File size: 4,491 Bytes
84d6b28 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 | import psycopg2
import psycopg2.extras
import json
import os
from dotenv import load_dotenv
from huggingface_hub import hf_hub_download
from pathlib import Path
from huggingface_hub import snapshot_download
from zoneinfo import ZoneInfo
load_dotenv()
POSTGRES_USER = os.environ.get("POSTGRES_USER")
POSTGRES_PASS = os.environ.get("POSTGRES_PASS")
HF_TOKEN = os.environ.get("HF_TOKEN")
def insert_data(website):
files_succeeded = 0
files_failed = 0
total_inserted = 0
total_duplicates = 0
repo_dir = snapshot_download(
repo_id="Amin1600/Web_Scraper_Data",
repo_type="dataset",
allow_patterns=f"job_data/raw/{website}/*",
token=HF_TOKEN,
tqdm_class=None
)
target_folder = Path(repo_dir) / "job_data" / "raw" / website
conn = psycopg2.connect(
host="localhost",
port="5433",
database="jobs",
user=POSTGRES_USER,
password=POSTGRES_PASS
)
cur = conn.cursor()
for job_file in target_folder.rglob('*.jsonl'):
cur.execute(
"SELECT 1 FROM file_metadata WHERE file_name = %s",
(job_file.name,)
)
if cur.fetchone():
print(f"Skipping {job_file.name} (already loaded)")
continue
# Build rows, tolerating bad JSON lines individually
rows = []
with open(job_file, "r", encoding="utf-8") as f:
for line_num, line in enumerate(f, start=1):
try:
job = json.loads(line)
except json.JSONDecodeError as e:
print(f"⚠️ Skipping malformed line {line_num} in {job_file.name}: {e}")
continue
rows.append((
job.get("source"),
job.get("search_term"),
job.get("job_id"),
job.get("url"),
job.get("collection_timestamp"),
job.get("job_title"),
job.get("company"),
job.get("location"),
job.get("employment_type"),
job.get("salary_min"),
job.get("salary_max"),
job.get("department"),
job.get("posting_date"),
job.get("job_description"),
job.get("requirements"),
json.dumps(job.get("skills", [])),
job.get("raw_html")
))
if not rows:
print(f"No valid rows in {job_file.name}, skipping.")
continue
try:
inserted = psycopg2.extras.execute_values(
cur,
"""
INSERT INTO stg_jobs (
source, search_term, job_id, url, collection_timestamp,
job_title, company, location, employment_type, salary_min,
salary_max, department, posting_date, job_description,
requirements, skills, raw_html
)
VALUES %s
ON CONFLICT (source, job_id)
DO NOTHING
RETURNING job_id;
""",
rows,
template="(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s::jsonb,%s)",
page_size=100,
fetch=True
)
num_inserted = len(inserted)
num_duplicates = len(rows) - num_inserted
cur.execute(
"INSERT INTO file_metadata (file_name) VALUES (%s)",
(job_file.name,)
)
conn.commit()
total_inserted += num_inserted
total_duplicates += num_duplicates
files_succeeded += 1
print(f"✅ {job_file.name}: {num_inserted} inserted, {num_duplicates} duplicates skipped")
except Exception as e:
conn.rollback()
files_failed += 1
print(f"❌ {job_file.name} failed, transaction rolled back: {e}")
continue
print("\n--- INSERTION REPORT ---")
print(f"Files loaded: {files_succeeded}")
print(f"Files failed: {files_failed}")
print(f"Total rows inserted: {total_inserted}")
print(f"Total duplicates skipped: {total_duplicates}")
cur.close()
conn.close()
if __name__ == "__main__":
scraped_websites = ["linkedin", "jobstreet"]
for website in scraped_websites:
insert_data(website) |