explain.depesz.com

PostgreSQL's explain analyze made readable

Result: uRCh

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

Limit (cost=280,728.85..280,728.90 rows=1 width=56) (actual rows= loops=)

2. 0.000 0.000 ↓ 0.0

GroupAggregate (cost=280,728.85..280,728.89 rows=1 width=56) (actual rows= loops=)

  • Group Key: (date_trunc('day'::text, "12664233803368080971"."1595310173297012401"))
3. 0.000 0.000 ↓ 0.0

Sort (cost=280,728.85..280,728.86 rows=1 width=20) (actual rows= loops=)

  • Sort Key: (date_trunc('day'::text, "12664233803368080971"."1595310173297012401"))
4. 0.000 0.000 ↓ 0.0

Nested Loop (cost=225,547.24..280,728.84 rows=1 width=20) (actual rows= loops=)

  • Join Filter: (("12664233803368080971"."16241286553150758564" = "8474989575017060194"."13547456022938880825") AND ("12664233803368080971"."14321061946992575142" = "8474989575017060194"."6533036012599388963"))
5. 0.000 0.000 ↓ 0.0

Hash Right Join (cost=146,674.70..201,300.53 rows=1 width=64) (actual rows= loops=)

  • Hash Cond: (("1080419292081764268"."10256110715429805520" = "12664233803368080971"."5035617706788715985") AND ("1080419292081764268"."9475408165495259831" = "12664233803368080971"."14321061946992575142"))
6. 0.000 0.000 ↓ 0.0

Subquery Scan on 1080419292081764268 (cost=91,982.88..146,591.64 rows=2,275 width=57) (actual rows= loops=)

  • Filter: ("1080419292081764268"."11567860340026220787" = 1)
7. 0.000 0.000 ↓ 0.0

WindowAgg (cost=91,982.88..140,903.23 rows=455,073 width=243) (actual rows= loops=)

8. 0.000 0.000 ↓ 0.0

Sort (cost=91,982.88..93,120.56 rows=455,073 width=235) (actual rows= loops=)

  • Sort Key: "ImportTable_2019082717064389219581".col_0, "ImportTable_2019082717064389219581".col_1, "ImportTable_2019082717064389219581".col_2, "ImportTable_2019082717064389219581".col_3, "ImportTable_2019082717064389219581".col_4, "ImportTable_2019082717064389219581".col_5, "ImportTable_2019082717064389219581".col_6, "ImportTable_2019082717064389219581".col_7, "ImportTable_2019082717064389219581".col_8, "ImportTable_2019082717064389219581".col_9, "ImportTable_2019082717064389219581".col_10, "ImportTable_2019082717064389219581".col_11, "ImportTable_2019082717064389219581".col_12, "ImportTable_2019082717064389219581".col_13, "ImportTable_2019082717064389219581".col_14, "ImportTable_2019082717064389219581".col_15, (to_char("ImportTable_2019082717064389219581".col_0, 'YYYY-MM-DD'::text)), "ImportTable_2019082717064389219581".col_17
9. 0.000 0.000 ↓ 0.0

Seq Scan on "ImportTable_2019082717064389219581" (cost=0.00..19,150.41 rows=455,073 width=235) (actual rows= loops=)

10. 0.000 0.000 ↓ 0.0

Hash (cost=54,691.81..54,691.81 rows=1 width=88) (actual rows= loops=)

11. 0.000 0.000 ↓ 0.0

Subquery Scan on 12664233803368080971 (cost=54,690.97..54,691.81 rows=1 width=88) (actual rows= loops=)

  • Filter: ("12664233803368080971"."4201516046564707048" = 1)
12. 0.000 0.000 ↓ 0.0

WindowAgg (cost=54,690.97..54,691.73 rows=6 width=478) (actual rows= loops=)

13. 0.000 0.000 ↓ 0.0

Sort (cost=54,690.97..54,690.98 rows=6 width=470) (actual rows= loops=)

