M sstats big/work/20260513 arrow chunking followups#17
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R/clean_spectronaut.R:9-12: added block_size parameter (default 16L * 1024L * 1024L) with coerce + validation. R/clean_spectronaut.R:44: CsvReadOptions$create now uses the parameter. R/converters.R:120-125: new @param block_size roxygen with the straddling-object workaround note. R/converters.R:148-156: bigSpectronauttoMSstatsFormat gains block_size, plumbed to reduceBigSpectronaut. tests/testthat/test-converters.R:97-163: validation tests (rejects negative/zero/NA/vector/string) + plumbing tests (default forwards 16 MiB, override forwards user's value). man/bigSpectronauttoMSstatsFormat.Rd: regenerated from roxygen.
…setnames so the package is data.table-aware (cedta()). R/clean_spectronaut.R:103-187: rewrote cleanSpectronautChunk in data.table: setDT(input) at entry; subsequent operations modify in place via :=. Two-step rename (setnames for standardize, then setnames with skip_absent = TRUE to map standardized→MSstats) matches the MSstatsConvert family pattern. Conditional NA assignment uses mask form dt[cond, Intensity := NA_real_]. Q-value filters preserve dplyr::if_else NA semantics via explicit is.na(EGQvalue) | EGQvalue >= cutoff. Dropped the leftover dplyr::collect(head(dplyr::select(...))) pattern — was a no-op residue from a prior refactor. Function shrank from ~88 lines to ~64. DESCRIPTION:20: added data.table to Imports. NAMESPACE: regenerated, now imports :=, .SD, setDT, setnames from data.table. tests/testthat/test-converters.R:97-211: 5 new tests — schema smoke test, filter_by_excluded, filter_by_identified, filter_by_qvalue (covering the NA-q-value case), and FFrgLossType row drop.
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📝 WalkthroughWalkthroughThis PR refactors Spectronaut CSV processing to use Apache Arrow record-batch streaming instead of readr chunking, converts chunk processing from dplyr to data.table operations, and introduces a configurable ChangesSpectronaut Arrow Streaming & Data.Table Refactor
Sequence Diagram(s)sequenceDiagram
participant User
participant bigSpectronauttoMSstatsFormat
participant reduceBigSpectronaut
participant ArrowDataset
User->>bigSpectronauttoMSstatsFormat: call (optional block_size)
bigSpectronauttoMSstatsFormat->>reduceBigSpectronaut: forward block_size
reduceBigSpectronaut->>ArrowDataset: configure CSV reader with block_size
ArrowDataset->>reduceBigSpectronaut: return record batches
reduceBigSpectronaut->>cleanSpectronautChunk: process each batch
cleanSpectronautChunk->>reduceBigSpectronaut: write processed chunk
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~25 minutes Possibly related PRs
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Actionable comments posted: 3
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In `@R/clean_spectronaut.R`:
- Around line 155-167: The current filter block assumes columns Excluded,
Identified, EGQvalue, PGQvalue, and FFrgLossType always exist and will error if
any are missing; update the logic in clean_spectronaut.R to guard each filter by
checking column presence (e.g., use "if ('Excluded' %in% names(input))" before
the Excluded filter, similarly for Identified, EGQvalue and PGQvalue before
applying qvalue-based NA assignment, and check for FFrgLossType before
subsetting), so each conditional only runs when its target column exists and the
behavior remains unchanged otherwise.
- Around line 25-58: The computed needed_cols is never passed to Arrow, so the
CSV reader still parses all columns; update the CsvConvertOptions usage to
include the projection by calling
arrow::CsvConvertOptions$create(include_columns = needed_cols) (or set the
include_columns field on convert_opts after creation) so that convert_opts
includes needed_cols before calling arrow::open_dataset/Scanner$create;
reference the symbols needed_cols and convert_opts (and the call
arrow::CsvConvertOptions$create) and ensure this happens prior to creating
ds/reader.
