genproc: deep dive

The README introduces the five layers on a minimal example. This vignette walks through the pieces a user needs once they start depending on genproc for real work: the shape of the result, how errors are reported, how the optional layers compose, and what the current edges are.

A small working example

The same synthetic file-conversion task as in the README — one row per file, convert() is the per-case function.

src_dir <- file.path(tempdir(), "genproc-vignette-src")
dst_dir <- file.path(tempdir(), "genproc-vignette-dst")
dir.create(src_dir, showWarnings = FALSE, recursive = TRUE)
dir.create(dst_dir, showWarnings = FALSE, recursive = TRUE)

write.csv(head(iris),       file.path(src_dir, "a.csv"), row.names = FALSE)
write.csv(head(mtcars),     file.path(src_dir, "b.csv"), row.names = FALSE)
write.csv(head(airquality), file.path(src_dir, "c.csv"), row.names = FALSE)

convert <- function(src_dir, src_file, dst_dir, dst_file) {
  df <- read.csv(file.path(src_dir, src_file))
  saveRDS(df, file.path(dst_dir, dst_file))
}

mask <- data.frame(
  src_dir  = src_dir,
  src_file = c("a.csv", "b.csv", "c.csv"),
  dst_dir  = dst_dir,
  dst_file = c("a.rds", "b.rds", "c.rds"),
  stringsAsFactors = FALSE
)

result <- genproc(convert, mask)

Anatomy of `genproc_result`

result is an S3 list with a stable, documented set of fields:

class(result)
#> [1] "genproc_result"
names(result)
#> [1] "log"                 "reproducibility"     "n_success"          
#> [4] "n_error"             "duration_total_secs" "status"

log: one row per case. Columns, in order: case_id, the mask parameter values (in the order they appear in the mask), then success, error_message, traceback, duration_secs.
reproducibility: the environment snapshot captured at run start (see below).
n_success, n_error: summary counts.
duration_total_secs: total wall-clock time.
status: "done" for a completed synchronous run; for a non-blocking run, one of "running" (future not resolved yet), "done (not collected)" (future resolved but result not yet collected via await()), "done" (collected), or "error".

These fields are guaranteed stable across minor versions; new fields may be added (e.g. worker_id for parallel runs), but existing ones will never be removed or renamed.

The log

Column order is designed for a human scanning a run:

result$log
#>     case_id
#> 1 case_0001
#> 2 case_0002
#> 3 case_0003
#>                                                                   src_dir
#> 1 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src
#> 2 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src
#> 3 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src
#>   src_file
#> 1    a.csv
#> 2    b.csv
#> 3    c.csv
#>                                                                   dst_dir
#> 1 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst
#> 2 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst
#> 3 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst
#>   dst_file success error_message traceback duration_secs
#> 1    a.rds    TRUE          <NA>      <NA>          0.00
#> 2    b.rds    TRUE          <NA>      <NA>          0.02
#> 3    c.rds    TRUE          <NA>      <NA>          0.00

case_id is stable and index-based (case_0001, case_0002, …) for now. A content-based variant is on the roadmap, for use cases where rows of the mask can be reordered between runs.

The reproducibility snapshot

str(result$reproducibility, max.level = 1)
#> List of 11
#>  $ timestamp    : POSIXct[1:1], format: "2026-05-07 21:14:19"
#>  $ r_version    : chr "R version 4.5.1 (2025-06-13 ucrt)"
#>  $ platform     : chr "x86_64-w64-mingw32"
#>  $ os           : chr "Windows 10 x64"
#>  $ locale       : chr "LC_COLLATE=C;LC_CTYPE=French_France.utf8;LC_MONETARY=French_France.utf8;LC_NUMERIC=C;LC_TIME=French_France.utf8"
#>  $ timezone     : chr "Europe/Paris"
#>  $ packages     : Named chr [1:29] "0.2.0" "0.6.39" "2.6.1" "1.2.0" ...
#>   ..- attr(*, "names")= chr [1:29] "genproc" "digest" "R6" "fastmap" ...
#>  $ mask_snapshot:'data.frame':   3 obs. of  4 variables:
#>  $ parallel     : NULL
#>  $ nonblocking  : NULL
#>  $ inputs       :List of 3

timestamp: POSIXct, start time of the run.
r_version, platform, os, locale, timezone: system info.
packages: named character vector of package -> version, for every package attached or loaded via namespace at run start.
mask_snapshot: the exact mask used (not a copy of a reference — the value).
parallel: NULL for a sequential run, or a plain list mirroring the parallel_spec() used. Dropped class to make the snapshot portable to serialization formats. The list also carries effective_strategy: the strategy actually applied by genproc(), which differs from strategy when the user passed workers without an explicit strategy and genproc() auto-defaulted to "multisession". Both fields are recorded so the snapshot is self-explanatory: strategy is what the user asked for, effective_strategy is what was applied.
nonblocking: same pattern, for nonblocking_spec().
inputs: a fingerprint of every file the mask refers to, or NULL if input tracking was disabled (track_inputs = FALSE). See the next section.

