Result: FW3q : Optimization for: plan #Oa5h

Settings

Optimization path:

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

(cost=72,697,149.00..74,175,198.29 rows=15,618,557 width=73) (actual rows= loops=)

• Hash Cond: (pay.agg_rgs_id = tab.agg_rgs_id)
2.

CTE w_rnd

3. 0.000 0.000 ↓ 0.0

Gather (cost=21,323,941.81..63,474,130.50 rows=14,981,317 width=21) (actual rows= loops=)

• Workers Planned: 2
4. 0.000 0.000 ↓ 0.0

Parallel Hash Join (cost=21,322,941.81..61,974,998.80 rows=6,242,215 width=21) (actual rows= loops=)

• Hash Cond: (pay_1.game_round_id = rnd.id)
5. 0.000 0.000 ↓ 0.0

Parallel Seq Scan on game_payments pay_1 (cost=0.00..31,900,776.53 rows=536,130,453 width=29) (actual rows= loops=)

6. 0.000 0.000 ↓ 0.0

Parallel Hash (cost=21,264,026.06..21,264,026.06 rows=3,591,020 width=8) (actual rows= loops=)

7. 0.000 0.000 ↓ 0.0

Parallel Index Scan using game_rounds_x1 on game_rounds rnd (cost=0.57..21,264,026.06 rows=3,591,020 width=8) (actual rows= loops=)

• Index Cond: ((end_time > '2019-11-01 00:00:00+00'::timestamp with time zone) AND (end_time <= '2019-11-0
8. 0.000 0.000 ↓ 0.0

CTE Scan on w_rnd pay (cost=0.00..299,626.34 rows=14,981,317 width=32) (actual rows= loops=)

9. 0.000 0.000 ↓ 0.0

(cost=6,960,002.00..6,960,002.00 rows=106,299,480 width=41) (actual rows= loops=)

10. 0.000 0.000 ↓ 0.0

Seq Scan on translated_ids tab (cost=0.00..6,960,002.00 rows=106,299,480 width=41) (actual rows= loops=)

• Filter: ((translated_id_type)::text = 'P'::text)