In `@tests/testthat/test-converters.R`:
- Around line 106-113: The test's expect_error calls are too broad—change them
to assert the error message mentions "block_size" so they only pass when
block_size validation fails; update each expect_error(reduceBigSpectronaut(...),
...) in tests/testthat/test-converters.R to include a second argument (string or
regex) that matches "block_size" (e.g., "block_size" or "block_size.*invalid")
for the negative values and vector cases, and similarly for the
suppressed-warning call so all invalid-block_size cases are checked by message
content rather than any error.
🪄 Autofix (Beta)
Fix all unresolved CodeRabbit comments on this PR:
- Push a commit to this branch (recommended)
- Create a new PR with the fixes
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DESCRIPTIONNAMESPACER/clean_spectronaut.RR/converters.Rman/bigSpectronauttoMSstatsFormat.Rdman/dot-prefixedPath.Rdtests/testthat/test-converters.R
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| # Columns cleanSpectronautChunk actually consumes; Arrow's | ||
| # convert_options$include_columns drops everything else at parse time so | ||
| # we never materialize the ~35 unused columns Spectronaut exports. | ||
| needed_cols <- c("R.FileName", "R.Condition", "R.Replicate", | ||
| "PG.ProteinAccessions", "EG.ModifiedSequence", | ||
| "FG.LabeledSequence", "FG.Charge", | ||
| "F.FrgIon", "F.Charge", | ||
| "EG.Identified", "F.ExcludedFromQuantification", | ||
| "F.FrgLossType", "PG.Qvalue", "EG.Qvalue", | ||
| intensity) | ||
| if (calculateAnomalyScores) { | ||
| needed_cols <- c(needed_cols, anomalyModelFeatures) | ||
| } | ||
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| # Arrow's CSV reader replaces readr::read_delim_chunked. Arrow releases | ||
| # per-batch state as soon as a batch is consumed, so peak memory is | ||
| # bounded by one record batch instead of growing with the dataset (readr | ||
| # keeps a string-interning pool that accumulates across chunks). The | ||
| # `delim` switch above already covers comma / tab / semicolon variants; | ||
| # Arrow's CSV reader handles all three the same way through | ||
| # CsvParseOptions$delimiter. | ||
| parse_opts <- arrow::CsvParseOptions$create(delimiter = delim) | ||
| convert_opts <- arrow::CsvConvertOptions$create() | ||
| read_opts <- arrow::CsvReadOptions$create(block_size = block_size) | ||
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| ds <- arrow::open_dataset( | ||
| input_file, | ||
| format = "csv", | ||
| parse_options = parse_opts, | ||
| convert_options = convert_opts, | ||
| read_options = read_opts | ||
| ) | ||
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| reader <- arrow::Scanner$create(ds)$ToRecordBatchReader() |
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🧩 Analysis chain
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# Verify whether column projection is actually wired in the Arrow path.
rg -n -C2 'needed_cols|CsvConvertOptions\\$create|include_columns|Scanner\\$create|open_dataset\\(' R/clean_spectronaut.RRepository: Vitek-Lab/MSstatsBig
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#!/bin/bash
# First, check for needed_cols usage in the file
echo "=== Searching for needed_cols usage ==="
rg -n 'needed_cols' R/clean_spectronaut.R
echo ""
echo "=== Searching for CsvConvertOptions and include_columns ==="
rg -n 'CsvConvertOptions\|include_columns' R/clean_spectronaut.R
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echo "=== Checking context around CsvConvertOptions create call ==="
rg -n -B3 -A3 'CsvConvertOptions\$create' R/clean_spectronaut.RRepository: Vitek-Lab/MSstatsBig
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echo "=== Code from line 50 onwards ==="
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wc -l R/clean_spectronaut.RRepository: Vitek-Lab/MSstatsBig
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#!/bin/bash
# Search for include_columns usage in the entire codebase
echo "=== Searching for include_columns in codebase ==="
rg -n 'include_columns' .
echo ""
echo "=== Searching for CsvConvertOptions usage patterns ==="
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Wire needed_cols into Arrow's column projection.
needed_cols is computed but never applied to CsvConvertOptions, so the reader still parses all CSV columns. This defeats the intended memory/performance reduction on wide Spectronaut exports.