The snapshot lives inside the result. You can compare two results by comparing their $reproducibility slots directly.

Input fingerprinting

reproducibility$inputs is the layer that protects against silent drift of upstream files. It is captured at t0 of the run, alongside the rest of the snapshot.

str(result$reproducibility$inputs, max.level = 1)
#> List of 3
#>  $ method: chr "stat"
#>  $ files :'data.frame':  0 obs. of  3 variables:
#>  $ refs  :'data.frame':  0 obs. of  3 variables:

method: currently "stat". Reserved for future extensions (e.g. "md5" for content hashing).
files: a deduplicated table of every file referenced by the mask, with its size in bytes and last-modified time. One row per unique path: a config file shared across 100 cases produces a single row.
refs: the (case_id, column, path) triples saying who referenced what. Joins back to files by path.

Heuristic detection

By default, every character column of the mask whose non-NA values are existing files (and contain a path separator) is treated as an input column. The mask used in this vignette has its paths split across src_dir and src_file, so the heuristic finds nothing useful — src_dir is a directory (excluded), src_file values are bare names (no separator). For a mask that holds absolute paths directly:

mask_paths <- data.frame(
  csv_in = file.path(src_dir, c("a.csv", "b.csv", "c.csv")),
  stringsAsFactors = FALSE
)
do_one <- function(csv_in) nrow(read.csv(csv_in))

run0 <- genproc(do_one, mask_paths)
run0$reproducibility$inputs$files
#>                                                                      path size
#> 1 C:/Users/rheri/AppData/Local/Temp/RtmpM78eGk/genproc-vignette-src/a.csv  221
#> 2 C:/Users/rheri/AppData/Local/Temp/RtmpM78eGk/genproc-vignette-src/b.csv  303
#> 3 C:/Users/rheri/AppData/Local/Temp/RtmpM78eGk/genproc-vignette-src/c.csv  161
#>                 mtime
#> 1 2026-05-07 21:14:19
#> 2 2026-05-07 21:14:19
#> 3 2026-05-07 21:14:19

Shared inputs are deduplicated

Many cases referencing the same upstream file produce a single row in files but one row per case in refs. This keeps the snapshot economical for masks where every case shares a configuration, schema, or lookup table.

config_path <- file.path(src_dir, "config.yml")
writeLines("threshold: 10", config_path)

mask_with_config <- data.frame(
  csv_in = file.path(src_dir, c("a.csv", "b.csv", "c.csv")),
  config = config_path,                       # same value across rows
  stringsAsFactors = FALSE
)
do_one_cfg <- function(csv_in, config) nrow(read.csv(csv_in))

run_shared <- genproc(do_one_cfg, mask_with_config)

# 4 rows: 3 unique csv_in + 1 config
nrow(run_shared$reproducibility$inputs$files)
#> [1] 4

# 6 rows: 3 cases x 2 input columns
nrow(run_shared$reproducibility$inputs$refs)
#> [1] 6

Overrides

genproc(..., input_cols = c("col1", "col2")) bypasses the heuristic and tracks exactly the named columns. Paths that don’t exist at capture time are recorded with NA size/mtime and a warning is emitted.
genproc(..., skip_input_cols = c("col")) keeps the heuristic but excludes a column (useful when a label column happens to match an existing file in cwd).
genproc(..., track_inputs = FALSE) disables tracking entirely. result$reproducibility$inputs is NULL.

input_cols and skip_input_cols are mutually exclusive. Mixing them raises an error — the two flags express contradictory intentions and the call should clarify.

