Shiny Performance Optimization: Speed Up Your R Apps

Key Takeaways

Tip

Reactive Caching Magic: Implementing smart caching strategies can reduce computation time by 90%+ while maintaining application responsiveness
Memory Management Mastery: Proper memory handling prevents crashes and enables applications to handle datasets 10x larger than naive implementations
Profiling-Driven Optimization: Systematic performance profiling identifies bottlenecks accurately, preventing wasted effort on non-critical optimizations
Asynchronous Processing Power: Non-blocking operations keep your UI responsive even during heavy computations that take minutes to complete
Production-Ready Performance: Advanced optimization techniques scale applications from prototype to enterprise-grade tools serving hundreds of concurrent users

Introduction

Application performance can make or break user adoption of your Shiny applications. While a functional app might impress during development, slow response times, memory issues, and unresponsive interfaces quickly frustrate users and stakeholders in real-world scenarios.

This comprehensive guide transforms your understanding of Shiny server optimization from basic reactive programming to advanced performance engineering. You’ll master the techniques that separate amateur applications from professional-grade tools capable of handling large datasets, complex computations, and multiple concurrent users without compromising user experience.

The optimization strategies covered here are battle-tested approaches used in production environments where performance directly impacts business outcomes. Whether you’re building internal dashboards that need to handle enterprise datasets or client-facing applications that must scale reliably, these techniques provide the foundation for exceptional performance.

Understanding Shiny Performance Fundamentals

Before diving into specific optimization techniques, it’s crucial to understand how Shiny processes requests and where performance bottlenecks typically occur.

flowchart TD
    A[User Input] --> B[Reactive Graph Update]
    B --> C[Dependency Calculation]
    C --> D[Expression Execution]
    D --> E[Data Processing]
    E --> F[Output Rendering]
    F --> G[UI Update]
    
    H[Performance Bottlenecks] --> I[Expensive Computations]
    H --> J[Large Data Operations]
    H --> K[Inefficient Reactive Design]
    H --> L[Memory Leaks]
    H --> M[Blocking Operations]
    
    style A fill:#e1f5fe
    style G fill:#e8f5e8
    style H fill:#fff3e0
    style I fill:#ffebee
    style J fill:#ffebee
    style K fill:#ffebee
    style L fill:#ffebee
    style M fill:#ffebee

The Reactive Performance Model

Shiny’s reactive system creates a dependency graph where changes propagate through connected expressions. Understanding this flow is essential for optimization:

Reactive Sources → Reactive Conductors → Reactive Endpoints

Each step in this chain represents a potential optimization opportunity. The key is identifying where expensive operations occur and implementing targeted improvements.

Common Performance Bottlenecks

Most Shiny performance issues fall into predictable categories:

Expensive Computations: Complex statistical models, data transformations, or iterative algorithms
Large Dataset Operations: Processing datasets that exceed available memory or require significant computation time
Inefficient Reactive Design: Unnecessary recalculations due to poor reactive expression structure
Memory Management Issues: Memory leaks or inefficient data storage patterns
Blocking Operations: Long-running tasks that freeze the user interface

Reactive Performance Patterns

Reactive Programming Cheatsheet - Essential performance patterns: shared reactives, debugging techniques, and avoiding infinite loops.

Optimize Reactives • Debug Flow • Prevent Loops

Advanced Reactive Expression Optimization

Reactive expressions are the heart of Shiny performance. Optimizing them effectively requires understanding both their computational cost and their position in the reactive dependency graph.

Smart Caching with Reactive Expressions

The most impactful optimization technique involves strategic caching of expensive computations:

# Inefficient: Recalculates expensive operation on every change
server <- function(input, output, session) {
  
  output$expensive_plot <- renderPlot({
    # This runs every time ANY input changes
    expensive_data <- perform_complex_calculation(input$dataset, input$params)
    create_visualization(expensive_data)
  })
}

# Efficient: Caches expensive calculation separately
server <- function(input, output, session) {
  
  # Cached reactive expression - only recalculates when dependencies change
  expensive_data <- reactive({
    # Only recalculates when input$dataset or input$params change
    perform_complex_calculation(input$dataset, input$params)
  })
  
  # Fast rendering using cached data
  output$expensive_plot <- renderPlot({
    data <- expensive_data()  # Uses cached result
    create_visualization(data)
  })
  
  output$summary_table <- renderTable({
    data <- expensive_data()  # Reuses same cached result
    create_summary(data)
  })
}