💡 Proposed fix
- convert_opts <- arrow::CsvConvertOptions$create()
+ convert_opts <- arrow::CsvConvertOptions$create(
+ include_columns = needed_cols
+ )🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@R/clean_spectronaut.R` around lines 25 - 58, The computed needed_cols is
never passed to Arrow, so the CSV reader still parses all columns; update the
CsvConvertOptions usage to include the projection by calling
arrow::CsvConvertOptions$create(include_columns = needed_cols) (or set the
include_columns field on convert_opts after creation) so that convert_opts
includes needed_cols before calling arrow::open_dataset/Scanner$create;
reference the symbols needed_cols and convert_opts (and the call
arrow::CsvConvertOptions$create) and ensure this happens prior to creating
ds/reader.
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| if (filter_by_excluded) { | ||
| input <- dplyr::mutate( | ||
| input, Intensity = dplyr::if_else(Excluded, NA_real_, Intensity)) | ||
|
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| input[Excluded == TRUE, Intensity := NA_real_] | ||
| } | ||
|
|
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| if (filter_by_identified) { | ||
| input <- dplyr::mutate( | ||
| input, Intensity = dplyr::if_else(Identified, Intensity, NA_real_)) | ||
| input[Identified == FALSE, Intensity := NA_real_] | ||
| } | ||
|
|
||
| if (filter_by_qvalue) { | ||
| input <- dplyr::mutate( | ||
| input, | ||
| Intensity = dplyr::if_else(EGQvalue < qvalue_cutoff, Intensity, NA_real_)) | ||
| input <- dplyr::mutate( | ||
| input, | ||
| Intensity = dplyr::if_else(PGQvalue < qvalue_cutoff, Intensity, NA_real_)) | ||
| # Preserve dplyr::if_else semantics: rows with NA q-values become NA. | ||
| input[is.na(EGQvalue) | EGQvalue >= qvalue_cutoff, Intensity := NA_real_] | ||
| input[is.na(PGQvalue) | PGQvalue >= qvalue_cutoff, Intensity := NA_real_] | ||
| } | ||
|
|
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| input <- dplyr::filter(input, FFrgLossType == "noloss") | ||
| if (is.element("LabeledSequence", colnames(input))) { | ||
| input <- dplyr::mutate(input, IsLabeled = grepl("Lys8", LabeledSequence) | grepl("Arg10", LabeledSequence)) | ||
| input <- dplyr::mutate(input, IsotopeLabelType := dplyr::if_else(IsLabeled, "H", "L")) | ||
|
|
||
| input <- input[FFrgLossType == "noloss"] |
There was a problem hiding this comment.
Guard filter steps when selected columns are absent.
After present_orig subsetting, downstream filters still assume Excluded, Identified, EGQvalue, PGQvalue, and FFrgLossType exist. Missing columns here will raise runtime errors.
🛡️ Proposed fix
- if (filter_by_excluded) {
+ if (filter_by_excluded && "Excluded" %in% colnames(input)) {
input[Excluded == TRUE, Intensity := NA_real_]
}
- if (filter_by_identified) {
+ if (filter_by_identified && "Identified" %in% colnames(input)) {
input[Identified == FALSE, Intensity := NA_real_]
}
if (filter_by_qvalue) {
- input[is.na(EGQvalue) | EGQvalue >= qvalue_cutoff, Intensity := NA_real_]
- input[is.na(PGQvalue) | PGQvalue >= qvalue_cutoff, Intensity := NA_real_]
+ if ("EGQvalue" %in% colnames(input)) {
+ input[is.na(EGQvalue) | EGQvalue >= qvalue_cutoff, Intensity := NA_real_]
+ }
+ if ("PGQvalue" %in% colnames(input)) {
+ input[is.na(PGQvalue) | PGQvalue >= qvalue_cutoff, Intensity := NA_real_]
+ }
}
-
- input <- input[FFrgLossType == "noloss"]
+ if ("FFrgLossType" %in% colnames(input)) {
+ input <- input[FFrgLossType == "noloss"]
+ }📝 Committable suggestion
‼️ IMPORTANT
Carefully review the code before committing. Ensure that it accurately replaces the highlighted code, contains no missing lines, and has no issues with indentation. Thoroughly test & benchmark the code to ensure it meets the requirements.
