590 lines
26 KiB
Python
590 lines
26 KiB
Python
"""
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BIGQUERY ANALYTICAL LAYER - DATA FILTERING
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FIXED: fact_asean_food_security_selected disimpan di fs_asean_gold (layer='gold')
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Filtering Order:
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1. Load data (single years only)
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2. Determine year boundaries (2013 - auto-detected end year)
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3. Filter complete indicators PER COUNTRY (auto-detect start year, no gaps)
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4. Filter countries with ALL pillars (FIXED SET)
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5. Filter indicators with consistent presence across FIXED countries
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6. Save analytical table (dengan nama/label lengkap untuk Looker Studio)
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"""
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import pandas as pd
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import numpy as np
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from datetime import datetime
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import logging
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from typing import Dict, List
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import json
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import sys
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if hasattr(sys.stdout, 'reconfigure'):
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sys.stdout.reconfigure(encoding='utf-8')
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from scripts.bigquery_config import get_bigquery_client, CONFIG, get_table_id
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from scripts.bigquery_helpers import (
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log_update,
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load_to_bigquery,
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read_from_bigquery,
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setup_logging,
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truncate_table,
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save_etl_metadata,
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)
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from google.cloud import bigquery
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# =============================================================================
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# ANALYTICAL LAYER CLASS
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# =============================================================================
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class AnalyticalLayerLoader:
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"""
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Analytical Layer Loader for BigQuery
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Key Logic:
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1. Complete per country (no gaps from start_year to end_year)
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2. Filter countries with all pillars
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3. Ensure indicators have consistent country count across all years
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4. Save dengan kolom lengkap (nama + ID) untuk kemudahan Looker Studio
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Output: fact_asean_food_security_selected -> DW layer (Gold) -> fs_asean_gold
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"""
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def __init__(self, client: bigquery.Client):
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self.client = client
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self.logger = logging.getLogger(self.__class__.__name__)
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self.logger.propagate = False
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self.df_clean = None
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self.df_indicator = None
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self.df_country = None
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self.df_pillar = None
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self.selected_country_ids = None
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self.start_year = 2013
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self.end_year = None
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self.baseline_year = 2023
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self.pipeline_metadata = {
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'source_class' : self.__class__.__name__,
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'start_time' : None,
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'end_time' : None,
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'duration_seconds' : None,
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'rows_fetched' : 0,
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'rows_transformed' : 0,
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'rows_loaded' : 0,
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'validation_metrics': {}
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}
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self.pipeline_start = None
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self.pipeline_end = None
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def load_source_data(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 1: LOADING SOURCE DATA from fs_asean_gold")
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self.logger.info("=" * 80)
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try:
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query = f"""
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SELECT
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f.country_id,
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c.country_name,
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f.indicator_id,
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i.indicator_name,
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i.direction,
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f.pillar_id,
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p.pillar_name,
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f.time_id,
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t.year,
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t.start_year,
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t.end_year,
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t.is_year_range,
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f.value,
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f.source_id
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FROM `{get_table_id('fact_food_security', layer='gold')}` f
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JOIN `{get_table_id('dim_country', layer='gold')}` c ON f.country_id = c.country_id
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JOIN `{get_table_id('dim_indicator', layer='gold')}` i ON f.indicator_id = i.indicator_id
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JOIN `{get_table_id('dim_pillar', layer='gold')}` p ON f.pillar_id = p.pillar_id
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JOIN `{get_table_id('dim_time', layer='gold')}` t ON f.time_id = t.time_id
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"""
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self.logger.info("Loading fact table with dimensions...")
