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Implement parallel processing for partition discovery in load_folder.py and enhance column filtering in load_sas.py

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Added support for parallel processing using ProcessPoolExecutor in the _discover_cluster_partitions function, allowing for efficient partition value discovery across multiple files. This change significantly reduces I/O overhead by reading only necessary columns during scans. Additionally, updated iter_sas_chunks and iter_text_chunks functions to accept a usecols parameter, enabling selective column parsing for improved performance during data loading. These enhancements aim to optimize resource usage and speed up the data processing pipeline.
This commit is contained in:
David Peterson 2026-04-21 21:43:42 -05:00
parent 1197846d10
commit dd83f58412
2 changed files with 209 additions and 49 deletions
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234
generic_loader/load_folder.py
View File

@ -157,7 +157,6 @@ import threading
from concurrent.futures import (
CancelledError,
ProcessPoolExecutor,
ThreadPoolExecutor,
as_completed,
)
from dataclasses import dataclass, field
@ -885,6 +884,94 @@ def discover_clusters(cfg: FolderConfig) -> List[ClusterSpec]:
return clusters
# ---------------------------------------------------------------------------
# Top-level workers (must be importable for ProcessPoolExecutor pickling)
# ---------------------------------------------------------------------------
def _prescan_worker(
path_str: str,
delimiter: str,
text_encoding: str,
quotechar: str,
) -> Tuple[
str,
Optional[int],
Optional[Dict[str, Tuple[str, Optional[str]]]],
Optional[str],
]:
"""Top-level prescan worker for ProcessPoolExecutor.
Reads only metadata (pyreadstat ``metadataonly=True``) for one file and
returns ``(path_str, number_rows, per_column_meta, error_message)``.
Kept at module level so ``ProcessPoolExecutor`` can pickle it; the
closure version we used to call from a ``ThreadPoolExecutor`` shared
the parent's GIL and serialised the per-file Python work, which is
why pre-scan felt slow even though the actual disk reads were fast.
"""
try:
meta = read_sas_metadata(
Path(path_str),
delimiter=delimiter,
text_encoding=text_encoding,
quotechar=quotechar,
)
n = getattr(meta, "number_rows", None)
col_meta = extract_union_metadata(meta)
return (
path_str,
int(n) if n is not None else None,
col_meta,
None,
)
except Exception as e:
return (path_str, None, None, f"{type(e).__name__}: {e}")
def _partition_scan_worker(
path_str: str,
partition_by: List[str],
delimiter: str,
text_encoding: str,
quotechar: str,
) -> Tuple[str, Optional[dict], Optional[str]]:
"""Top-level partition-discovery worker for ProcessPoolExecutor.
Streams ``path_str`` with ``usecols=partition_by`` so pyreadstat only
decodes the partition columns themselves - on a wide sas7bdat that's
typically a 10x+ I/O reduction over reading every column. Returns
``(path_str, partial_partition_tree, error_message)``.
``columns`` is intentionally not passed: partition-value normalisation
needs the cluster-wide schema, which is out of process. The merger in
:func:`_discover_cluster_partitions` re-applies normalisation when it
folds the per-file trees together.
"""
try:
def _chunks() -> Any:
for chunk_df, _meta in iter_sas_chunks(
Path(path_str),
delimiter=delimiter,
text_encoding=text_encoding,
quotechar=quotechar,
usecols=list(partition_by),
):
yield chunk_df
tree = discover_partition_values_chunked(
_chunks(), list(partition_by),
)
return (path_str, tree, None)
except Exception as e:
import traceback as _traceback
return (
path_str,
None,
f"{type(e).__name__}: {e}\n{_traceback.format_exc()}",
)
# ---------------------------------------------------------------------------
# Per-cluster load
# ---------------------------------------------------------------------------
@ -929,27 +1016,83 @@ def _infer_cluster_schema(
def _discover_cluster_partitions(
cluster: ClusterSpec,
columns: Dict,
*,
workers: int = 1,
) -> dict:
"""Scan ALL files in ``cluster`` to discover partition values.
Returns a nested partition-value tree suitable for passing to
:func:`load_sas.render_partition_ddl` and :func:`load_sas.create_table`.
Each file is scanned chunk-by-chunk so the full dataset is never
materialized in memory.