Suggested change
| if (filter_by_excluded) { | |
| input <- dplyr::mutate( | |
| input, Intensity = dplyr::if_else(Excluded, NA_real_, Intensity)) | |
| input[Excluded == TRUE, Intensity := NA_real_] | |
| } | |
| if (filter_by_identified) { | |
| input <- dplyr::mutate( | |
| input, Intensity = dplyr::if_else(Identified, Intensity, NA_real_)) | |
| input[Identified == FALSE, Intensity := NA_real_] | |
| } | |
| if (filter_by_qvalue) { | |
| input <- dplyr::mutate( | |
| input, | |
| Intensity = dplyr::if_else(EGQvalue < qvalue_cutoff, Intensity, NA_real_)) | |
| input <- dplyr::mutate( | |
| input, | |
| Intensity = dplyr::if_else(PGQvalue < qvalue_cutoff, Intensity, NA_real_)) | |
| # Preserve dplyr::if_else semantics: rows with NA q-values become NA. | |
| input[is.na(EGQvalue) | EGQvalue >= qvalue_cutoff, Intensity := NA_real_] | |
| input[is.na(PGQvalue) | PGQvalue >= qvalue_cutoff, Intensity := NA_real_] | |
| } | |
| input <- dplyr::filter(input, FFrgLossType == "noloss") | |
| if (is.element("LabeledSequence", colnames(input))) { | |
| input <- dplyr::mutate(input, IsLabeled = grepl("Lys8", LabeledSequence) | grepl("Arg10", LabeledSequence)) | |
| input <- dplyr::mutate(input, IsotopeLabelType := dplyr::if_else(IsLabeled, "H", "L")) | |
| input <- input[FFrgLossType == "noloss"] | |
| if (filter_by_excluded && "Excluded" %in% colnames(input)) { | |
| input[Excluded == TRUE, Intensity := NA_real_] | |
| } | |
| if (filter_by_identified && "Identified" %in% colnames(input)) { | |
| input[Identified == FALSE, Intensity := NA_real_] | |
| } | |
| if (filter_by_qvalue) { | |
| # Preserve dplyr::if_else semantics: rows with NA q-values become NA. | |
| if ("EGQvalue" %in% colnames(input)) { | |
| input[is.na(EGQvalue) | EGQvalue >= qvalue_cutoff, Intensity := NA_real_] | |
| } | |
| if ("PGQvalue" %in% colnames(input)) { | |
| input[is.na(PGQvalue) | PGQvalue >= qvalue_cutoff, Intensity := NA_real_] | |
| } | |
| } | |
| if ("FFrgLossType" %in% colnames(input)) { | |
| input <- input[FFrgLossType == "noloss"] | |
| } |
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@R/clean_spectronaut.R` around lines 155 - 167, The current filter block
assumes columns Excluded, Identified, EGQvalue, PGQvalue, and FFrgLossType
always exist and will error if any are missing; update the logic in
clean_spectronaut.R to guard each filter by checking column presence (e.g., use
"if ('Excluded' %in% names(input))" before the Excluded filter, similarly for
Identified, EGQvalue and PGQvalue before applying qvalue-based NA assignment,
and check for FFrgLossType before subsetting), so each conditional only runs
when its target column exists and the behavior remains unchanged otherwise.