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self.df_clean = self.client.query(query).result().to_dataframe(create_bqstorage_client=False)
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self.logger.info(f" Loaded: {len(self.df_clean):,} rows")
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if 'is_year_range' in self.df_clean.columns:
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yr = self.df_clean['is_year_range'].value_counts()
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self.logger.info(f" Breakdown:")
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self.logger.info(f" Single years (is_year_range=False): {yr.get(False, 0):,}")
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self.logger.info(f" Year ranges (is_year_range=True): {yr.get(True, 0):,}")
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self.df_indicator = read_from_bigquery(self.client, 'dim_indicator', layer='gold')
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self.df_country = read_from_bigquery(self.client, 'dim_country', layer='gold')
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self.df_pillar = read_from_bigquery(self.client, 'dim_pillar', layer='gold')
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self.logger.info(f" Indicators: {len(self.df_indicator)}")
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self.logger.info(f" Countries: {len(self.df_country)}")
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self.logger.info(f" Pillars: {len(self.df_pillar)}")
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self.pipeline_metadata['rows_fetched'] = len(self.df_clean)
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return True
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except Exception as e:
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self.logger.error(f"Error loading source data: {e}")
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raise
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def determine_year_boundaries(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 2: DETERMINE YEAR BOUNDARIES")
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self.logger.info("=" * 80)
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df_2023 = self.df_clean[self.df_clean['year'] == self.baseline_year]
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baseline_indicator_count = df_2023['indicator_id'].nunique()
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self.logger.info(f"\nBaseline Year: {self.baseline_year}")
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self.logger.info(f"Baseline Indicator Count: {baseline_indicator_count}")
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years_sorted = sorted(self.df_clean['year'].unique(), reverse=True)
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selected_end_year = None
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for year in years_sorted:
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if year >= self.baseline_year:
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df_year = self.df_clean[self.df_clean['year'] == year]
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year_indicator_count = df_year['indicator_id'].nunique()
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status = "OK" if year_indicator_count >= baseline_indicator_count else "X"
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self.logger.info(f" [{status}] Year {int(year)}: {year_indicator_count} indicators")
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if year_indicator_count >= baseline_indicator_count and selected_end_year is None:
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selected_end_year = int(year)
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if selected_end_year is None:
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selected_end_year = self.baseline_year
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self.logger.warning(f" [!] No year found, using baseline: {selected_end_year}")
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else:
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self.logger.info(f"\n [OK] Selected End Year: {selected_end_year}")
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self.end_year = selected_end_year
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[
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(self.df_clean['year'] >= self.start_year) &
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(self.df_clean['year'] <= self.end_year)
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].copy()
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self.logger.info(f"\nFiltering {self.start_year}-{self.end_year}:")
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self.logger.info(f" Rows before: {original_count:,}")
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self.logger.info(f" Rows after: {len(self.df_clean):,}")
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return self.df_clean
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def filter_complete_indicators_per_country(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 3: FILTER COMPLETE INDICATORS PER COUNTRY (NO GAPS)")
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self.logger.info("=" * 80)
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grouped = self.df_clean.groupby([
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'country_id', 'country_name', 'indicator_id', 'indicator_name',
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'pillar_id', 'pillar_name'
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])
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valid_combinations = []
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removed_combinations = []
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for (country_id, country_name, indicator_id, indicator_name,
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pillar_id, pillar_name), group in grouped:
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years_present = sorted(group['year'].unique())
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start_year = int(min(years_present))
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end_year_actual = int(max(years_present))
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expected_years = list(range(start_year, self.end_year + 1))
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missing_years = [y for y in expected_years if y not in years_present]
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has_gap = len(missing_years) > 0
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is_complete = (
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end_year_actual >= self.end_year and
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not has_gap and
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(self.end_year - start_year) >= 4
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)
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if is_complete:
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valid_combinations.append({'country_id': country_id, 'indicator_id': indicator_id})
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else:
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reasons = []
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if end_year_actual < self.end_year:
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reasons.append(f"ends {end_year_actual}")
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if has_gap:
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gap_str = str(missing_years[:3])[1:-1]
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if len(missing_years) > 3:
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gap_str += "..."
