generic_loader/load_folder.py

@@ -132,8 +132,13 @@ from __future__ import annotations
 
 import argparse
 import getpass
+import multiprocessing as mp
+import os
+import queue as _queue_mod
 import re
 import sys
+import threading
+from concurrent.futures import ProcessPoolExecutor, as_completed
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
@@ -155,6 +160,7 @@ from load_sas import (
     discover_partition_values_chunked,
     infer_schema,
     iter_sas_chunks,
+    read_sas_metadata,
     read_sas_preview,
     render_create_indexes,
     render_create_table,
@@ -702,7 +708,15 @@ def _discover_cluster_partitions(
     return merged
 
 
-def load_cluster(conn, cluster: ClusterSpec, schemaname: str) -> int:
+def load_cluster(
+    conn,
+    cluster: ClusterSpec,
+    schemaname: str,
+    *,
+    workers: int = 1,
+    progress_queue: Any = None,
+    db_overrides: Optional[Dict[str, Optional[str]]] = None,
+) -> int:
     """Load every file in ``cluster`` into one table. Returns total rows loaded.
 
     When ``cluster.partition_by`` is non-empty, partition values are
@@ -713,6 +727,20 @@ def load_cluster(conn, cluster: ClusterSpec, schemaname: str) -> int:
     file mid-cluster fails, earlier chunks - including chunks from earlier
     files in the cluster - stay committed; only the in-flight chunk is
     rolled back by :func:`main`.
+
+    ``workers`` controls parallelism for the *append* phase. The first file
+    always runs serially on ``conn`` (to create the table and, when
+    partitioned, pre-create partitions). When ``workers > 1`` the remaining
+    files dispatch to a ``ProcessPoolExecutor``; each worker opens its own
+    psycopg2 connection, re-infers the per-file schema, runs the same
+    :func:`load_sas.assert_schema_compatible` check the serial path uses,
+    and streams chunks via COPY. Workers report per-chunk row counts to
+    ``progress_queue`` so the caller can drive a single aggregated tqdm
+    bar regardless of how many workers are in flight.
+
+    ``db_overrides`` carries ``{"user", "password"}`` into workers when the
+    caller prompted for credentials interactively; leave ``None`` to let
+    workers read the standard libpq environment variables on their own.
     """
     if not cluster.files:
         return 0
@@ -782,21 +810,44 @@ def load_cluster(conn, cluster: ClusterSpec, schemaname: str) -> int:
         conn, schemaname, cluster.tablename, first, first_columns,
         cluster.include, cluster.exclude,
         total_rows=first_total_rows,
+        progress_queue=progress_queue,
     )
+    # Commit the first file (and the CREATE TABLE) before spawning workers
+    # so their ``assert_schema_compatible`` probes actually see the new
+    # table. Without this, worker connections started mid-transaction on
+    # the main connection would see nothing in information_schema.
+    conn.commit()
 
-    for path in rest:
-        columns, path_total_rows = _infer_cluster_schema(
-            path, cluster.include, cluster.exclude
-        )
-        # Uses the same check that if_exists=append runs. A type mismatch or
-        # missing column aborts the cluster; because chunks commit as they
-        # load, earlier chunks in the cluster remain in the table.
-        assert_schema_compatible(conn, schemaname, cluster.tablename, columns)
-        total += _stream_file(
-            conn, schemaname, cluster.tablename, path, columns,
-            cluster.include, cluster.exclude,
-            total_rows=path_total_rows,
-        )
+    if rest:
+        if workers > 1:
+            total += _load_remaining_files_parallel(
+                rest,
+                schemaname,
+                cluster.tablename,
+                cluster.include,
+                cluster.exclude,
+                workers=workers,
+                progress_queue=progress_queue,
+                db_overrides=db_overrides,
+            )
+        else:
+            for path in rest:
+                columns, path_total_rows = _infer_cluster_schema(
+                    path, cluster.include, cluster.exclude
+                )
+                # Uses the same check that if_exists=append runs. A type
+                # mismatch or missing column aborts the cluster; because
+                # chunks commit as they load, earlier chunks in the
+                # cluster remain in the table.
+                assert_schema_compatible(
+                    conn, schemaname, cluster.tablename, columns
+                )
+                total += _stream_file(
+                    conn, schemaname, cluster.tablename, path, columns,
+                    cluster.include, cluster.exclude,
+                    total_rows=path_total_rows,
+                    progress_queue=progress_queue,
+                )
 