Comparing runs with `diff_inputs()`

# Rewrite a.csv with strictly more content (size changes)
write.csv(iris, file.path(src_dir, "a.csv"), row.names = FALSE)

run1 <- genproc(do_one, mask_paths)
diff_inputs(run0, run1)
#> genproc input diff (method: stat)
#>   Changed:   1
#>   Unchanged: 2
#>   Removed:   0
#>   Added:     0
#>   Cases affected: 1
#> 
#> Changed files:
#>   C:/Users/rheri/AppData/Local/Temp/RtmpM78eGk/genproc-vignette-src/a.csv
#>       size:  221 B -> 4.1 KB
#>       mtime: 2026-05-07 21:14:19 -> 2026-05-07 21:14:19
#> 
#> Cases affected (use rerun_affected() to re-run):
#>   case_0001

diff_inputs() returns an S3 object (genproc_input_diff) with a print method for human reading and named list components for programmatic access ($changed, $unchanged, $removed, $added). Files are matched by canonical absolute path; cross-machine comparison would need a separate matcher and is out of scope for this version.

diff_inputs() refuses to compare snapshots produced with different methods (forward-compatible with a future hash mode).

How errors are reported

A case that throws does not stop the run. Here we delete a source file between two runs:

file.remove(file.path(src_dir, "b.csv"))
#> [1] TRUE
result_broken <- genproc(convert, mask)
#> Warning in file(file, "rt"): cannot open file
#> 'C:\Users\rheri\AppData\Local\Temp\RtmpM78eGk/genproc-vignette-src/b.csv': No
#> such file or directory

result_broken$n_success
#> [1] 2
result_broken$n_error
#> [1] 1

The failing row carries the error message and a filtered traceback. The errors() helper subsets log on success == FALSE:

bad <- errors(result_broken)
bad$error_message
#> [1] "cannot open the connection"
cat(bad$traceback[1], "\n")
#> 1. read.csv(file.path(src_dir, src_file))
#> 2. read.table(file = file, header = header, sep = sep, quote = quote, dec = dec, fill = fill, comment.char = comment.ch ...
#> 3. file(file, "rt")

The traceback is captured via withCallingHandlers() at the moment the error is thrown — it is the real R call stack, not a string pulled out of conditionMessage(). The internal tryCatch() and withCallingHandlers() frames are filtered out so the trace reads like a normal R error.

Restore the file for subsequent sections:

write.csv(head(mtcars), file.path(src_dir, "b.csv"), row.names = FALSE)

Inspecting and re-running a result

Three helpers digest a genproc_result without touching result$log directly.

errors(result) returns the subset of failed cases as a data.frame:

errors(result_broken)

summary(result) dispatches to summary.genproc_result() and returns a printable digest with the run status, success rate, duration statistics (mean / max / slowest case_id) and the top recurring error messages:

summary(result_broken)
#> genproc result summary
#>   Status     : done
#>   Cases      : 3 (2 ok, 1 error)
#>   Success    : 67%
#>   Total time : 0.02s
#>   Per case   : mean 0.000s, max 0.000s (slowest: case_0001)
#> 
#> Top errors:
#>     1x  cannot open the connection

The summary object is a list — the printed view is rendered by print.genproc_result_summary() for human reading; the underlying fields (status, success_rate, duration_stats, top_errors) are programmatically accessible too.

Two more helpers close the loop by re-running a targeted subset. Their case_ids are local to the subset (re-numbered starting at case_0001); the link back to the original run is via the matching rows of r0$reproducibility$mask_snapshot.

rerun_failed(r0, f) re-runs only the cases that failed in r0 — useful after fixing the function or the input:

# Re-run with a hardened f that handles the missing file gracefully.
result_fixed <- rerun_failed(
  result_broken,
  f = function(src_dir, src_file, dst_dir, dst_file) {
    in_path <- file.path(src_dir, src_file)
    if (!file.exists(in_path)) return(NA)
    df <- read.csv(in_path)
    saveRDS(df, file.path(dst_dir, dst_file))
  }
)

rerun_affected(r0, diff, f) re-runs only the cases referenced by a diff_inputs() result — the natural action after detecting that some upstream input files have drifted:

d <- diff_inputs(run0, run1)
refreshed <- rerun_affected(run0, d, f = do_one)

This closes the reproducibility loop: detect drift → re-run only the cases whose inputs changed, not the whole mask.

Building blocks: extracting `f` and the mask from a working example

The vignette so far assumed both f and mask were already written by hand. In practice you often start from a working script for one specific case and want to derive the parameterized function and the mask template automatically. Three exported helpers do this, in order.

1. `from_example_to_function()` — example expression to function

Take an example expression that works on one case. Every external value (string literals, environment symbols that are not functions) becomes a parameter of the resulting function, with its current value stored as the default. Locally bound symbols (assignment targets, function formals) are protected.