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reasons.append(f"gap:{gap_str}")
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if (self.end_year - start_year) < 4:
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reasons.append(f"span={self.end_year - start_year}")
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removed_combinations.append({
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'country_name' : country_name,
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'indicator_name': indicator_name,
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'reasons' : ", ".join(reasons)
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})
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self.logger.info(f"\n [+] Valid: {len(valid_combinations):,}")
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self.logger.info(f" [-] Removed: {len(removed_combinations):,}")
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df_valid = pd.DataFrame(valid_combinations)
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df_valid['key'] = df_valid['country_id'].astype(str) + '_' + df_valid['indicator_id'].astype(str)
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self.df_clean['key'] = (self.df_clean['country_id'].astype(str) + '_' +
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self.df_clean['indicator_id'].astype(str))
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[self.df_clean['key'].isin(df_valid['key'])].copy()
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self.df_clean = self.df_clean.drop('key', axis=1)
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self.logger.info(f"\n Rows before: {original_count:,}")
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self.logger.info(f" Rows after: {len(self.df_clean):,}")
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self.logger.info(f" Countries: {self.df_clean['country_id'].nunique()}")
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self.logger.info(f" Indicators: {self.df_clean['indicator_id'].nunique()}")
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return self.df_clean
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def select_countries_with_all_pillars(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 4: SELECT COUNTRIES WITH ALL PILLARS (FIXED SET)")
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self.logger.info("=" * 80)
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total_pillars = self.df_clean['pillar_id'].nunique()
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country_pillar_count = self.df_clean.groupby(['country_id', 'country_name']).agg({
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'pillar_id' : 'nunique',
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'indicator_id': 'nunique',
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'year' : lambda x: f"{int(x.min())}-{int(x.max())}"
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}).reset_index()
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country_pillar_count.columns = [
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'country_id', 'country_name', 'pillar_count', 'indicator_count', 'year_range'
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]
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for _, row in country_pillar_count.sort_values('pillar_count', ascending=False).iterrows():
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status = "[+] KEEP" if row['pillar_count'] == total_pillars else "[-] REMOVE"
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self.logger.info(
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f" {status:<12} {row['country_name']:25s} "
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f"{row['pillar_count']}/{total_pillars} pillars"
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)
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selected_countries = country_pillar_count[country_pillar_count['pillar_count'] == total_pillars]
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self.selected_country_ids = selected_countries['country_id'].tolist()
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self.logger.info(f"\n FIXED SET: {len(self.selected_country_ids)} countries")
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[self.df_clean['country_id'].isin(self.selected_country_ids)].copy()
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self.logger.info(f" Rows before: {original_count:,}")
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self.logger.info(f" Rows after: {len(self.df_clean):,}")
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return self.df_clean
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def filter_indicators_consistent_across_fixed_countries(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 5: FILTER INDICATORS WITH CONSISTENT PRESENCE")
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self.logger.info("=" * 80)
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indicator_country_start = self.df_clean.groupby([
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'indicator_id', 'indicator_name', 'country_id'
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])['year'].min().reset_index()
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indicator_country_start.columns = ['indicator_id', 'indicator_name', 'country_id', 'start_year']
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indicator_max_start = indicator_country_start.groupby([
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'indicator_id', 'indicator_name'
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])['start_year'].max().reset_index()
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indicator_max_start.columns = ['indicator_id', 'indicator_name', 'max_start_year']
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valid_indicators = []
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removed_indicators = []
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for _, ind_row in indicator_max_start.iterrows():
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indicator_id = ind_row['indicator_id']
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indicator_name = ind_row['indicator_name']
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max_start = int(ind_row['max_start_year'])
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span = self.end_year - max_start
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if span < 4:
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removed_indicators.append({
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'indicator_name': indicator_name,
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'reason' : f"span={span} < 4"
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})
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continue
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expected_years = list(range(max_start, self.end_year + 1))
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ind_data = self.df_clean[self.df_clean['indicator_id'] == indicator_id]
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all_years_complete = True
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problematic_years = []
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for year in expected_years:
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country_count = ind_data[ind_data['year'] == year]['country_id'].nunique()
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if country_count < len(self.selected_country_ids):
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all_years_complete = False
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problematic_years.append(f"{int(year)}({country_count})")
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if all_years_complete:
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valid_indicators.append(indicator_id)
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else:
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removed_indicators.append({
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'indicator_name': indicator_name,
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'reason' : f"missing countries in years: {', '.join(problematic_years[:5])}"
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})
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self.logger.info(f"\n [+] Valid: {len(valid_indicators)}")
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self.logger.info(f" [-] Removed: {len(removed_indicators)}")
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if not valid_indicators:
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raise ValueError("No valid indicators found after filtering!")
