Package {memtoc}

Title: 'Tictoc'-Style Memory Usage Tracking
Version: 0.1.1
Description: Provides simple start/stop memory tracking functions tic_mem() and toc_mem() that can be nested, inspired by the 'tictoc' package. Track RAM usage during code execution with support for logging, custom messages, nested tracking blocks, and parallel worker monitoring. Features continuous background polling to estimate peak memory usage across main process and workers. Integrates with the 'future' package ecosystem for automatic worker detection. Designed for monitoring memory consumption in parallel workflows.
License: MIT + file LICENSE
URL: https://github.com/jcoa05/memtoc
BugReports: https://github.com/jcoa05/memtoc/issues
Encoding: UTF-8
Depends: R (≥ 4.1.0)
Imports: ps (≥ 1.7.0), cli (≥ 3.0.0), callr (≥ 3.7.0)
Suggests: testthat (≥ 3.2.0), withr, future, parallelly, knitr, rmarkdown
Config/testthat/edition: 3
Language: en-US
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
NeedsCompilation: no
Packaged: 2026-09-15 19:55:54 UTC; jocampo
Author: Juan Ocampo ORCID iD [aut, cre]
Maintainer: Juan Ocampo <jocampo1997@hotmail.com>
Repository: CRAN
Date/Publication: 2026-09-26 17:10:02 UTC

memtoc: 'Tictoc'-Style Memory Usage Tracking

Description

Provides simple start/stop memory tracking functions tic_mem() and toc_mem() that can be nested, inspired by the 'tictoc' package. Track RAM usage during code execution with support for logging, custom messages, nested tracking blocks, and parallel worker monitoring. Features continuous background polling to estimate peak memory usage across main process and workers. Integrates with the 'future' package ecosystem for automatic worker detection. Designed for monitoring memory consumption in parallel workflows.

The memtoc package provides simple start/stop memory tracking functions that can be nested, inspired by the tictoc package for timing. Track RAM usage during code execution with support for logging and custom messages.

Main Functions

Basic Usage

tic_mem("data loading")
data <- read.csv("large_file.csv")
toc_mem()
#> data loading: 142.3 MB peak | 89.1 MB current | 2.34 sec elapsed

Nested Tracking

tic_mem("full pipeline")
  tic_mem("preprocessing")
  # ... code ...
  toc_mem()

  tic_mem("modeling")
  # ... code ...
  toc_mem()
toc_mem()

Author(s)

Maintainer: Juan Ocampo jocampo1997@hotmail.com (ORCID)

Authors:

See Also

Useful links:

Check memtoc capabilities

Description

Tests which features are available on the current system. This is useful for understanding why certain features might be disabled.

Usage

mem_capabilities()

Value

A named logical vector with elements:

memory_queries

Can query process memory via ps package

background_polling

Can spawn background processes via callr

Examples

mem_capabilities()

Clear the memtoc stack

Description

Removes all entries from the tracking stack. This is useful if an error occurred before toc_mem() could be called, leaving orphaned entries on the stack.

Usage

mem_clear()

Details

Also stops any running background monitors associated with orphaned entries.

Value

Invisibly returns NULL.

Examples

tic_mem("will be cleared")
mem_clear()
# Stack is now empty

Clear the memory tracking log

Description

Removes all entries from the memtoc log. This does not affect the tracking stack (active tic_mem/toc_mem blocks).

Usage

mem_clearlog()

Value

Invisibly returns NULL.

See Also

mem_log() to retrieve the log before clearing

Examples

mem_clearlog()
tic_mem("example", interval = NULL, workers = "none")
toc_mem(log = TRUE, quiet = TRUE)
nrow(mem_log())
mem_clearlog()
nrow(mem_log())

Diagnose why background polling might not be working

Description

Runs detailed diagnostics to identify issues with background process spawning. Useful for troubleshooting when mem_capabilities() shows background_polling = FALSE.

Usage

mem_diagnose()

Value

Invisibly returns a list with diagnostic results

Examples

mem_diagnose()

Retrieve the memory tracking log

Description

Returns a data frame containing all logged memory tracking results. Results are added to the log when toc_mem(log = TRUE) is called.

Usage

mem_log(format = c("data.frame", "list"))

Arguments

format

Character string specifying the output format.

"data.frame"

(Default) Returns a data frame with one row per logged result

"list"

Returns the raw list of memtoc_result objects

Value

A data frame (default) or list containing logged results. The data frame has columns:

msg

Label from tic_mem(), or NA if none provided

mem_start

Memory (RSS) in bytes at start

mem_end

Memory (RSS) in bytes at end

mem_peak

Peak memory in bytes

mem_change

Change in memory (bytes)

elapsed

Elapsed time in seconds

tic_timestamp

When tic_mem() was called

toc_timestamp

When toc_mem() was called

See Also

mem_clearlog() to clear the log, toc_mem() with log = TRUE

Examples

mem_clearlog()
tic_mem("step 1", interval = NULL, workers = "none")
x <- numeric(100)
toc_mem(log = TRUE, quiet = TRUE)
tic_mem("step 2", interval = NULL, workers = "none")
y <- sum(x)
toc_mem(log = TRUE, quiet = TRUE)
mem_log()
mem_clearlog()

Get information about current parallel setup

Description

Returns diagnostic information about the detected parallel backend and any workers that can be monitored.

Usage

mem_parallel_info()

Value

A list with parallel backend information

Examples

info <- mem_parallel_info()
info$main_pid
info$worker_pids

Print formatted log output

Description

Displays the memory tracking log in a human-readable format, similar to how tictoc's tic.log() output can be printed with writeLines().

Usage

mem_print_log()

Value

Invisibly returns a character vector of formatted log lines.