# An example that works for ONE specific case
input_path  <- file.path(src_dir, "a.csv")
output_path <- file.path(dst_dir, "a-from-example.rds")

example <- expression({
  df <- read.csv(input_path)
  saveRDS(df, output_path)
})

fn <- from_example_to_function(example)
formals(fn)
#> $param_1
#> [1] "C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src/a.csv"
#> 
#> $param_2
#> [1] "C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst/a-from-example.rds"

2. `from_function_to_mask()` — function signature to mask template

Once you have the function, derive a one-row template data.frame that mirrors its signature. You can then rbind() extra rows to build a full mask.

mask_template <- from_function_to_mask(fn)
mask_template
#>                                                                         param_1
#> 1 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src/a.csv
#>                                                                                      param_2
#> 1 C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst/a-from-example.rds

3. `rename_function_params()` — give the parameters domain names

The auto-generated names (param_1, param_2, …) are stable but not informative. Rename them in place — formals and body are updated together, the function source is not edited.

fn_named <- rename_function_params(
  fn, c(param_1 = "input_path", param_2 = "output_path")
)
formals(fn_named)
#> $input_path
#> [1] "C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-src/a.csv"
#> 
#> $output_path
#> [1] "C:\\Users\\rheri\\AppData\\Local\\Temp\\RtmpM78eGk/genproc-vignette-dst/a-from-example.rds"

Putting it together: a renamed function plus a manually-built mask that follows the same column names.

mask_built <- data.frame(
  input_path  = file.path(src_dir, c("a.csv", "b.csv", "c.csv")),
  output_path = file.path(dst_dir, c("a2.rds", "b2.rds", "c2.rds")),
  stringsAsFactors = FALSE
)
genproc(fn_named, mask_built)$n_success
#> [1] 3

The f_mapping argument shown in the next section is the inline equivalent of step 3 — convenient when you don’t need a renamed function for any other purpose than this single genproc() call.

Parameter renaming with `f_mapping`

If the function you already have uses parameter names that don’t match your mask’s column names, f_mapping renames them in place without touching the source:

# `f` uses generic names; the mask uses domain names.
f <- function(input_dir, input_file, output_dir, output_file) {
  df <- read.csv(file.path(input_dir, input_file))
  saveRDS(df, file.path(output_dir, output_file))
}

genproc(
  f = f,
  mask = mask,
  f_mapping = c(
    "input_dir"   = "src_dir",
    "input_file"  = "src_file",
    "output_dir"  = "dst_dir",
    "output_file" = "dst_file"
  )
)

Parallel execution in depth

parallel_spec() records intent only; the actual workers are started lazily by future when the plan is resolved.

Power-user pattern: manage the plan yourself

Across many genproc() calls in the same session, installing the plan once amortizes worker startup:

future::plan(future::multisession, workers = 6)

result_1 <- genproc(convert, mask, parallel = parallel_spec())
result_2 <- genproc(convert, mask, parallel = parallel_spec())
# reuses the same workers

One-off pattern: let genproc install the plan

When workers is passed without strategy, genproc() auto-defaults to multisession for that single call and restores the previous plan on exit. This avoids the silent trap of workers = N being ignored because the current plan is sequential:

genproc(convert, mask, parallel = parallel_spec(workers = 4))

Amortization threshold

multisession costs roughly 1–3 seconds per worker at first use. Below ~10 seconds of real work, a parallel run can be slower than a sequential one. Don’t read a single short benchmark as evidence of a bug: run at workload size.

RNG reproducibility

parallel_spec(seed = TRUE) (the default) draws independent L’Ecuyer-CMRG streams from a random master seed. Identical master seed — identical per-case RNG state regardless of worker count or chunking. To pin the master, pass an integer: parallel_spec(seed = 42L).

Non-blocking execution in depth

job <- genproc(convert, mask, nonblocking = nonblocking_spec())
status(job)         # "running", "done (not collected)", "done", or "error"
job <- await(job)   # blocks until resolution
job$log

What’s in the skeleton

Before await():

log, n_success, n_error, duration_total_secs are NULL.
reproducibility is already populated — the snapshot is captured synchronously, before the future is submitted, so it reflects t0.
status is "running".
The future itself is stored in attr(x, "future").

After await():

All fields populated as in a synchronous run.
attr(x, "future") is removed.
status is "done" — or "error" if the wrapper future itself crashed (a rare case: per-case errors are caught by the logging layer and don’t propagate to the wrapper).

Idempotence

await() is idempotent. Calling it on an object that has already been materialized (or was synchronous to begin with) returns it unchanged. This makes it safe to pepper in user code without tracking whether a particular result has already been collected.