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original_count = len(self.df_clean)
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self.df_clean = self.df_clean[self.df_clean['indicator_id'].isin(valid_indicators)].copy()
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self.df_clean = self.df_clean.merge(
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indicator_max_start[['indicator_id', 'max_start_year']], on='indicator_id', how='left'
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)
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self.df_clean = self.df_clean[self.df_clean['year'] >= self.df_clean['max_start_year']].copy()
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self.df_clean = self.df_clean.drop('max_start_year', axis=1)
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self.logger.info(f"\n Rows before: {original_count:,}")
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self.logger.info(f" Rows after: {len(self.df_clean):,}")
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self.logger.info(f" Countries: {self.df_clean['country_id'].nunique()}")
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self.logger.info(f" Indicators: {self.df_clean['indicator_id'].nunique()}")
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self.logger.info(f" Pillars: {self.df_clean['pillar_id'].nunique()}")
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return self.df_clean
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def verify_no_gaps(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 6: VERIFY NO GAPS")
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self.logger.info("=" * 80)
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expected_countries = len(self.selected_country_ids)
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verification = self.df_clean.groupby(['indicator_id', 'year'])['country_id'].nunique().reset_index()
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verification.columns = ['indicator_id', 'year', 'country_count']
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all_good = (verification['country_count'] == expected_countries).all()
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if all_good:
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self.logger.info(f" VERIFICATION PASSED — all combinations have {expected_countries} countries")
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else:
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bad = verification[verification['country_count'] != expected_countries]
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for _, row in bad.head(10).iterrows():
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self.logger.error(
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f" Indicator {int(row['indicator_id'])}, Year {int(row['year'])}: "
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f"{int(row['country_count'])} countries (expected {expected_countries})"
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)
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raise ValueError("Gap verification failed!")
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return True
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def analyze_indicator_availability_by_year(self):
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self.logger.info("\n" + "=" * 80)
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self.logger.info("STEP 7: ANALYZE INDICATOR AVAILABILITY BY YEAR")
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self.logger.info("=" * 80)
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year_stats = self.df_clean.groupby('year').agg({
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'indicator_id': 'nunique',
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'country_id' : 'nunique'
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}).reset_index()
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year_stats.columns = ['year', 'indicator_count', 'country_count']
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self.logger.info(f"\n{'Year':<8} {'Indicators':<15} {'Countries':<12} {'Rows'}")
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self.logger.info("-" * 50)
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for _, row in year_stats.iterrows():
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year = int(row['year'])
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row_count = len(self.df_clean[self.df_clean['year'] == year])
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self.logger.info(
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f"{year:<8} {int(row['indicator_count']):<15} "
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f"{int(row['country_count']):<12} {row_count:,}"
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)
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indicator_details = self.df_clean.groupby([
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'indicator_id', 'indicator_name', 'pillar_name', 'direction'
