Rspack Sftrace

3) Optional: Generate a filter file from symbols

sftrace filter works on function symbols from an object file (for rspack, the binding .node file).

# Enter the target example directory first
cd examples/react

# Prefer the locally built profiling binding from the monorepo
BINDING_NODE="$(realpath ../../crates/node_binding/rspack.linux-x64-gnu.node)"

# Regex mode
sftrace filter -p "$BINDING_NODE" -r 'finish_modules|FlagDependencyExportsPlugin' -o sftrace.filter

# List mode (one regex per line)
# sftrace filter -p "$BINDING_NODE" --list symbols.list -o sftrace.filter

If your binding file name differs by platform, replace the .node path accordingly.

4) Record sftrace (example: build in examples/react)

Run from the target example directory and keep outputs local to that example.

cd examples/react

TRACE_DIR="sftrace-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$TRACE_DIR"

# Full trace
sftrace record -o "$TRACE_DIR/sf.log" -- pnpm build

# Filtered trace (requires sftrace.filter from step 3)
sftrace record -f sftrace.filter -o "$TRACE_DIR/sf.filtered.log" -- pnpm build

5) Optional: Analyze sf.log by polars

Convert sftrace log to pola dataframe.

cd examples/react
TRACE_DIR="sftrace-YYYYMMDD-HHMMSS" # replace with your run directory
sftrace convert --type pola "$TRACE_DIR/sf.log" -o "$TRACE_DIR/sf.pola"

This will generate two files, whose schema format is as follows:

1. sf.pola

This records all events from sftrace log.

2. sf.pola.symtab

This records the function symbol name and file path of func_id.

You can use python-polars to perform data analysis on sf.pola.

import polars as pl

sf = pl.scan_parquet("./sf.pola")
symtab = pl.scan_parquet("./sf.pola.symtab")

# Query the functions that appear most frequently
(
  sf
    .filter(pl.col("kind").eq(1))
    .group_by("func_id")
    .agg(pl.len().alias("func_count"))
    .top_k(10, by="func_count")
    .join(symtab, on="func_id")
    .collect()
)

# Query the leaf frame of longest execution time
(
  sf
    .filter(~pl.col("frame_id").is_in(pl.col("parent").implode()))
    .group_by("frame_id")
    .agg([
      pl.col("func_id").first(),
      pl.col("time").filter(pl.col("kind").eq(1)).first().alias("entry_time"),
      pl.col("time").filter(pl.col("kind").is_in([2, 3])).last().alias("exit_time"),
    ])
    .filter(pl.col("exit_time").is_not_null())
    .with_columns(pl.col("exit_time").sub("entry_time").alias("duration"))
    .top_k(10, by="duration")
    .join(symtab, on="func_id")
    .collect()
)

6) Optional: Visualization sf.log

Convert sftrace log to perfetto protobuf format.

cd examples/react
TRACE_DIR="sftrace-YYYYMMDD-HHMMSS" # replace with your run directory
sftrace convert "$TRACE_DIR/sf.log" -o "$TRACE_DIR/sf.pb.gz"

Visualization using viztracer

vizviewer --use_external_processor "$TRACE_DIR/sf.pb.gz"

Use this only for visualization.

Filtering Notes

• sftrace filter matches function symbols by regex/list. It is not a first-class crate-path/module-path filter.
• Filtering does not automatically keep all descendants. If a child function symbol does not match your filter, it may disappear from the trace.
• Cross-thread relationships (for example via rayon) are not reconstructed as a single uninterrupted call chain.
• For complete call stacks, record without filter (or with a broad filter) and narrow down during analysis.