polynote/polynote

Features

• Notebook Environments - Provides an interactive document environment combining executable code cells, rich text, and visual output.
• Polyglot Execution Environments - Provides a polyglot execution environment that runs multiple language runtimes within a single session.
• Polyglot Data Science Environments - Provides an integrated environment supporting the concurrent use of multiple languages for data analysis and scientific computing.
• Interactive Notebooks - Combines executable code, rich text, and data visualizations into a single document for technical workflows.
• Interactive Technical Documents - A document creator combining executable code cells with formatted prose, mathematical expressions, and data visualizations.
• Notebook-Based IDEs - Provides a notebook-based editor with autocomplete and error highlighting for Scala and other JVM languages.
• Cross-Language Data Exchange - Enables bidirectional movement of data and variables between Python and JVM runtimes using wrappers.
• Python Runtimes - Integrates Python execution environments to perform dynamic data manipulation and analysis within the notebook.
• Cross-Runtime Variable Exchange - Exchanges data and variables between different language runtimes for cross-language data analysis.
• Notebook Code Execution - Executes code in multiple languages within a web-based notebook for interactive data exploration.
• Variable Scope Management - Tracks variable availability based on the physical sequence of cells to ensure values are accessed only after definition.
• Cross-Language Object Mapping - Transforms data types from one language into native collections of another to enable language-specific operations.
• Immutable Data Management - Ensures computation reproducibility by using an immutable data model for the document state and execution results.
• Polyglot Documents - Creates documents that integrate multiple programming languages and markdown for hybrid authoring.
• Rich Text Authoring - Provides a visual editor and markup syntax for rich text formatting, including mathematical expressions.
• Apache Spark Pipelines - Configures Spark runtimes and submits distributed data processing jobs from an interactive environment.
• Complex Data Views - Explores nested data structures through tree-like views and generates schema visualizations for analysis.
• Apache Spark Analytics - Integrates with Spark clusters to execute distributed processing tasks and visualize the results.
• Declarative Visualization Languages - Supports chart creation using visualization specifications that reference variables and data defined in notebook cells.
• Data Visualization - Transforms table-like results from code cells into dedicated visualization cells for detailed inspection.
• Symbol Visualizations - Inspects symbols using various representations, including data tables and lazy-loading streams for large datasets.
• Distributed Data Processing - Integrates with local or remote Apache Spark clusters to execute distributed data processing tasks.
• Interpreter Symbol Tables - Displays a real-time symbol table of available variables and their values based on the currently focused cell.
• Python Package Managers - Manages Python package installations and virtual environments using custom resolvers.
• Dependency Resolvers - Resolves and downloads Java libraries using coordinate systems while handling transitive dependency chains.
• IDE Features - Provides autocomplete and error highlighting within the notebook editor to accelerate development.
• Runtime Symbol Inspectors - Polyglot Notebooks provides a view of the names and values of variables produced by cells to debug state quickly.
• Symbol Table Inspection - Maintains a real-time symbol table of defined variables and functions for runtime monitoring.
• JVM Dependency Resolution - Provides coordinate-based resolution for JVM and Python libraries to set up the execution environment.
• Kernel Process Configuration - Defines environment variables and virtual machine arguments for the execution kernel process.
• MIME-Based Display Customization - Registers specialized display formats for custom types to render content as images or structured markup.
• Cellular Execution Scoping - Tracks variable availability based on the physical sequence of cells to ensure values are accessed only after definition.
• Dependency Resolvers - Implements coordinate-based resolution to download and cache JVM and Python libraries for session setup.
• Rich Text Annotations - Enables formatted documentation and annotations between code cells using a rich text editor.
• MIME Type Renderers - Registers specialized display formats to render arbitrary data as HTML or other MIME types.
• Immutable State Stores - Uses an immutable data model for document state and execution results to ensure computation reproducibility.
• Interactive Notebooks - Interactive notebook environment for Scala and other languages.

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