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]).agg({'year': ['min', 'max'], 'country_id': 'nunique'}).reset_index()
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indicator_details.columns = [
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'indicator_id', 'indicator_name', 'pillar_name', 'direction',
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'start_year', 'end_year', 'country_count'
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]
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indicator_details['year_range'] = (
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indicator_details['start_year'].astype(int).astype(str) + '-' +
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indicator_details['end_year'].astype(int).astype(str)
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)
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indicator_details = indicator_details.sort_values(['pillar_name', 'start_year', 'indicator_name'])
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self.logger.info(f"\nTotal Indicators: {len(indicator_details)}")
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for pillar, count in indicator_details.groupby('pillar_name').size().items():
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self.logger.info(f" {pillar}: {count} indicators")
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self.logger.info(f"\n{'-'*100}")
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self.logger.info(f"{'ID':<5} {'Indicator Name':<55} {'Pillar':<15} {'Years':<12} {'Dir':<8} {'Countries'}")
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self.logger.info(f"{'-'*100}")
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for _, row in indicator_details.iterrows():
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direction = 'higher+' if row['direction'] == 'higher_better' else 'lower-'
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self.logger.info(
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f"{int(row['indicator_id']):<5} {row['indicator_name'][:52]:<55} "
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f"{row['pillar_name'][:13]:<15} {row['year_range']:<12} "
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f"{direction:<8} {int(row['country_count'])}"
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)
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return year_stats
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def save_analytical_table(self):
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# ---------------------------------------------------------------
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# CHANGED: nama tabel baru + kolom lengkap untuk Looker Studio
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|
# ---------------------------------------------------------------
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table_name = 'fact_asean_food_security_selected'
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|
|
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self.logger.info("\n" + "=" * 80)
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self.logger.info(f"STEP 8: SAVE TO [DW/Gold] {table_name} -> fs_asean_gold")
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self.logger.info("=" * 80)
|
|
|
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try:
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# ------------------------------------------------------------------
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# Pilih kolom: ID + Nama lengkap + value
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# Kolom nama memudahkan filtering/slicing langsung di Looker Studio
|
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# tanpa perlu join ulang ke tabel dimensi.
|
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# ------------------------------------------------------------------
|
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analytical_df = self.df_clean[[
|
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'country_id',
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'country_name',
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'indicator_id',
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'indicator_name',
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'direction',
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|
'pillar_id',
|
|
'pillar_name',
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'time_id',
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'year',
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|
'value',
|
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]].copy()
|
|
|
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analytical_df = analytical_df.sort_values(
|
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['year', 'country_name', 'pillar_name', 'indicator_name']
|
|
).reset_index(drop=True)
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|
|
|
# Pastikan tipe data konsisten
|
|
analytical_df['country_id'] = analytical_df['country_id'].astype(int)
|
|
analytical_df['country_name'] = analytical_df['country_name'].astype(str)
