completing the data visualization loop. This modular design ensures clear separation of concerns for data acquisition, processing, backend rendering, and client-side presentation, making it ideal for flow graph representation.

CLI & IPython Orchestrator [Expand]

The primary user interface, handling command-line arguments and IPython magic commands. It orchestrates the entire profiling, data processing, and visualization workflow.

Related Classes/Methods:

Profile Data Acquisition Layer [Expand]

An abstraction responsible for reading and initially parsing various types of raw performance profile data (e.g., cProfile, import times). It dispatches to specific profile processors.

Related Classes/Methods:

Runtime Profile Processor [Expand]

Specializes in parsing, structuring, and enriching runtime performance data (e.g., from cProfile dumps) into a standardized format suitable for visualization.

Related Classes/Methods:

Import Profile Processor

Focuses on processing and structuring import time performance data, including hierarchical organization and adding visualization-specific attributes.

Related Classes/Methods:

Visualization Generation Engine (Backend)

Transforms processed performance data into the final interactive web-based visualization, involving templating HTML and embedding data for the frontend.

Related Classes/Methods:

Local Web Server

A lightweight HTTP server responsible for hosting and serving the interactive visualization frontend (HTML, CSS, JavaScript) and processed data to the client.

Related Classes/Methods:

Frontend Visualization (Client-side) [Expand]

The interactive web application, primarily built with D3.js, Bootstrap, HTML, CSS, and JavaScript, responsible for rendering the performance data as an icicle graph and providing user interaction.

Related Classes/Methods:

FAQ