     # -- Index support ------------------------------------------------------
     if cluster.indexes:
@@ -815,8 +866,25 @@ def _stream_file(
     exclude,
     *,
     total_rows: Optional[int] = None,
+    progress_queue: Any = None,
 ) -> int:
+    """Stream ``path`` into an existing table chunk by chunk.
+
+    When ``progress_queue`` is provided, each chunk's row count is published
+    to the queue as ``("rows", n)`` tuples instead of being rendered to a
+    per-file tqdm bar. That lets :func:`main` drive a single folder-wide
+    progress bar from a background drainer thread, which is the only way
+    to keep a coherent progress view when the folder loader is running
+    files in parallel workers.
+    """
     def _chunks():
+        if progress_queue is not None:
+            for chunk_df, _chunk_meta in iter_sas_chunks(path):
+                chunk_df = apply_column_filter(chunk_df, include, exclude)
+                progress_queue.put(("rows", len(chunk_df)))
+                yield chunk_df
+            return
+
         pbar = tqdm(
             total=total_rows,
             unit="row",
@@ -836,6 +904,134 @@ def _stream_file(
     return copy_dataframes(conn, schemaname, tablename, _chunks(), columns)
 
 
+# ---------------------------------------------------------------------------
+# Parallel append workers
+# ---------------------------------------------------------------------------
+
+
+def _worker_load_append_file(
+    path_str: str,
+    schemaname: str,
+    tablename: str,
+    include: Optional[List[str]],
+    exclude: Optional[List[str]],
+    progress_queue: Any,
+    db_overrides: Optional[Dict[str, Optional[str]]],
+) -> Tuple[str, int, Optional[str]]:
+    """Worker process: load one SAS file in append mode.
+
+    Runs in a subprocess spawned by :func:`_load_remaining_files_parallel`.
+    Opens its own psycopg2 connection, re-infers the per-file schema (so
+    per-file ``INTEGER`` vs ``BIGINT`` drift is caught by the existing
+    schema-compat check just like in the serial path), and streams chunks
+    via ``COPY``. Row counts are published to the shared queue for the
+    main process's global tqdm bar.
+
+    Returns ``(path_str, rows_loaded, error_or_None)`` - failures are
+    returned rather than raised so the parent can aggregate results
+    across workers without losing partial progress.
+    """
+    from pathlib import Path as _Path
+
+    from dotenv import load_dotenv as _load_dotenv
+
+    from load_sas import (
+        apply_column_filter as _apply_column_filter,
+        assert_schema_compatible as _assert_schema_compatible,
+        connect as _connect,
+        copy_dataframes as _copy_dataframes,
+        infer_schema as _infer_schema,
+        iter_sas_chunks as _iter_sas_chunks,
+        read_sas_preview as _read_sas_preview,
+    )
+
+    _load_dotenv()
+
+    path = _Path(path_str)
+    try:
+        preview_df, meta = _read_sas_preview(path)
+        preview_df = _apply_column_filter(preview_df, include, exclude)
+        total_rows = getattr(meta, "number_rows", None)
+        columns = _infer_schema(preview_df, meta, total_rows=total_rows)
+
+        user = db_overrides.get("user") if db_overrides else None
+        password = db_overrides.get("password") if db_overrides else None
+        conn = _connect(user=user, password=password)
+        conn.autocommit = False
+        try:
+            _assert_schema_compatible(conn, schemaname, tablename, columns)
+
+            def _chunks():
+                for chunk_df, _chunk_meta in _iter_sas_chunks(path):
+                    chunk_df = _apply_column_filter(chunk_df, include, exclude)
+                    if progress_queue is not None:
+                        progress_queue.put(("rows", len(chunk_df)))
+                    yield chunk_df
+
+            rows = _copy_dataframes(
+                conn, schemaname, tablename, _chunks(), columns
+            )
+            conn.commit()
+            return (path_str, rows, None)
+        finally:
+            conn.close()
+    except Exception as e:
+        return (path_str, 0, f"{type(e).__name__}: {e}")
+
+
+def _load_remaining_files_parallel(
+    files: List[Path],
+    schemaname: str,
+    tablename: str,
+    include: Optional[List[str]],
+    exclude: Optional[List[str]],
+    *,
+    workers: int,
+    progress_queue: Any,
+    db_overrides: Optional[Dict[str, Optional[str]]],
+) -> int:
+    """Run append-mode loads for ``files`` across a process pool.
+
+    Each file is an independent unit of work submitted to
+    ``ProcessPoolExecutor``. Workers infer schema, validate compatibility,
+    and stream via COPY just like the serial path. Failures are collected
+    and re-raised as a single ``RuntimeError`` at the end so that all
+    other workers' rows still count toward the committed total.
+    """
+    total = 0
+    errors: List[Tuple[str, str]] = []
+
+    with ProcessPoolExecutor(max_workers=workers) as pool:
+        futures = [
+            pool.submit(
+                _worker_load_append_file,
+                str(p),
+                schemaname,
+                tablename,
+                include,
+                exclude,
+                progress_queue,
+                db_overrides,
+            )
+            for p in files
+        ]
+        for fut in as_completed(futures):
+            path_str, rows, err = fut.result()
+            if err is not None:
+                errors.append((path_str, err))
+            else:
+                total += rows
+
+    if errors:
+        joined = "\n".join(f"    {p}: {e}" for p, e in errors)
+        raise RuntimeError(
+            f"{len(errors)} worker(s) failed while appending to "
+            f"{schemaname}.{tablename}:\n{joined}"
+        )
+
+    return total
+
+
 # ---------------------------------------------------------------------------
 # CLI
 # ---------------------------------------------------------------------------
@@ -875,6 +1071,22 @@ def _build_argparser() -> argparse.ArgumentParser:
             "PGUSER / PGPASSWORD from the environment or .env file."
         ),
     )
+    p.add_argument(
+        "--workers",
+        type=int,
+        default=1,
+        metavar="N",
+        help=(
+            "Number of worker processes for the append phase. With N=1 "
+            "(default) files load serially on the main connection. With "
+            "N>1 the first file of each cluster still runs serially (to "
+            "create the table), then the remaining files load in parallel "
+            "across N processes, each with its own psycopg2 connection. "
+            "On a big box try N close to your core count. When N>1 the "
+            "per-chunk row target drops to 500,000 unless you've pinned "
+            "GENERIC_LOADER_CHUNK_ROWS, so peak memory stays bounded."
+        ),
+    )
     return p
 
