add schema dump: parquet footer reader generating schemas.json and file_tree.md
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268
gera_schemas.py
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268
gera_schemas.py
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import os
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import json
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import sys
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import pyarrow.parquet as pq
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import s3fs
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import boto3
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import duckdb
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from dotenv import load_dotenv
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load_dotenv()
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S3_ENDPOINT = os.environ["HETZNER_S3_ENDPOINT"]
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S3_BUCKET = os.environ["HETZNER_S3_BUCKET"]
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ACCESS_KEY = os.environ["AWS_ACCESS_KEY_ID"]
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SECRET_KEY = os.environ["AWS_SECRET_ACCESS_KEY"]
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s3_host = S3_ENDPOINT.removeprefix("https://").removeprefix("http://")
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# --- boto3 client (listing only, zero egress) ---
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boto = boto3.client(
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"s3",
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endpoint_url=S3_ENDPOINT,
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aws_access_key_id=ACCESS_KEY,
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aws_secret_access_key=SECRET_KEY,
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)
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# --- s3fs filesystem (footer-only reads via pyarrow) ---
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fs = s3fs.S3FileSystem(
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client_kwargs={"endpoint_url": S3_ENDPOINT},
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key=ACCESS_KEY,
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secret=SECRET_KEY,
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)
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# ------------------------------------------------------------------ #
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# Phase 1: File inventory via S3 List API (zero data egress)
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# ------------------------------------------------------------------ #
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print("Phase 1: listing S3 objects...")
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paginator = boto.get_paginator("list_objects_v2")
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inventory = {} # "dataset/table" -> {files: [...], total_size: int}
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for page in paginator.paginate(Bucket=S3_BUCKET):
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for obj in page.get("Contents", []):
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key = obj["Key"]
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if not key.endswith(".parquet"):
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continue
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parts = key.split("/")
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if len(parts) < 3:
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continue
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dataset, table = parts[0], parts[1]
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dt = f"{dataset}/{table}"
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if dt not in inventory:
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inventory[dt] = {"files": [], "total_size_bytes": 0}
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inventory[dt]["files"].append(key)
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inventory[dt]["total_size_bytes"] += obj["Size"]
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print(f" Found {len(inventory)} tables across {S3_BUCKET}")
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# ------------------------------------------------------------------ #
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# Phase 2: Schema reads — footer only (~30 KB per table)
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# ------------------------------------------------------------------ #
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print("Phase 2: reading parquet footers...")
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def fmt_size(b):
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for unit in ("B", "KB", "MB", "GB", "TB"):
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if b < 1024 or unit == "TB":
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return f"{b:.1f} {unit}"
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b /= 1024
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def extract_col_descriptions(schema):
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"""Try to pull per-column descriptions from Arrow metadata."""
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descriptions = {}
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meta = schema.metadata or {}
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# BigQuery exports embed a JSON blob under b'pandas' with column_info
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pandas_meta_raw = meta.get(b"pandas") or meta.get(b"pandas_metadata")
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if pandas_meta_raw:
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try:
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pm = json.loads(pandas_meta_raw)
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for col in pm.get("columns", []):
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name = col.get("name")
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desc = col.get("metadata", {}) or {}
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if isinstance(desc, dict) and "description" in desc:
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descriptions[name] = desc["description"]
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except Exception:
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pass
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# Also try top-level b'description' or b'schema'
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for key in (b"description", b"schema", b"BigQuery:description"):
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val = meta.get(key)
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if val:
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try:
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descriptions["__table__"] = val.decode("utf-8", errors="replace")
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except Exception:
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pass
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return descriptions
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schemas = {}
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errors = []
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for i, (dt, info) in enumerate(sorted(inventory.items())):
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dataset, table = dt.split("/", 1)
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first_file = info["files"][0]
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s3_path = f"{S3_BUCKET}/{first_file}"
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try:
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schema = pq.read_schema(fs.open(s3_path))
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col_descs = extract_col_descriptions(schema)
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# Build raw metadata dict (decode bytes keys/values)
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raw_meta = {}
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if schema.metadata:
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for k, v in schema.metadata.items():
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try:
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dk = k.decode("utf-8", errors="replace")
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dv = v.decode("utf-8", errors="replace")
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# Try to parse JSON values
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try:
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dv = json.loads(dv)
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except Exception:
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pass
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raw_meta[dk] = dv
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except Exception:
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pass
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columns = []
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for field in schema:
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col = {
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"name": field.name,
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"type": str(field.type),
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"nullable": field.nullable,
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}
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if field.name in col_descs:
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col["description"] = col_descs[field.name]
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# Check field-level metadata
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if field.metadata:
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for k, v in field.metadata.items():
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try:
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dk = k.decode("utf-8", errors="replace")
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dv = v.decode("utf-8", errors="replace")
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if dk in ("description", "DESCRIPTION", "comment"):
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col["description"] = dv
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except Exception:
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pass
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columns.append(col)
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schemas[f"{dataset}.{table}"] = {
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"path": f"s3://{S3_BUCKET}/{dataset}/{table}/",
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"file_count": len(info["files"]),
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"total_size_bytes": info["total_size_bytes"],
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"total_size_human": fmt_size(info["total_size_bytes"]),
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"columns": columns,
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"metadata": raw_meta,
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}
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print(f" [{i+1}/{len(inventory)}] ✓ {dataset}.{table} ({len(columns)} cols, {fmt_size(info['total_size_bytes'])})")
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except Exception as e:
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errors.append({"table": f"{dataset}.{table}", "error": str(e)})
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print(f" [{i+1}/{len(inventory)}] ✗ {dataset}.{table}: {e}", file=sys.stderr)
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# ------------------------------------------------------------------ #
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# Phase 3: Enrich from br_bd_metadados.bigquery_tables (small table)
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# ------------------------------------------------------------------ #
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META_TABLE = "br_bd_metadados.bigquery_tables"
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meta_dt = "br_bd_metadados/bigquery_tables"
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if meta_dt in inventory:
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print(f"Phase 3: enriching from {META_TABLE}...")
