| """Write the star schema as Parquet (typed as the ClickHouse DDL declares it) and as CSVWithNames with \\N for NULL.""" |
| from __future__ import annotations |
|
|
| import csv |
| import hashlib |
| from pathlib import Path |
|
|
| import numpy as np |
| import pandas as pd |
| import pyarrow as pa |
| import pyarrow.parquet as pq |
|
|
| MONEY = pa.decimal128(18, 2) |
| RATE = pa.decimal128(5, 4) |
|
|
| |
| SCHEMA: dict[str, list[tuple[str, pa.DataType, int | None]]] = { |
| "dim_date": [("date_key", pa.uint32(), None), ("date", pa.date32(), None), ("year", pa.uint16(), None), ("quarter", pa.uint8(), None), |
| ("quarter_name", pa.string(), None), ("month", pa.uint8(), None), ("month_name", pa.string(), None), |
| ("year_month", pa.uint32(), None), ("day_of_week", pa.uint8(), None), ("day_name", pa.string(), None), |
| ("is_weekend", pa.uint8(), None)], |
| "dim_product": [("product_id", pa.uint32(), None), ("product_name", pa.string(), None), ("subcategory", pa.string(), None), |
| ("category", pa.string(), None), ("brand", pa.string(), None), ("list_price", MONEY, 2), ("unit_cost", MONEY, 2)], |
| "dim_customer": [("customer_id", pa.uint32(), None), ("customer_name", pa.string(), None), ("segment", pa.string(), None), |
| ("country", pa.string(), None), ("region", pa.string(), None), ("city", pa.string(), None), ("signup_date", pa.date32(), None)], |
| "dim_store": [("store_id", pa.uint16(), None), ("store_name", pa.string(), None), ("channel", pa.string(), None), |
| ("country", pa.string(), None), ("region", pa.string(), None), ("city", pa.string(), None)], |
| "orders": [("order_id", pa.uint32(), None), ("order_date", pa.date32(), None), ("customer_id", pa.uint32(), None), ("store_id", pa.uint16(), None)], |
| "order_lines": [("order_line_id", pa.uint32(), None), ("order_id", pa.uint32(), None), ("date_key", pa.uint32(), None), |
| ("order_date", pa.date32(), None), ("customer_id", pa.uint32(), None), ("store_id", pa.uint16(), None), |
| ("product_id", pa.uint32(), None), ("quantity", pa.uint16(), None), ("unit_price", MONEY, 2), ("discount_pct", RATE, 4), |
| ("net_amount", MONEY, 2), ("unit_cost", MONEY, 2), ("cost_amount", MONEY, 2)], |
| "returns": [("return_id", pa.uint32(), None), ("order_line_id", pa.uint32(), None), ("order_id", pa.uint32(), None), |
| ("product_id", pa.uint32(), None), ("customer_id", pa.uint32(), None), ("store_id", pa.uint16(), None), |
| ("sale_date", pa.date32(), None), ("sale_date_key", pa.uint32(), None), ("return_date", pa.date32(), None), |
| ("return_date_key", pa.uint32(), None), ("returned_qty", pa.uint16(), None), ("refund_amount", MONEY, 2), ("reason", pa.string(), None)], |
| } |
|
|
| NULLABLE = {("dim_customer", "segment"), ("orders", "customer_id"), ("order_lines", "customer_id"), ("returns", "customer_id")} |
|
|
|
|
| def _decimal_array(ints: np.ndarray, typ: pa.DataType) -> pa.Array: |
| """Exact decimals from whole numbers of the smallest unit (cents, basis points): a decimal128 is that integer, stored |
| as 16 little-endian bytes, read at the type's scale.""" |
| v = np.ascontiguousarray(ints, dtype=np.int64) |
| wide = np.empty((len(v), 2), dtype="<i8") |
| wide[:, 0], wide[:, 1] = v, v >> 63 |
| return pa.Array.from_buffers(typ, len(v), [None, pa.py_buffer(wide.tobytes())]) |
|
|
|
|
| def _decimal_text(ints: np.ndarray, scale: int) -> np.ndarray: |
| v = np.asarray(ints, dtype=np.int64) |
| unit = 10 ** scale |
| whole, frac = np.abs(v) // unit, np.abs(v) % unit |
| sign = np.where(v < 0, "-", "") |
| return np.char.add(np.char.add(np.char.add(sign, whole.astype(str)), "."), np.char.zfill(frac.astype(str), scale)) |
|
|
|
|
| def to_arrow(name: str, df: pd.DataFrame) -> pa.Table: |
| cols, fields = [], [] |
| for col, typ, scale in SCHEMA[name]: |
| s = df[col] |
| nullable = (name, col) in NULLABLE |
| if not nullable and s.isna().any(): |
| raise ValueError(f"{name}.{col} has NULLs but is declared NOT NULL") |
| if scale is not None: |
| arr = _decimal_array(s.to_numpy(dtype=np.int64), typ) |
| elif pa.types.is_date32(typ): |
| arr = pa.array(pd.to_datetime(s).dt.date, type=typ) |
| elif pa.types.is_string(typ): |
| arr = pa.array(s.astype(object).where(s.notna(), None).tolist(), type=typ) |
| else: |
| arr = pa.array(s.astype("Int64").to_numpy(dtype=object, na_value=None) if nullable else s.to_numpy(dtype=np.int64), type=typ) |
| cols.append(arr) |
| fields.append(pa.field(col, typ, nullable=nullable)) |
| return pa.Table.from_arrays(cols, schema=pa.schema(fields)) |
|
|
|
|
| def to_csv_frame(name: str, df: pd.DataFrame) -> pd.DataFrame: |
| """The table as the text CSVWithNames carries: decimals at their scale, ISO dates, whole numbers without a point.""" |
| out = {} |
| for col, typ, scale in SCHEMA[name]: |
| s = df[col] |
| if scale is not None: |
| out[col] = _decimal_text(s.to_numpy(dtype=np.int64), scale) |
| elif pa.types.is_date32(typ): |
| out[col] = pd.to_datetime(s).dt.strftime("%Y-%m-%d") |
| elif pa.types.is_string(typ): |
| out[col] = s |
| else: |
| out[col] = s.astype("Int64") |
| return pd.DataFrame(out) |
|
|
|
|
| def write(tables: dict[str, pd.DataFrame], parquet_dir: Path, csv_dir: Path) -> dict[str, dict]: |
| """Writes both formats and returns, per file, its rows, bytes and SHA-256.""" |
| parquet_dir.mkdir(parents=True, exist_ok=True) |
| csv_dir.mkdir(parents=True, exist_ok=True) |
| manifest: dict[str, dict] = {} |
| for name in SCHEMA: |
| t = to_arrow(name, tables[name]) |
| pq.write_table(t, parquet_dir / f"{name}.parquet", compression="zstd", row_group_size=1_000_000, |
| write_statistics=True, use_dictionary=True) |
| path = csv_dir / f"{name}.csv" |
| to_csv_frame(name, tables[name]).to_csv(path, index=False, na_rep="\\N", quoting=csv.QUOTE_MINIMAL, lineterminator="\n", encoding="utf-8") |
| for p in (parquet_dir / f"{name}.parquet", path): |
| manifest[f"{p.parent.name}/{p.name}"] = {"rows": t.num_rows, "bytes": p.stat().st_size, "sha256": sha256(p)} |
| return manifest |
|
|
|
|
| def sha256(path: Path) -> str: |
| h = hashlib.sha256() |
| with path.open("rb") as fh: |
| for chunk in iter(lambda: fh.read(1 << 20), b""): |
| h.update(chunk) |
| return h.hexdigest() |
|
|