Each file is read with ``usecols=cluster.partition_by`` so pyreadstat
only decodes the partition columns - on a wide sas7bdat that drops
the bytes-touched-per-file by an order of magnitude vs the old
full-row scan. With ``workers > 1`` the per-file scans run in a
``ProcessPoolExecutor`` and the partial trees are merged as they
complete (true parallelism, visible in ``htop`` as N python procs).
``include`` / ``exclude`` are intentionally not honoured here:
partition columns are validated against the inferred schema before
we ever get called, which already enforces that they survived any
explicit include/exclude filter. The old serial path applied the
filter for symmetry, but it was a no-op once usecols pinned us to
the partition set anyway.
"""
tkw = _build_text_kw(cluster)
merged: dict = {}
for path in cluster.files:
def _filtered_chunks(p=path):
for chunk_df, _chunk_meta in iter_sas_chunks(p, **tkw):
yield apply_column_filter(
chunk_df, cluster.include, cluster.exclude
)
file_tree = discover_partition_values_chunked(
_filtered_chunks(), cluster.partition_by, columns,
if not cluster.files:
return merged
n_workers = max(1, min(int(workers), len(cluster.files)))
if n_workers <= 1 or len(cluster.files) == 1:
for path in cluster.files:
def _filtered_chunks(p=path):
for chunk_df, _meta in iter_sas_chunks(
p, usecols=list(cluster.partition_by), **tkw,
):
yield chunk_df
file_tree = discover_partition_values_chunked(
_filtered_chunks(), cluster.partition_by, columns,
)
_merge_partition_trees(merged, file_tree)
return merged
with ProcessPoolExecutor(max_workers=n_workers) as ppool:
futures = {
ppool.submit(
_partition_scan_worker,
str(path),
list(cluster.partition_by),
tkw["delimiter"],
tkw["text_encoding"],
tkw["quotechar"],
): path
for path in cluster.files
}
bar = tqdm(
total=len(futures),
unit="file",
desc=" discovering partitions",
file=sys.stderr,
dynamic_ncols=True,
)
_merge_partition_trees(merged, file_tree)
try:
for fut in as_completed(futures):
path = futures[fut]
_path_str, file_tree, err = fut.result()
bar.update(1)
if err is not None:
raise RuntimeError(
f"partition discovery failed for {path}: {err}"
)
if file_tree:
_merge_partition_trees(merged, file_tree)
finally:
bar.close()
return merged
@ -1041,7 +1184,7 @@ def load_cluster(
file=sys.stderr,
)
partition_values = _discover_cluster_partitions(
cluster, first_columns,
cluster, first_columns, workers=workers,
)
total_parts = _count_partitions(partition_values)
print(
@ -1676,7 +1819,7 @@ def main(argv: Optional[List[str]] = None) -> int:
file=sys.stderr,
)
partition_values = _discover_cluster_partitions(
c, columns,
c, columns, workers=max(1, int(args.workers)),
)
total_parts = _count_partitions(partition_values)
print(
@ -1787,42 +1930,35 @@ def main(argv: Optional[List[str]] = None) -> int:
file=sys.stderr,
)
else:
prescan_workers = min(16, max(1, len(all_files)))