 
@@ -993,6 +1205,101 @@ def main(argv: Optional[List[str]] = None) -> int:
     if args.dbcreds:
         db_user = input("Database username: ")
         db_password = getpass.getpass("Database password: ")
+    db_overrides: Optional[Dict[str, Optional[str]]] = (
+        {"user": db_user, "password": db_password} if args.dbcreds else None
+    )
+
+    workers = max(1, int(args.workers))
+
+    # When running parallel workers, bound peak memory: each worker buffers a
+    # chunk (read + prepared + serialized) so total memory scales with
+    # workers × chunk_rows × avg_row_bytes. Drop the default chunk target to
+    # 500k unless the operator has explicitly pinned it. Setting the env var
+    # before workers spawn means they inherit it through forkserver / spawn.
+    if (
+        workers > 1
+        and "GENERIC_LOADER_CHUNK_ROWS" not in os.environ
+    ):
+        os.environ["GENERIC_LOADER_CHUNK_ROWS"] = "500000"
+        print(
+            "[info] parallel mode: bounding per-chunk rows to 500,000. "
+            "Pin GENERIC_LOADER_CHUNK_ROWS to override.",
+            file=sys.stderr,
+        )
+
+    # -- Metadata pre-scan -----------------------------------------------------
+    # Sum ``number_rows`` across every file so the tqdm bar has a real
+    # denominator. ``read_sas_metadata`` uses pyreadstat's ``metadataonly=True``
+    # fast path; a few ms per sas7bdat even on large files.
+    print(
+        f"pre-scanning row counts for {sum(len(c.files) for c in loadable)} "
+        f"file(s)...",
+        file=sys.stderr,
+    )
+    grand_total = 0
+    unknown_total_files: List[str] = []
+    for c in loadable:
+        for p in c.files:
+            try:
+                meta = read_sas_metadata(p)
+                n = getattr(meta, "number_rows", None)
+                if n is None:
+                    unknown_total_files.append(p.name)
+                else:
+                    grand_total += int(n)
+            except Exception as e:
+                unknown_total_files.append(f"{p.name} ({e})")
+    if unknown_total_files:
+        print(
+            f"[warn] could not read row count from "
+            f"{len(unknown_total_files)} file(s); progress bar ETA will "
+            f"be approximate.",
+            file=sys.stderr,
+        )
+    print(
+        f"  total rows across folder: {grand_total:,}",
+        file=sys.stderr,
+    )
+
+    # -- Shared progress plumbing ---------------------------------------------
+    # The queue crosses process boundaries when workers > 1 (managed proxy)
+    # and is a plain in-process queue otherwise; the put/get contract is
+    # identical either way. A daemon thread drains it and advances the one
+    # tqdm bar that spans the whole folder load.
+    manager: Optional[Any] = None
+    progress_queue: Any
+    if workers > 1:
+        manager = mp.Manager()
+        progress_queue = manager.Queue()
+    else:
+        progress_queue = _queue_mod.Queue()
+
+    pbar = tqdm(
+        total=grand_total or None,
+        unit="row",
+        unit_scale=True,
+        desc=f"{cfg.folder.name}",
+        file=sys.stderr,
+        dynamic_ncols=True,
+    )
+    stop_drainer = threading.Event()
+
+    def _drainer() -> None:
+        while not stop_drainer.is_set():
+            try:
+                event = progress_queue.get(timeout=0.1)
+            except _queue_mod.Empty:
+                continue
+            except (EOFError, OSError):
+                return
+            if not event:
+                continue
+            kind = event[0]
+            if kind == "rows":
+                pbar.update(event[1])
+
+    drainer_thread = threading.Thread(target=_drainer, daemon=True)
+    drainer_thread.start()
 
