explain.depesz.com

PostgreSQL's explain analyze made readable

Result: Atel

Settings
# exclusive inclusive rows x rows loops node
1. 0.000 0.000 ↓ 0.0

Subquery Scan on result (cost=544,016,876.25..577,655,524.00 rows=354,091,029 width=120) (actual rows= loops=)

2. 0.000 0.000 ↓ 0.0

Sort (cost=544,016,876.25..544,902,103.82 rows=354,091,029 width=130) (actual rows= loops=)

  • Sort Key: refined_1.commodity_code
3. 0.000 0.000 ↓ 0.0

Merge Full Join (cost=424,801,897.08..469,531,312.09 rows=354,091,029 width=130) (actual rows= loops=)

  • Merge Cond: ((refined_1.commodity_code = comtrade_data_monthly.commodity_code) AND (refined_1.aggregate_level = comtrade_data_monthly.aggregate_level) AND (refined_1.year = comtrade_data_monthly.yea
  • Filter: (GREATEST((COALESCE(refined_1.trade_value, '0'::text))::double precision, (COALESCE(comtrade_data_monthly.trade_value, '0'::text))::double precision) > '0'::double precision)
4. 0.000 0.000 ↓ 0.0

Sort (cost=212,400,948.54..215,056,631.26 rows=1,062,273,088 width=35) (actual rows= loops=)

  • Sort Key: refined_1.commodity_code, refined_1.aggregate_level, refined_1.year, refined_1.period, refined_1.reporter_code, refined_1.partner_code
5. 0.000 0.000 ↓ 0.0

Seq Scan on comtrade_data_monthly refined_1 (cost=0.00..24,095,739.88 rows=1,062,273,088 width=35) (actual rows= loops=)

6. 0.000 0.000 ↓ 0.0

Materialize (cost=212,400,948.54..217,712,313.98 rows=1,062,273,088 width=35) (actual rows= loops=)

7. 0.000 0.000 ↓ 0.0

Sort (cost=212,400,948.54..215,056,631.26 rows=1,062,273,088 width=35) (actual rows= loops=)

  • Sort Key: comtrade_data_monthly.commodity_code, comtrade_data_monthly.aggregate_level, comtrade_data_monthly.year, comtrade_data_monthly.period, comtrade_data_monthly.reporter_code, comt
8. 0.000 0.000 ↓ 0.0

Seq Scan on comtrade_data_monthly (cost=0.00..24,095,739.88 rows=1,062,273,088 width=35) (actual rows= loops=)