Examples

mem_clearlog()
tic_mem("step 1", interval = NULL, workers = "none")
x <- numeric(100)
toc_mem(log = TRUE, quiet = TRUE)
tic_mem("step 2", interval = NULL, workers = "none")
y <- sum(x)
toc_mem(log = TRUE, quiet = TRUE)
mem_print_log()
mem_clearlog()

Recover data from a crashed monitoring session

Description

Attempts to recover memory samples from a previous session that crashed or was interrupted before toc_mem() was called. The monitor periodically saves checkpoints to disk, so some data may be recoverable.

Usage

mem_recover(pid = NULL, path = NULL)

Arguments

pid

Process ID for a checkpoint in the current session's temporary directory. If NULL, lists available recovery files in that directory.

path

Direct path to a recovery file. Overrides pid if provided.

Details

Checkpoints are stored in the current R session's temporary directory. After restarting R, use path to locate a surviving checkpoint in the previous session's temporary directory; recovery is not guaranteed if that directory has been removed. Normal completion removes checkpoints. Nested blocks have separate files and can be recovered using path.

Value

If pid or path is provided, returns a data frame of recovered samples or NULL if not found. If both are NULL, returns a list of available recovery files with their metadata.

Examples

mem_recover()

# Recover a small example checkpoint from a temporary file.
local({
  path <- tempfile(fileext = ".rds")
  on.exit(unlink(path))
  samples <- data.frame(
    timestamp = Sys.time(),
    pid = Sys.getpid(),
    rss = 1024^2
  )
  saveRDS(samples, path)
  recovered <- mem_recover(path = path)
  stopifnot(identical(recovered, samples))
  recovered
})

Print method for memtoc_result objects

Description

Print method for memtoc_result objects

Usage

## S3 method for class 'memtoc_result'
print(x, ...)

Arguments

x

A memtoc_result object

...

Additional arguments (ignored)

Value

Invisibly returns x

Start memory tracking

Description

Begins a memory tracking block. Call toc_mem() to end the block and see the results. Multiple calls to tic_mem() can be nested, and each toc_mem() will match with the most recent unmatched tic_mem().

Usage

tic_mem(msg = NULL, quiet = TRUE, interval = 1, workers = "auto")

Arguments

msg

Optional character string label for this tracking block. This label is displayed in the output from toc_mem() and stored in the log if log = TRUE is passed to toc_mem().

quiet

Logical. If TRUE (default), no message is printed when tracking starts. Set to FALSE to see a startup message.

interval

Numeric. Polling interval in seconds for background monitoring. Set to NULL or 0 to disable background polling and use snapshot-only mode (faster but only captures start/end memory). Default is 1 second.

workers

Worker specification for parallel monitoring:

  • "auto" (default): Automatically detect future workers and child processes

  • "none": Only monitor the main R process

  • "children": Monitor main process and all child processes

  • Integer vector: Explicit list of PIDs to monitor

Details

When interval is set (default), a background R process is spawned to sample memory usage. The reported peak is the maximum observed sample; allocations between samples may be missed. Starting and running the background process adds overhead.

For very short operations (under 1 second), consider using interval = NULL to avoid the background process startup overhead (~200ms).

Parallel Worker Monitoring

When using future for parallel processing, memtoc can automatically detect and monitor worker processes. Set workers = "auto" to enable this feature. The trajectory will include memory samples from all workers, and the result will show aggregate statistics.

Value

Invisibly returns the timestamp when tracking started.

See Also

toc_mem() to stop tracking, mem_log() to retrieve logged results, mem_parallel_info() to check parallel backend status

Examples

# Track a small allocation using start and end snapshots.
tic_mem("small allocation", interval = NULL, workers = "none")
x <- numeric(1000)
toc_mem()

# Nested blocks are stopped in reverse order.
tic_mem("outer", interval = NULL, workers = "none")
tic_mem("inner", interval = NULL, workers = "none")
y <- sum(x)
toc_mem()
toc_mem()

Stop memory tracking and report results

Description

Ends a memory tracking block started by tic_mem() and reports the results. By default, prints a summary message showing peak memory, current memory, and elapsed time.

Usage

toc_mem(log = FALSE, quiet = FALSE)

Arguments

log

Logical. If TRUE, stores the result in an internal log that can be retrieved later with mem_log(). Default is FALSE.

quiet

Logical. If TRUE, suppresses the output message. Default is FALSE (message is shown).

Value

Invisibly returns a memtoc_result object (a list) containing:

msg

The label passed to tic_mem(), or NULL

mem_start

Memory (RSS) in bytes at start (main process)

mem_end

Memory (RSS) in bytes at end (main process)

mem_peak

Peak memory during the interval (total across all processes)

mem_change

Change in memory (end - start) in bytes

elapsed

Elapsed time in seconds

tic_timestamp

POSIXct timestamp when tic_mem() was called

toc_timestamp

POSIXct timestamp when toc_mem() was called

trajectory

Data frame of memory samples (if background polling was used)

n_samples

Number of samples collected

n_workers

Number of worker processes monitored (0 if main process only)

worker_stats

Data frame with per-worker peak memory (if workers monitored)

See Also

tic_mem() to start tracking, mem_log() to retrieve logged results

Examples

tic_mem("small allocation", interval = NULL, workers = "none")
x <- numeric(1000)
result <- toc_mem()
result$elapsed
result$mem_peak
result$n_samples

# Brief background polling; sample availability depends on startup time.
tic_mem("polling", interval = 0.1, workers = "none")
Sys.sleep(0.3)
polled <- toc_mem()
if (!is.null(polled$trajectory)) {
  head(polled$trajectory)
}

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