# Sophisticated: Multiple levels of caching with selective invalidation
server <- function(input, output, session) {
  
  # Level 1: Raw data processing (changes only with dataset)
  processed_data <- reactive({
    # Heavy data cleaning and transformation
    clean_and_transform(input$dataset)
  })
  
  # Level 2: Filtered data (changes with dataset or filters)
  filtered_data <- reactive({
    data <- processed_data()  # Uses cached processed data
    apply_filters(data, input$filters)
  })
  
  # Level 3: Analysis results (changes with data or analysis parameters)
  analysis_results <- reactive({
    data <- filtered_data()  # Uses cached filtered data
    perform_analysis(data, input$analysis_params)
  })
  
  # Fast outputs using multi-level cache
  output$visualization <- renderPlot({
    results <- analysis_results()
    create_plot(results, input$plot_style)  # Only plot style changes trigger re-render
  })
}

Monitor Reactive Performance Live

See performance optimization in action with real metrics:

Understanding reactive caching and execution timing is essential for optimization. Watch how Shiny’s reactive system minimizes unnecessary computations through intelligent dependency tracking.

Experience Live Performance Tracking →

Use the real-time performance dashboard to see execution times, cache efficiency, and dependency depth - then apply these insights to optimize your own applications.

Reactive Expression Hierarchy Design

Organizing reactive expressions in a logical hierarchy minimizes unnecessary recalculations:

server <- function(input, output, session) {
  
  # Foundation layer: Raw data (changes rarely)
  raw_data <- reactive({
    # Expensive data loading operation
    load_large_dataset(input$data_source)
  })
  
  # Processing layer: Data transformations (changes with processing parameters)
  processed_data <- reactive({
    data <- raw_data()
    transform_data(data, input$processing_options)
  })
  
  # Analysis layer: Statistical computations (changes with analysis settings)
  analysis_data <- reactive({
    data <- processed_data()
    run_statistical_analysis(data, input$analysis_method)
  })
  
  # Presentation layer: Formatting for display (changes with display options)
  formatted_results <- reactive({
    results <- analysis_data()
    format_for_display(results, input$display_format)
  })
  
  # Output layer: Fast rendering from formatted data
  output$main_plot <- renderPlot({
    create_plot(formatted_results())
  })
}

Memory Management and Data Handling

Efficient memory management is crucial for applications handling large datasets or serving multiple users simultaneously.

Memory-Efficient Data Processing

Large datasets require careful memory management to prevent application crashes and maintain performance:

# Memory-efficient data processing techniques
server <- function(input, output, session) {
  
  # Strategy 1: Chunked processing for large datasets
  process_large_dataset <- reactive({
    # Process data in chunks to manage memory usage
    chunk_size <- 10000
    total_rows <- nrow(input_data())
    
    results <- list()
    for(i in seq(1, total_rows, chunk_size)) {
      end_row <- min(i + chunk_size - 1, total_rows)
      chunk <- input_data()[i:end_row, ]
      
      # Process chunk and store results
      results[[length(results) + 1]] <- process_chunk(chunk)
      
      # Clean up chunk to free memory
      rm(chunk)
      gc()  # Force garbage collection
    }
    
    # Combine results efficiently
    do.call(rbind, results)
  })
  
  # Strategy 2: Selective column loading
  filtered_data <- reactive({
    # Only load columns needed for current analysis
    required_columns <- get_required_columns(input$analysis_type)
    input_data()[, required_columns, drop = FALSE]
  })
  
  # Strategy 3: Data sampling for interactive exploration
  sample_data <- reactive({
    if(nrow(input_data()) > 100000) {
      # Use statistical sampling for large datasets
      sample_indices <- sample(nrow(input_data()), 10000)
      input_data()[sample_indices, ]
    } else {
      input_data()
    }
  })
}

Memory Leak Prevention

Preventing memory leaks ensures long-term application stability:

server <- function(input, output, session) {
  
  # Use reactiveValues for complex state management
  app_state <- reactiveValues(
    large_objects = NULL,
    temporary_data = NULL
  )
  
  # Clean up resources when no longer needed
  observeEvent(input$clear_cache, {
    app_state$large_objects <- NULL
    app_state$temporary_data <- NULL
    gc()  # Force garbage collection
  })
  
  # Clean up on session end
  session$onSessionEnded(function() {
    # Clear all reactive values
    app_state$large_objects <- NULL
    app_state$temporary_data <- NULL
    
    # Additional cleanup for external resources
    cleanup_external_connections()
  })
}

Profiling and Performance Monitoring

Systematic performance profiling identifies actual bottlenecks rather than assumed problems, ensuring optimization efforts focus on areas with maximum impact.