Comment on lines
+106
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+113
| expect_error(reduceBigSpectronaut(input_file, output_file, block_size = -1L)) | ||
| expect_error(reduceBigSpectronaut(input_file, output_file, block_size = 0L)) | ||
| expect_error(reduceBigSpectronaut(input_file, output_file, block_size = NA_integer_)) | ||
| expect_error(reduceBigSpectronaut(input_file, output_file, block_size = c(1L, 2L))) | ||
| expect_error(suppressWarnings( | ||
| reduceBigSpectronaut(input_file, output_file, block_size = "16MB") | ||
| )) | ||
| }) |
There was a problem hiding this comment.
Strengthen invalid block_size expectations to avoid false positives.
These assertions currently pass on any error. If reducer internals fail for another reason, this test can still pass even when block_size validation regresses. Constrain the expected error to include block_size in the message.
Suggested tightening
- expect_error(reduceBigSpectronaut(input_file, output_file, block_size = -1L))
- expect_error(reduceBigSpectronaut(input_file, output_file, block_size = 0L))
- expect_error(reduceBigSpectronaut(input_file, output_file, block_size = NA_integer_))
- expect_error(reduceBigSpectronaut(input_file, output_file, block_size = c(1L, 2L)))
+ expect_error(reduceBigSpectronaut(input_file, output_file, block_size = -1L), regexp = "block_size")
+ expect_error(reduceBigSpectronaut(input_file, output_file, block_size = 0L), regexp = "block_size")
+ expect_error(reduceBigSpectronaut(input_file, output_file, block_size = NA_integer_), regexp = "block_size")
+ expect_error(reduceBigSpectronaut(input_file, output_file, block_size = c(1L, 2L)), regexp = "block_size")
expect_error(suppressWarnings(
reduceBigSpectronaut(input_file, output_file, block_size = "16MB")
- ))
+ ), regexp = "block_size")🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In `@tests/testthat/test-converters.R` around lines 106 - 113, The test's
expect_error calls are too broad—change them to assert the error message
mentions "block_size" so they only pass when block_size validation fails; update
each expect_error(reduceBigSpectronaut(...), ...) in
tests/testthat/test-converters.R to include a second argument (string or regex)
that matches "block_size" (e.g., "block_size" or "block_size.*invalid") for the
negative values and vector cases, and similarly for the suppressed-warning call
so all invalid-block_size cases are checked by message content rather than any
error.
tonywu1999
reviewed
May 19, 2026
| anomalyModelFeatures=c()) { | ||
| calculateAnomalyScores=FALSE, | ||
| anomalyModelFeatures=c(), | ||
| block_size = 16L * 1024L * 1024L) { |
Contributor
There was a problem hiding this comment.
- For people who may want to increase their block size to increase the speed of processing, add a recommendation on how to estimate the adequate block size to maximize speed while reducing the risk of the system crashing
- Make it more clear in the MSstatsBig / MSstatsConvert documentation on which columns we actually need from users for Spectronaut
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Motivation and Context
reduceBigSpectronaut()previously streamed Spectronaut CSV exports throughreadr::read_delim_chunked, which holds a string-interning pool that grows across chunks and pushed peak memory well above one batch's working set. This branch ports the reader to Arrow (which releases per-batch state) and follows up with the three remaining items fromTODO-arrow_chunking_followups.md:block_sizewas Arrow's 256 KiB default — too small for wide Spectronaut rows, causingInvalid: straddling object straddles two block boundarieserrors and excessive parser overhead.cleanSpectronautChunk()ran every batch through ~13 sequentialdplyrverbs on adata.frame, producing repeated transient column allocations and fragmenting R's allocator. The rest of theMSstatsConvertfamily isdata.table-native.Solution: replace