     conn = connect(user=db_user, password=db_password)
     conn.autocommit = False
@@ -1002,10 +1309,18 @@ def main(argv: Optional[List[str]] = None) -> int:
         for cluster in loadable:
             print(
                 f"\n>>> loading cluster {cluster.tablename!r} "
-                f"({len(cluster.files)} file(s))"
+                f"({len(cluster.files)} file(s)) "
+                f"[workers={workers}]"
             )
             try:
-                rows = load_cluster(conn, cluster, cfg.schemaname)
+                rows = load_cluster(
+                    conn,
+                    cluster,
+                    cfg.schemaname,
+                    workers=workers,
+                    progress_queue=progress_queue,
+                    db_overrides=db_overrides,
+                )
                 conn.commit()
                 totals.append((cluster.tablename, len(cluster.files), rows))
                 print(
@@ -1022,7 +1337,14 @@ def main(argv: Optional[List[str]] = None) -> int:
                 if args.fail_fast:
                     break
     finally:
+        # Drain any pending progress events before shutting the bar down so
+        # the final rendered total matches what actually landed.
+        stop_drainer.set()
+        drainer_thread.join(timeout=2.0)
+        pbar.close()
         conn.close()
+        if manager is not None:
+            manager.shutdown()
 
     print("\n=== summary ===")
     for name, fcount, rows in totals:

generic_loader/load_sas.py

@@ -580,6 +580,22 @@ def read_sas_preview(
     return reader(str(Path(path)), row_limit=row_limit, **kwargs)
 
 
+def read_sas_metadata(path: Path) -> Any:
+    """Read only the metadata (no rows) from a SAS file.
+
+    Uses pyreadstat's ``metadataonly=True`` fast path: the reader decodes
+    the file header (column names, formats, total row count, etc.) and
+    returns without touching the data pages. Orders of magnitude faster
+    than :func:`read_sas_preview` when all you need is
+    ``meta.number_rows`` - typically a few ms per sas7bdat file, which
+    makes it cheap to pre-scan a whole folder to populate a global
+    progress bar.
+    """
+    reader, kwargs = _sas_reader(path)
+    _, meta = reader(str(Path(path)), metadataonly=True, **kwargs)
+    return meta
+
+
 def iter_sas_chunks(
     path: Path,
     *,