Built-in Shiny Profiling

Shiny provides built-in profiling capabilities for reactive expressions:

# Enable reactive expression profiling
options(shiny.reactlog = TRUE)

# Run your application, then analyze the reactive log
# In R console after stopping the app:
# shiny::reactlogShow()

Advanced Profiling with Profvis

For detailed performance analysis, use the profvis package:

library(profvis)

# Profile specific functions
profvis({
  # Your expensive computation here
  result <- expensive_analysis_function(large_dataset)
})

# Profile within Shiny server logic
server <- function(input, output, session) {
  
  expensive_computation <- reactive({
    profvis({
      # Wrap expensive operations for profiling
      perform_complex_analysis(input$data)
    })
  })
}

Real-time Performance Monitoring

Implement monitoring within your application to track performance in production:

server <- function(input, output, session) {
  
  # Performance monitoring reactive
  performance_stats <- reactiveValues(
    computation_times = numeric(0),
    memory_usage = numeric(0),
    last_update = Sys.time()
  )
  
  # Monitored expensive computation
  expensive_analysis <- reactive({
    start_time <- Sys.time()
    start_memory <- as.numeric(gc()[2, 2])
    
    # Your computation here
    result <- perform_analysis(input$data)
    
    # Record performance metrics
    end_time <- Sys.time()
    end_memory <- as.numeric(gc()[2, 2])
    
    computation_time <- as.numeric(difftime(end_time, start_time, units = "secs"))
    memory_used <- end_memory - start_memory
    
    # Update performance stats
    performance_stats$computation_times <- c(
      tail(performance_stats$computation_times, 99), 
      computation_time
    )
    performance_stats$memory_usage <- c(
      tail(performance_stats$memory_usage, 99), 
      memory_used
    )
    performance_stats$last_update <- Sys.time()
    
    result
  })
  
  # Performance dashboard output
  output$performance_monitor <- renderText({
    if(length(performance_stats$computation_times) > 0) {
      avg_time <- mean(performance_stats$computation_times)
      avg_memory <- mean(performance_stats$memory_usage)
      paste0(
        "Average computation time: ", round(avg_time, 2), " seconds\n",
        "Average memory usage: ", round(avg_memory, 2), " MB\n",
        "Last update: ", performance_stats$last_update
      )
    }
  })
}

Optimize Table Performance for Large Datasets

Apply performance optimization insights to efficient table rendering:

Large datasets require careful consideration of table performance. The optimization strategies you’ve learned - reactive caching, memory management, and efficient processing - directly apply to creating responsive table displays.

Test Performance Configurations →

Use the DT Configuration Playground to compare client-side vs server-side processing performance and understand how different table features impact application responsiveness with large datasets.

Asynchronous Processing and Non-Blocking Operations

For computations that take significant time, asynchronous processing prevents UI freezing and improves user experience.

Future-Based Asynchronous Processing

Using the future and promises packages for non-blocking operations:

library(future)
library(promises)

# Configure asynchronous execution
plan(multisession, workers = 4)

server <- function(input, output, session) {
  
  # Asynchronous computation that doesn't block UI
  async_analysis <- reactive({
    future({
      # Long-running computation in separate process
      perform_lengthy_analysis(input$data)
    }) %...>% {
      # Process results when computation completes
      format_results(.)
    }
  })
  
  # Non-blocking output rendering
  output$async_results <- renderTable({
    async_analysis() %...>% {
      # Display results when available
      create_results_table(.)
    }
  })
  
  # Progress indication for long-running operations
  observeEvent(input$start_analysis, {
    # Show progress indicator
    shinyWidgets::show_alert(
      title = "Processing", 
      text = "Analysis in progress...", 
      type = "info"
    )
    
    # Start async computation with completion callback
    async_analysis() %...>% {
      # Hide progress indicator when done
      shinyWidgets::close_alert()
    }
  })
}

Background Task Management

Implement sophisticated background task management for complex workflows:

server <- function(input, output, session) {
  
  # Task management system
  task_manager <- reactiveValues(
    active_tasks = list(),
    completed_tasks = list(),
    task_counter = 0
  )
  
  # Function to submit background tasks
  submit_background_task <- function(task_name, computation_function, ...) {
    task_id <- paste0("task_", task_manager$task_counter + 1)
    task_manager$task_counter <- task_manager$task_counter + 1
    
    # Create future for background computation
    task_future <- future({
      computation_function(...)
    })
    
    # Store task information
    task_manager$active_tasks[[task_id]] <- list(
      name = task_name,
      future = task_future,
      start_time = Sys.time(),
      status = "running"
    )
    
    # Monitor task completion
    task_future %...>% {
      # Move to completed tasks
      task_manager$completed_tasks[[task_id]] <- list(
        name = task_name,
        result = .,
        start_time = task_manager$active_tasks[[task_id]]$start_time,
        end_time = Sys.time(),
        status = "completed"
      )
      
      # Remove from active tasks
      task_manager$active_tasks[[task_id]] <- NULL
    }
    
    return(task_id)
  }
  
  # Task monitoring interface
  output$task_monitor <- renderTable({
    active <- lapply(task_manager$active_tasks, function(task) {
      data.frame(
        Name = task$name,
        Status = task$status,
        Duration = as.numeric(difftime(Sys.time(), task$start_time, units = "secs")),
        stringsAsFactors = FALSE
      )
    })
    
    if(length(active) > 0) {
      do.call(rbind, active)
    } else {
      data.frame(Name = "No active tasks", Status = "", Duration = "")
    }
  })
}

Database Query Optimization

When working with database-backed applications, query optimization is crucial for performance.

Efficient Database Queries

Optimize database interactions to minimize data transfer and processing time:

library(DBI)
library(dbplyr)

server <- function(input, output, session) {
  
  # Connection pooling for better performance
  pool <- pool::dbPool(
    drv = RPostgreSQL::PostgreSQL(),
    dbname = "mydb",
    host = "localhost",
    user = "user",
    password = "password",
    minSize = 1,
    maxSize = 10
  )
  
  # Optimized data loading with server-side filtering
  filtered_data <- reactive({
    # Build query with server-side filtering
    query <- tbl(pool, "large_table") %>%
      filter(
        date >= !!input$start_date,
        date <= !!input$end_date,
        category %in% !!input$categories
      ) %>%
      select(!!!syms(get_required_columns(input$analysis_type)))
    
    # Execute query and collect results
    collect(query)
  })
  
  # Cached aggregations for dashboard metrics
  summary_metrics <- reactive({
    # Use database aggregation functions for efficiency
    tbl(pool, "large_table") %>%
      filter(
        date >= !!input$start_date,
        date <= !!input$end_date
      ) %>%
      summarise(
        total_records = n(),
        avg_value = mean(value, na.rm = TRUE),
        max_value = max(value, na.rm = TRUE),
        min_value = min(value, na.rm = TRUE)
      ) %>%
      collect()
  })
  
  # Clean up connection pool on session end
  session$onSessionEnded(function() {
    pool::poolClose(pool)
  })
}

Query Result Caching

Implement intelligent caching for database queries:

server <- function(input, output, session) {
  
  # Query cache management
  query_cache <- reactiveValues(
    cache = list(),
    cache_timestamps = list(),
    max_age = 300  # 5 minutes cache expiration
  )
  
  # Cached database query function
  cached_query <- function(query_key, query_function) {
    current_time <- Sys.time()
    
    # Check if cached result exists and is still valid
    if(query_key %in% names(query_cache$cache)) {
      cache_age <- as.numeric(difftime(
        current_time, 
        query_cache$cache_timestamps[[query_key]], 
        units = "secs"
      ))
      
      if(cache_age < query_cache$max_age) {
        # Return cached result
        return(query_cache$cache[[query_key]])
      }
    }
    
    # Execute query and cache result
    result <- query_function()
    query_cache$cache[[query_key]] <- result
    query_cache$cache_timestamps[[query_key]] <- current_time
    
    return(result)
  }
  
  # Use cached queries in reactive expressions
  dashboard_data <- reactive({
    query_key <- paste0(
      "dashboard_", 
      input$date_range[1], "_", 
      input$date_range[2], "_",
      paste(input$filters, collapse = "_")
    )
    
    cached_query(query_key, function() {
      # Expensive database query
      execute_dashboard_query(input$date_range, input$filters)
    })
  })
}

Common Performance Issues and Solutions

Understanding and preventing common performance pitfalls ensures consistently fast applications.

Issue 1: Reactive Expression Over-Invalidation

Problem: Reactive expressions recalculate unnecessarily due to dependencies on frequently changing inputs.

Solution:

# Problematic: Recalculates on every keystroke
problematic_analysis <- reactive({
  expensive_computation(input$text_input, input$numeric_input)
})

# Optimized: Use debouncing to reduce recalculations
library(shinyjs)

optimized_analysis <- reactive({
  # Debounce text input to reduce recalculation frequency
  debounced_text <- input$text_input
  expensive_computation(debounced_text, input$numeric_input)
}) %>% debounce(1000)  # Wait 1 second after last change

Issue 2: Memory Accumulation in Long-Running Sessions

Problem: Memory usage grows over time due to accumulated objects in reactive values.

Solution:

server <- function(input, output, session) {
  
  # Implement circular buffer for storing historical data
  historical_data <- reactiveValues(
    values = numeric(0),
    max_length = 1000  # Limit stored values
  )
  
  # Add new data with automatic cleanup
  observeEvent(input$new_data, {
    new_values <- c(historical_data$values, input$new_data)
    
    # Keep only recent values to prevent memory growth
    if(length(new_values) > historical_data$max_length) {
      start_index <- length(new_values) - historical_data$max_length + 1
      historical_data$values <- new_values[start_index:length(new_values)]
    } else {
      historical_data$values <- new_values
    }
  })
  
  # Periodic memory cleanup
  observe({
    invalidateLater(300000)  # Every 5 minutes
    gc()  # Force garbage collection
  })
}

Issue 3: Inefficient Data Structure Operations

Problem: Using inefficient data structures or operations for large datasets.

Solution:

# Instead of repeated rbind operations (slow)
slow_data_accumulation <- function(data_list) {
  result <- data.frame()
  for(item in data_list) {
    result <- rbind(result, item)  # Inefficient
  }
  return(result)
}

# Use efficient data combination
fast_data_accumulation <- function(data_list) {
  # Pre-allocate or use efficient binding
  do.call(rbind, data_list)  # Much faster
}

# For repeated filtering operations
efficient_filtering <- reactive({
  # Use data.table for large dataset operations
  library(data.table)
  dt_data <- as.data.table(large_dataset())
  
  # Fast filtering with data.table syntax
  filtered <- dt_data[
    category %in% input$selected_categories & 
    value > input$min_value
  ]
  
  # Convert back to data.frame if needed
  as.data.frame(filtered)
})

Performance Testing Best Practices

Always test performance optimizations with realistic data volumes and usage patterns. What works well with small test datasets may not scale to production data sizes. Use profiling tools to measure actual performance improvements rather than assuming optimizations are effective.

Test Your Understanding

Quiz Question 1: Reactive Expression Caching Strategy

You have an expensive data processing operation that takes 10 seconds to complete. This operation depends on input$dataset (changes rarely) and input$processing_method (changes occasionally). Your visualization depends on this processed data plus input$plot_style (changes frequently). What’s the optimal caching strategy?

Put everything in one reactive expression that recalculates when any input changes
Create separate reactive expressions for data processing and visualization
Use a single reactive expression with conditional logic to avoid recalculation
Cache the final visualization output to avoid any recalculation

Consider how often each input changes and the computational cost
Think about the dependency chain and where expensive operations occur
Remember that reactive expressions only recalculate when their direct dependencies change

B) Create separate reactive expressions for data processing and visualization

The optimal approach separates concerns by computational cost and change frequency:

# Expensive processing - only recalculates when dataset or method changes
processed_data <- reactive({
  expensive_processing(input$dataset, input$processing_method)
})

# Fast visualization - uses cached processed data
output$plot <- renderPlot({
  data <- processed_data()  # Uses cached result
  create_plot(data, input$plot_style)  # Only plot style changes trigger re-render
})

This strategy ensures the expensive 10-second operation only runs when necessary, while plot style changes (frequent) only trigger fast visualization updates using the cached processed data.

Quiz Question 2: Memory Management for Large Datasets

Your Shiny app needs to process a 2GB dataset that exceeds available memory. Users need to filter and analyze subsets of this data interactively. What’s the best approach for memory management?

Load the entire dataset into memory and use R’s filtering functions
Use database storage with server-side filtering and load only needed subsets
Break the dataset into multiple files and load them sequentially
Use data compression to fit the dataset in available memory

Consider memory limitations and interactive filtering requirements
Think about scalability and response time for user interactions
Remember that users typically work with subsets, not entire datasets

B) Use database storage with server-side filtering and load only needed subsets

For datasets larger than available memory, database storage with server-side filtering is optimal:

# Database approach - only loads filtered subsets
filtered_data <- reactive({
  tbl(database_connection, "large_table") %>%
    filter(
      date >= !!input$start_date,
      category %in% !!input$categories
    ) %>%
    collect()  # Only bring filtered results into memory
})

Benefits:

Memory usage remains manageable regardless of dataset size
Fast interactive filtering using database indexes
Scalable architecture that handles even larger datasets
Server-side aggregations reduce data transfer

This approach is far superior to loading 2GB into memory, which would likely crash the application or consume all available system resources.

Quiz Question 3: Asynchronous Processing Decision

You’re building a Shiny app where users can run statistical models that take 2-5 minutes to complete. During this time, users should still be able to interact with other parts of the interface and potentially start additional analyses. What’s the best implementation strategy?

Use standard reactive expressions and display a progress bar
Implement asynchronous processing with the future and promises packages
Break the analysis into smaller chunks and process them sequentially
Pre-compute all possible analyses and cache the results

Consider user experience during long-running operations
Think about concurrent usage and multiple simultaneous analyses
Remember that blocking operations freeze the entire interface

B) Implement asynchronous processing with the future and promises packages

For long-running operations (2-5 minutes), asynchronous processing is essential:

library(future)
library(promises)

# Configure async execution
plan(multisession, workers = 4)

server <- function(input, output, session) {
  
  # Non-blocking analysis
  async_analysis <- reactive({
    future({
      run_statistical_model(input$model_params)
    }) %...>% {
      format_results(.)
    }
  })
  
  # UI remains responsive during computation
  output$results <- renderTable({
    async_analysis() %...>% {
      display_results_table(.)
    }
  })
}

Why this is optimal: - UI remains fully responsive during long computations - Users can start multiple analyses simultaneously - Background processing uses separate CPU cores efficiently - Proper error handling and progress indication possible

Standard reactive expressions would freeze the interface for 2-5 minutes, making the application unusable.

Conclusion

Mastering Shiny server performance optimization transforms your applications from functional prototypes into professional-grade tools capable of handling real-world demands. The techniques covered in this guide - from reactive expression caching to asynchronous processing - provide the foundation for building applications that scale efficiently and provide exceptional user experiences.

The key to effective optimization lies in systematic profiling to identify actual bottlenecks, implementing targeted improvements, and testing with realistic data volumes and usage patterns. Performance optimization is an iterative process where small improvements compound to create dramatically faster applications.

Your journey into advanced Shiny development now includes the performance engineering skills necessary for production deployment. These optimization techniques become even more critical as you move toward building enterprise-grade applications that serve multiple concurrent users and handle large-scale data processing requirements.

Next Steps

Based on what you’ve learned about server performance optimization, here are the recommended paths for continuing your advanced Shiny development:

Immediate Next Steps (Complete These First)

Interactive Features Overview - Apply performance optimization techniques to complex interactive components
Production Deployment Strategies - Learn how optimization prepares applications for production environments
Practice Exercise: Optimize an existing Shiny application by implementing reactive caching, profiling bottlenecks, and measuring performance improvements

Building on Your Foundation (Choose Your Path)

For Advanced Development Focus:

For Production and Deployment Focus:

For Interactive Features Integration:

Long-term Goals (2-4 Weeks)

Build a high-performance dashboard that handles datasets larger than available memory using database integration and server-side processing
Implement a production monitoring system that tracks application performance metrics in real-time
Create an enterprise-grade application with asynchronous processing, intelligent caching, and automatic performance optimization
Contribute to the Shiny community by sharing performance optimization techniques or open-sourcing optimized application architectures

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