Default strategy: why `"multisession"`

Unlike parallel_spec(), nonblocking_spec() defaults to strategy = "multisession", not NULL. Rationale: a function named “non-blocking” must not silently block because the current future::plan() is sequential (the default in a fresh R session). Power-users who manage their plan can pass strategy = NULL explicitly to defer.

Plan lifetime

When nonblocking_spec() installs a plan, the previous plan is not restored on genproc() exit — it is restored by await() once the future has been collected. Restoring earlier would call future::plan() while the wrapper future is still running, which shuts down the multisession workers and surfaces a “Future was canceled” error at collection time. The trade-off is that if you never call await(), the installed plan stays active for the rest of the session. Power-users who pass strategy = NULL and manage the plan themselves are not affected.

Composition: parallel × non-blocking

The two optional layers are orthogonal:

job <- genproc(
  convert, mask,
  parallel    = parallel_spec(workers = 6),
  nonblocking = nonblocking_spec()
)
# get control back immediately
# ... do other work ...
job <- await(job)

The non-blocking layer launches one outer future. Inside it, the parallel layer dispatches cases via future.apply. Note: with both set to multisession, future.apply detects it is already inside a future and degrades the inner layer to sequential by default, to avoid worker explosion. For true nested parallelism, install future::plan(list(...)) explicitly and pass strategy = NULL on both specs.

Default `mc.cores` in the wrapper subprocess

On Windows and in some RStudio configurations, the wrapper subprocess inherits getOption("mc.cores") set to 1 (the legacy default for parallel::mclapply(), which is a no-op on Windows). Without intervention, parallelly would refuse to spawn the inner workers because workers / 1 exceeds the localhost hard limit, and the composed call would fail with a confusing "only 1 CPU cores available for this R process (per 'mc.cores')" error.

genproc() handles this transparently. In the composed case (parallel != NULL && nonblocking != NULL), it makes two adjustments inside the wrapper subprocess:

Sets R_PARALLELLY_AVAILABLECORES_METHODS = "system" so that parallelly ignores mc.cores and uses the true detected core count for its hard-limit check.
Raises options(mc.cores) from 1 to the system core count, so that parallelly’s soft-limit warning (“only 1 CPU cores available… 200% load”) does not fire with a misleading message after the hard limit has been lifted.

Both adjustments only apply if the user has not set their own values, and only inside the wrapper subprocess. The calling session is never modified.

genproc: deep dive

A small working example

Anatomy of `genproc_result`

The log

The reproducibility snapshot

Input fingerprinting

Heuristic detection

Shared inputs are deduplicated

Overrides

Comparing runs with `diff_inputs()`

How errors are reported

Inspecting and re-running a result

Building blocks: extracting `f` and the mask from a working example

1. `from_example_to_function()` — example expression to function

2. `from_function_to_mask()` — function signature to mask template

3. `rename_function_params()` — give the parameters domain names

Parameter renaming with `f_mapping`

Parallel execution in depth

Power-user pattern: manage the plan yourself

One-off pattern: let genproc install the plan

Amortization threshold

RNG reproducibility

Non-blocking execution in depth

What’s in the skeleton

Idempotence

Default strategy: why `"multisession"`

Plan lifetime

Composition: parallel × non-blocking

Default `mc.cores` in the wrapper subprocess

Progress monitoring

Current edges and roadmap

genproc: deep dive

A small working example

Anatomy of genproc_result

The log

The reproducibility snapshot

Input fingerprinting

Heuristic detection

Shared inputs are deduplicated

Overrides

Comparing runs with diff_inputs()

How errors are reported

Inspecting and re-running a result

Building blocks: extracting f and the mask from a working example

1. from_example_to_function() — example expression to function

2. from_function_to_mask() — function signature to mask template

3. rename_function_params() — give the parameters domain names

Parameter renaming with f_mapping

Parallel execution in depth

Power-user pattern: manage the plan yourself

One-off pattern: let genproc install the plan

Amortization threshold

RNG reproducibility

Non-blocking execution in depth

What’s in the skeleton

Idempotence

Default strategy: why "multisession"

Plan lifetime

Composition: parallel × non-blocking

Default mc.cores in the wrapper subprocess

Progress monitoring

Current edges and roadmap

Anatomy of `genproc_result`

Comparing runs with `diff_inputs()`

Building blocks: extracting `f` and the mask from a working example

1. `from_example_to_function()` — example expression to function

2. `from_function_to_mask()` — function signature to mask template

3. `rename_function_params()` — give the parameters domain names

Parameter renaming with `f_mapping`

Default strategy: why `"multisession"`

Default `mc.cores` in the wrapper subprocess