# Cap at min(workers, file_count, 32). Pyreadstat metadata reads are
# mostly C-side I/O + struct decoding, but the per-file Python work
# (extract_union_metadata, dict construction) was being serialised
# by the GIL in the old ThreadPool path - which is why the bar
# crawled even though disk was idle. ProcessPool gives us actual
# parallelism; you'll now see N python procs in htop.
prescan_workers = min(
32, max(1, max(workers, 16)), len(all_files),
)
print(
f"pre-scanning row counts + per-column metadata for "
f"{len(all_files)} file(s) across {prescan_workers} thread(s)...",
f"{len(all_files)} file(s) across {prescan_workers} "
f"process(es)...",
file=sys.stderr,
)
def _scan_one(
p: Path,
) -> Tuple[
Path,
Optional[int],
Optional[Dict[str, Tuple[str, Optional[str]]]],
Optional[str],
]:
try:
_prescan_tkw = dict(
delimiter=cfg.delimiter,
text_encoding=cfg.text_encoding,
quotechar=cfg.quotechar,
)
meta = read_sas_metadata(p, **_prescan_tkw)
n = getattr(meta, "number_rows", None)
col_meta = extract_union_metadata(meta)
return (
p,
int(n) if n is not None else None,
col_meta,
None,
)
except Exception as e:
return (p, None, None, str(e))
unknown_total_files: List[str] = []
running_total = 0
with ThreadPoolExecutor(max_workers=prescan_workers) as tpool:
with ProcessPoolExecutor(max_workers=prescan_workers) as ppool:
futures = {
ppool.submit(
_prescan_worker,
str(p),
cfg.delimiter,
cfg.text_encoding,
cfg.quotechar,
): p
for p in all_files
}
prescan_bar = tqdm(
total=len(all_files),
unit="file",
@ -1831,16 +1967,20 @@ def main(argv: Optional[List[str]] = None) -> int:
dynamic_ncols=True,
)
try:
for p, n, col_meta, err in tpool.map(_scan_one, all_files):
for fut in as_completed(futures):
path_obj = futures[fut]
path_str, n, col_meta, err = fut.result()
prescan_bar.update(1)
if err is not None:
unknown_total_files.append(f"{p.name} ({err})")
unknown_total_files.append(
f"{path_obj.name} ({err})"
)
elif n is None:
unknown_total_files.append(p.name)
unknown_total_files.append(path_obj.name)
else:
running_total += n
if col_meta is not None:
file_meta_by_path[str(p)] = col_meta
file_meta_by_path[path_str] = col_meta
finally:
prescan_bar.close()

24
generic_loader/load_sas.py
View File

@ -811,6 +811,7 @@ def iter_text_chunks(
encoding: str = "utf-8",
quotechar: str = '"',
chunksize: Optional[int] = None,
usecols: Optional[List[str]] = None,
):
"""Yield ``(df_chunk, meta)`` tuples for streaming text file loads.
@ -818,6 +819,10 @@ def iter_text_chunks(
iteration. The metadata object is rebuilt for each chunk with the
chunk's column names and ``number_rows`` set to the total file rows
(computed once up front).
When ``usecols`` is provided, only those columns are parsed - useful
for cheap partition-value discovery scans where the rest of the row
would be wasted I/O.
"""
path = Path(path)
if chunksize is None:
@ -832,8 +837,7 @@ def iter_text_chunks(
total = _count_text_lines(path, encoding)
reader = pd.read_csv(
path,
read_csv_kwargs: Dict[str, Any] = dict(
delimiter=delimiter,
encoding=encoding,
quotechar=quotechar,
@ -842,6 +846,10 @@ def iter_text_chunks(
keep_default_na=True,
na_values=[""],
)
if usecols is not None:
read_csv_kwargs["usecols"] = list(usecols)
reader = pd.read_csv(path, **read_csv_kwargs)
for chunk_df in reader:
meta = _build_text_metadata(list(chunk_df.columns), number_rows=total)
yield chunk_df, meta
@ -941,6 +949,7 @@ def iter_sas_chunks(
delimiter: str = ",",
text_encoding: str = "utf-8",
quotechar: str = '"',
usecols: Optional[List[str]] = None,
):
"""Yield ``(df_chunk, meta)`` tuples for streaming loads.
@ -952,6 +961,13 @@ def iter_sas_chunks(
parseable, otherwise from :data:`DEFAULT_CHUNK_ROWS`. An explicit int
always wins.
When ``usecols`` is provided, pyreadstat only decodes the listed
columns. For wide sas7bdat files this is dramatically cheaper than
a full read - the C decoder skips unwanted columns instead of
materializing them. Used by partition-value discovery to avoid
re-reading every byte of every file just to extract a couple of
partition keys.
For text files, delegates to :func:`iter_text_chunks`.
"""
if _is_text_file(path):
@ -961,6 +977,7 @@ def iter_sas_chunks(
encoding=text_encoding,
quotechar=quotechar,
chunksize=chunksize,
usecols=usecols,
)
return
if chunksize is None:
@ -973,6 +990,9 @@ def iter_sas_chunks(
else:
chunksize = DEFAULT_CHUNK_ROWS
reader, kwargs = _sas_reader(path)
if usecols is not None:
kwargs = dict(kwargs)
kwargs["usecols"] = list(usecols)
yield from pyreadstat.read_file_in_chunks(
reader, str(Path(path)), chunksize=chunksize, **kwargs
)