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try:
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con = duckdb.connect()
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con.execute("INSTALL httpfs; LOAD httpfs;")
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con.execute(f"""
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SET s3_endpoint='{s3_host}';
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SET s3_access_key_id='{ACCESS_KEY}';
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SET s3_secret_access_key='{SECRET_KEY}';
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SET s3_url_style='path';
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""")
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meta_path = f"s3://{S3_BUCKET}/br_bd_metadados/bigquery_tables/*.parquet"
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# Peek at available columns
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available = [r[0] for r in con.execute(f"DESCRIBE SELECT * FROM '{meta_path}' LIMIT 1").fetchall()]
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print(f" Metadata columns: {available}")
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# Try to find dataset/table description columns
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desc_col = next((c for c in available if "description" in c.lower()), None)
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ds_col = next((c for c in available if c.lower() in ("dataset_id", "dataset", "schema_name")), None)
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tbl_col = next((c for c in available if c.lower() in ("table_id", "table_name", "table")), None)
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if desc_col and ds_col and tbl_col:
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rows = con.execute(f"""
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SELECT {ds_col}, {tbl_col}, {desc_col}
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FROM '{meta_path}'
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""").fetchall()
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for ds, tbl, desc in rows:
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key = f"{ds}.{tbl}"
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if key in schemas and desc:
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schemas[key]["table_description"] = desc
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print(f" Enriched {len(rows)} table descriptions")
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else:
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print(f" Could not find expected columns (dataset_id, table_id, description) — skipping enrichment")
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con.close()
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except Exception as e:
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print(f" Enrichment failed: {e}", file=sys.stderr)
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else:
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print("Phase 3: br_bd_metadados.bigquery_tables not in S3 — skipping enrichment")
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# ------------------------------------------------------------------ #
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# Phase 4a: Write schemas.json
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# ------------------------------------------------------------------ #
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print("Phase 4: writing outputs...")
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output = {
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"_meta": {
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"bucket": S3_BUCKET,
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"total_tables": len(schemas),
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"total_size_bytes": sum(v["total_size_bytes"] for v in schemas.values()),
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"total_size_human": fmt_size(sum(v["total_size_bytes"] for v in schemas.values())),
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"errors": errors,
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},
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"tables": dict(sorted(schemas.items())),
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}
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with open("schemas.json", "w", encoding="utf-8") as f:
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json.dump(output, f, ensure_ascii=False, indent=2)
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print(f" ✓ schemas.json ({len(schemas)} tables)")
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# ------------------------------------------------------------------ #
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# Phase 4b: Write file_tree.md
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# ------------------------------------------------------------------ #
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lines = [
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f"# S3 File Tree: {S3_BUCKET}",
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"",
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]
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# Group by dataset
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datasets_map = {}
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for dt_key, info in sorted(inventory.items()):
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dataset, table = dt_key.split("/", 1)
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datasets_map.setdefault(dataset, []).append((table, info))
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total_files = sum(len(v["files"]) for v in inventory.values())
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total_bytes = sum(v["total_size_bytes"] for v in inventory.values())
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for dataset, tables in sorted(datasets_map.items()):
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ds_bytes = sum(i["total_size_bytes"] for _, i in tables)
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ds_files = sum(len(i["files"]) for _, i in tables)
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lines.append(f"## {dataset}/ ({len(tables)} tables, {fmt_size(ds_bytes)}, {ds_files} files)")
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lines.append("")
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for table, info in sorted(tables):
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schema_entry = schemas.get(f"{dataset}.{table}", {})
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ncols = len(schema_entry.get("columns", []))
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col_str = f", {ncols} cols" if ncols else ""
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table_desc = schema_entry.get("table_description", "")
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desc_str = f" — {table_desc}" if table_desc else ""
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lines.append(f" - **{table}/** ({len(info['files'])} files, {fmt_size(info['total_size_bytes'])}{col_str}){desc_str}")
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lines.append("")
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lines += [
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"---",
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f"**Total: {len(inventory)} tables · {fmt_size(total_bytes)} · {total_files} parquet files**",
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]
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with open("file_tree.md", "w", encoding="utf-8") as f:
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f.write("\n".join(lines) + "\n")
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print(f" ✓ file_tree.md ({len(inventory)} tables)")
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print()
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print("Done!")
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print(f" schemas.json — full column-level schema dump")
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print(f" file_tree.md — bucket tree with sizes")
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if errors:
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print(f" {len(errors)} tables failed (see schemas.json _meta.errors)")
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