|
|
analytical_df['indicator_id'] = analytical_df['indicator_id'].astype(int)
|
|
analytical_df['indicator_name']= analytical_df['indicator_name'].astype(str)
|
|
analytical_df['direction'] = analytical_df['direction'].astype(str)
|
|
analytical_df['pillar_id'] = analytical_df['pillar_id'].astype(int)
|
|
analytical_df['pillar_name'] = analytical_df['pillar_name'].astype(str)
|
|
analytical_df['time_id'] = analytical_df['time_id'].astype(int)
|
|
analytical_df['year'] = analytical_df['year'].astype(int)
|
|
analytical_df['value'] = analytical_df['value'].astype(float)
|
|
|
|
self.logger.info(f" Kolom yang disimpan: {list(analytical_df.columns)}")
|
|
self.logger.info(f" Total rows: {len(analytical_df):,}")
|
|
|
|
# Schema BigQuery
|
|
schema = [
|
|
bigquery.SchemaField("country_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("country_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("indicator_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("direction", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("pillar_name", "STRING", mode="REQUIRED"),
|
|
bigquery.SchemaField("time_id", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("year", "INTEGER", mode="REQUIRED"),
|
|
bigquery.SchemaField("value", "FLOAT", mode="REQUIRED"),
|
|
]
|
|
|
|
rows_loaded = load_to_bigquery(
|
|
self.client, analytical_df, table_name,
|
|
layer='gold', write_disposition="WRITE_TRUNCATE", schema=schema
|
|
)
|
|
|
|
self.pipeline_metadata['rows_loaded'] = rows_loaded
|
|
log_update(self.client, 'DW', table_name, 'full_load', rows_loaded)
|
|
|
|
metadata = {
|
|
'source_class' : self.__class__.__name__,
|
|
'table_name' : table_name,
|
|
'execution_timestamp': self.pipeline_start,
|
|
'duration_seconds' : (datetime.now() - self.pipeline_start).total_seconds(),
|
|
'rows_fetched' : self.pipeline_metadata['rows_fetched'],
|
|
'rows_transformed' : rows_loaded,
|
|
'rows_loaded' : rows_loaded,
|
|
'completeness_pct' : 100.0,
|
|
'config_snapshot' : json.dumps({
|
|
'start_year' : self.start_year,
|
|
'end_year' : self.end_year,
|
|
'fixed_countries': len(self.selected_country_ids),
|
|
'no_gaps' : True,
|
|
'layer' : 'gold',
|
|
'columns' : 'id + name + value (Looker Studio ready)'
|
|
}),
|
|
'validation_metrics' : json.dumps({
|
|
'fixed_countries' : len(self.selected_country_ids),
|
|
'total_indicators': int(self.df_clean['indicator_id'].nunique())
|
|
})
|
|
}
|
|
save_etl_metadata(self.client, metadata)
|
|
|
|
self.logger.info(f" ✓ {table_name}: {rows_loaded:,} rows → [DW/Gold] fs_asean_gold")
|
|
self.logger.info(f" Metadata → [AUDIT] etl_metadata")
|
|
return rows_loaded
|
|
|
|
except Exception as e:
|
|
self.logger.error(f"Error saving: {e}")
|
|
raise
|
|
|
|
def run(self):
|
|
self.pipeline_start = datetime.now()
|
|
self.pipeline_metadata['start_time'] = self.pipeline_start
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("Output: fact_asean_food_security_selected → fs_asean_gold")
|
|
self.logger.info("=" * 80)
|
|
|
|
self.load_source_data()
|
|
self.determine_year_boundaries()
|
|
self.filter_complete_indicators_per_country()
|
|
self.select_countries_with_all_pillars()
|
|
self.filter_indicators_consistent_across_fixed_countries()
|
|
self.verify_no_gaps()
|
|
self.analyze_indicator_availability_by_year()
|
|
self.save_analytical_table()
|
|
|
|
self.pipeline_end = datetime.now()
|
|
duration = (self.pipeline_end - self.pipeline_start).total_seconds()
|
|
|
|
self.logger.info("\n" + "=" * 80)
|
|
self.logger.info("COMPLETED")
|
|
self.logger.info("=" * 80)
|
|
self.logger.info(f" Duration : {duration:.2f}s")
|
|
self.logger.info(f" Year Range : {self.start_year}-{self.end_year}")
|
|
self.logger.info(f" Countries : {len(self.selected_country_ids)}")
|
|
self.logger.info(f" Indicators : {self.df_clean['indicator_id'].nunique()}")
|
|
self.logger.info(f" Rows Loaded: {self.pipeline_metadata['rows_loaded']:,}")
|
|
|
|
|
|
# =============================================================================
|
|
# AIRFLOW TASK FUNCTION
|
|
# =============================================================================
|
|
|
|
def run_analytical_layer():
|
|
"""
|
|
Airflow task: Build fact_asean_food_security_selected dari fact_food_security + dims.
|
|
Dipanggil setelah dimensional_model_to_gold selesai.
|
|
"""
|
|
from scripts.bigquery_config import get_bigquery_client
|
|
client = get_bigquery_client()
|
|
loader = AnalyticalLayerLoader(client)
|
|
loader.run()
|
|
print(f"Analytical layer loaded: {loader.pipeline_metadata['rows_loaded']:,} rows")
|
|
|
|
|
|
# =============================================================================
|
|
# MAIN EXECUTION
|
|
# =============================================================================
|
|
|
|
if __name__ == "__main__":
|
|
print("=" * 80)
|
|
print("Output: fact_asean_food_security_selected → fs_asean_gold")
|
|
print("=" * 80)
|
|
|
|
logger = setup_logging()
|
|
client = get_bigquery_client()
|
|
loader = AnalyticalLayerLoader(client)
|
|
loader.run()
|
|
|
|
print("\n" + "=" * 80)
|
|
print("[OK] COMPLETED")
|
|
print("=" * 80) |