readrwith Arrow'sCsvReadOptions/Scanner/ToRecordBatchReader, raise the defaultblock_sizeto 16 MiB and expose it as a user parameter, document the straddling-object workaround at the parameter, and rewritecleanSpectronautChunk()in puredata.table.Changes
readr::read_delim_chunkedinreduceBigSpectronaut(). Usesarrow::open_dataset()+Scanner+ToRecordBatchReaderto stream record batches; preserves the existing comma/tab/semicolon delimiter switch viaCsvParseOptions$delimiter. Per-batch progress logging every 1,000 batches.block_sizeparameter on bothreduceBigSpectronaut()and the user-facingbigSpectronauttoMSstatsFormat(). Default16L * 1024L * 1024L(16 MiB) replaces Arrow's 256 KiB default. Coerced to integer and validated (length 1, non-NA, positive).@param block_sizedocuments the exact error string (Invalid: straddling object straddles two block boundaries) and the recommended override (64L * 1024L * 1024L) so users hitting the straddling error on pathological rows have a self-service fix.cleanSpectronautChunk()rewritten indata.table.setDT(input)at entry; column selection viadt[, cols, with = FALSE]; two-step rename viasetnames(..., skip_absent = TRUE)matching theMSstatsConvertfamily convention; in-place column updates via:=; conditional NA assignment via mask formdt[cond, Intensity := NA_real_]. Function shrank from ~88 lines to ~64.is.na(EGQvalue) | EGQvalue >= cutoffso rows with missing q-values still getIntensity = NA, matching the previousdplyr::if_elsebehavior (a naivedata.tabletranslation would silently change this).dplyr::collect(head(dplyr::select(...)))pattern at the old lines 140/144 was a no-op residue from an earlier Arrow-Table refactor and is gone.data.tableadded toImportsinDESCRIPTIONand imported via@importFrom data.table := .SD setDT setnamesso the package iscedta()-aware. RegeneratedNAMESPACEandman/bigSpectronauttoMSstatsFormat.Rdviadevtools::document().Testing
All tests run via
devtools::test(): 51 PASS, 0 FAIL, 0 WARN, 0 SKIP.New tests added in
tests/testthat/test-converters.R:reduceBigSpectronautvalidatesblock_size: rejects negative, zero,NA, length-2 vector, and unparseable string inputs.bigSpectronauttoMSstatsFormatplumbsblock_sizethrough: spies onreduceBigSpectronautviamockery::stub, asserts the default forwards16L * 1024L * 1024Land an explicit override forwards the user's value.cleanSpectronautChunkschema smoke test: synthetic minimal Spectronaut-shaped input, asserts output column set and basic values.cleanSpectronautChunkfilter_by_excluded: rows withExcluded == "True"getIntensity = NA.cleanSpectronautChunkfilter_by_identified: rows withIdentified == "False"getIntensity = NA.cleanSpectronautChunkfilter_by_qvalue(incl. NA case): rows below cutoff are kept, above cutoff becomeNA, andNAq-values becomeNA— the explicit semantic guarantee from the rewrite.cleanSpectronautChunkdrops rows whereF.FrgLossType != "noloss".Before this branch
cleanSpectronautChunkhad no direct test coverage (existing tests stubbedreduceBigSpectronautout entirely).Checklist Before Requesting a Review
Motivation & Context
Spectronaut CSV imports previously used readr::read_delim_chunked() which accumulates string-interning state across chunks, causing unbounded memory growth on very large exports. Certain extra-wide Spectronaut exports also triggered Arrow-style “straddling object” errors when rows spanned block boundaries. Additionally, cleanSpectronautChunk() used dplyr pipelines which are less optimal for large, chunked in-place processing.
Solution: replace the readr chunked reader with an Arrow-based streaming CSV reader that processes single record-batches (bounding peak memory) and expose a tunable block_size for block-boundary issues. Rewrite cleanSpectronautChunk() in data.table for in-place, memory-efficient transformations. Add progress logging and parameter plumbing so callers can tune block_size.
Detailed changes
Package metadata and imports
R/clean_spectronaut.R
R/converters.R
man pages
Tests (tests/testthat/test-converters.R)
Unit tests added or modified
Coding guidelines / violations
No coding guideline violations identified. The changes: