## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
# library(memtoc)

## ----basic--------------------------------------------------------------------
# tic_mem("load data")
# data <- read.csv("large_file.csv")
# toc_mem()
# #> ✔ load data: 142.3 MB peak | 142.3 MB current | 1.24 sec | 2 samples

## ----polling------------------------------------------------------------------
# tic_mem("matrix operations", interval = 0.5)  # Sample every 0.5 seconds
# 
# # Create a large temporary matrix
# x <- matrix(rnorm(1e8), ncol = 1000)  # ~800 MB
# y <- colMeans(x)                        # x can be garbage collected
# rm(x)
# gc()
# 
# result <- toc_mem()
# #> ✔ matrix operations: 812.4 MB peak | 45.2 MB current | 3.21 sec | 7 samples

## ----snapshot-----------------------------------------------------------------
# tic_mem("quick op", interval = NULL)  # Snapshot mode
# y <- 1:100
# toc_mem()

## ----nested-------------------------------------------------------------------
# tic_mem("full pipeline")
# 
#   tic_mem("step 1: load")
#   data <- read.csv("data.csv")
#   toc_mem()
#   #> ✔ step 1: load: 50.2 MB peak | 50.2 MB current | 1.2 sec
# 
#   tic_mem("step 2: transform")
#   features <- transform(data)
#   toc_mem()
#   #> ✔ step 2: transform: 125.8 MB peak | 98.3 MB current | 2.4 sec
# 
#   tic_mem("step 3: model")
#   model <- train(features)
#   toc_mem()
#   #> ✔ step 3: model: 512.1 MB peak | 201.5 MB current | 45.2 sec
# 
# toc_mem()
# #> ✔ full pipeline: 512.1 MB peak | 201.5 MB current | 48.8 sec

## ----logging------------------------------------------------------------------
# mem_clearlog()
# 
# for (i in 1:10) {
#   tic_mem(paste("iteration", i))
#   # ... do work ...
#   toc_mem(log = TRUE, quiet = TRUE)
# }
# 
# # Get all results as a data frame
# results <- mem_log()
# summary(results$mem_peak)

## ----parallel-----------------------------------------------------------------
# library(future)
# library(future.apply)
# 
# # Set up parallel workers
# plan(multisession, workers = 4)
# 
# # Check that workers are detected
# mem_parallel_info()
# #> ── Parallel Backend Info
# #> • Main process PID: 12345
# #> • Current plan: multisession
# #> • Workers configured: 4
# 
# # Monitor parallel job
# tic_mem("parallel computation", workers = "auto")
# result <- future_lapply(1:100, function(i) {
#   x <- rnorm(1e6)
#   mean(x)
# }, future.seed = TRUE)
# mem_result <- toc_mem()
# #> ✔ parallel computation: 1.2 GB peak | 245 MB current | 5.4 sec | 4 workers
# 
# # View per-worker breakdown
# mem_result$worker_stats
# 
# # Clean up
# plan(sequential)

## ----warnings-----------------------------------------------------------------
# tic_mem("memory intensive")
# # ... allocate lots of memory ...
# toc_mem()
# #> ✔ memory intensive: 12.4 GB peak | 11.2 GB current | 45.2 sec
# #> ⚠ System RAM high: 87.3% used

## ----recovery-----------------------------------------------------------------
# # List checkpoints in this R session
# mem_recover()
# #> ℹ Found 1 recovery file:
# #>   • PID 12345: 15.2 KB, 152 samples
# 
# # Recover the data
# recovered <- mem_recover(pid = 12345)
# head(recovered)

## ----diagnostics--------------------------------------------------------------
# mem_capabilities()
# #>     memory_queries background_polling
# #>               TRUE               TRUE
# 
# # Detailed troubleshooting
# mem_diagnose()