  • Sort Key: "ImportTable_2019082717063854898594".col_0, "ImportTable_2019082717063854898594".col_1, "ImportTable_2019082717063854898594".col_2, "ImportTable_2019082717063854898594".col_3, "ImportTable_2019082717063854898594".col_4, "ImportTable_2019082717063854898594".col_5, "ImportTable_2019082717063854898594".col_6, "ImportTable_2019082717063854898594".col_7, "ImportTable_2019082717063854898594".col_8, "ImportTable_2019082717063854898594".col_9, "ImportTable_2019082717063854898594".col_10, "ImportTable_2019082717063854898594".col_11, "ImportTable_2019082717063854898594".col_12, "ImportTable_2019082717063854898594".col_13, (CASE WHEN (date_part('month'::text, "ImportTable_2019082717063854898594".col_0) = '11'::double precision) THEN '11'::text WHEN (date_part('month'::text, "ImportTable_2019082717063854898594".col_0) = '12'::double precision) THEN '12'::text WHEN (date_part('month'::text, "ImportTable_2019082717063854898594".col_0) = '8'::double precision) THEN '08'::text WHEN (date_part('month'::text, "ImportTable_2019082717063854898594".col_0) = '9'::double precision) THEN '09'::text WHEN (date_part('month'::text, "ImportTable_2019082717063854898594".col_0) = '10'::double precision) THEN '10'::text ELSE 'NA'::text END), (to_char("ImportTable_2019082717063854898594".col_0, 'YYYY-MM-DD'::text)), "ImportTable_2019082717063854898594".col_15
14. 0.000 0.000 ↓ 0.0

Gather (cost=1,000.00..54,690.89 rows=6 width=470) (actual rows= loops=)

  • Workers Planned: 2
15. 0.000 0.000 ↓ 0.0

Parallel Seq Scan on "ImportTable_2019082717063854898594" (cost=0.00..53,690.29 rows=2 width=470) (actual rows= loops=)

  • Filter: ((col_0 >= '2019-12-16 00:00:00+00'::timestamp with time zone) AND (col_0 <= '2019-12-31 23:59:59+00'::timestamp with time zone) AND (col_1 <= '1000'::numeric) AND (col_2 = ANY ('{美白淡斑,保湿面膜}'::text[])) AND (col_0 >= '2019-12-16 00:00:00+00'::timestamp with time zone) AND (col_0 <= '2019-12-31 23:59:59+00'::timestamp with time zone) AND (col_1 <= '1000'::numeric) AND (col_2 = ANY ('{美白淡斑,保湿面膜}'::text[])) AND (col_0 >= '2019-12-16 00:00:00+00'::timestamp with time zone) AND (col_0 <= '2019-12-31 23:59:59+00'::timestamp with time zone) AND (col_1 <= '1000'::numeric) AND (col_2 = ANY ('{美白淡斑,保湿面膜}'::text[])))
16. 0.000 0.000 ↓ 0.0

Subquery Scan on 8474989575017060194 (cost=78,872.54..79,428.10 rows=14 width=69) (actual rows= loops=)

  • Filter: ("8474989575017060194"."11703359359500454926" = 1)
17. 0.000 0.000 ↓ 0.0

WindowAgg (cost=78,872.54..79,392.48 rows=2,849 width=921) (actual rows= loops=)

18. 0.000 0.000 ↓ 0.0

Sort (cost=78,872.54..78,879.66 rows=2,849 width=913) (actual rows= loops=)

  • Sort Key: "ImportTable_2019082717064260437142".col_0, "ImportTable_2019082717064260437142".col_1, "ImportTable_2019082717064260437142".col_2, "ImportTable_2019082717064260437142".col_3, "ImportTable_2019082717064260437142".col_4, "ImportTable_2019082717064260437142".col_5, "ImportTable_2019082717064260437142".col_6, "ImportTable_2019082717064260437142".col_7, "ImportTable_2019082717064260437142".col_8, "ImportTable_2019082717064260437142".col_9, "ImportTable_2019082717064260437142".col_10, "ImportTable_2019082717064260437142".col_11, "ImportTable_2019082717064260437142".col_12, "ImportTable_2019082717064260437142".col_13, "ImportTable_2019082717064260437142".col_14, "ImportTable_2019082717064260437142".col_15, "ImportTable_2019082717064260437142".col_16, "ImportTable_2019082717064260437142".col_17, "ImportTable_2019082717064260437142".col_18, "ImportTable_2019082717064260437142".col_19, "ImportTable_2019082717064260437142".col_20, "ImportTable_2019082717064260437142".col_21, "ImportTable_2019082717064260437142".col_22, "ImportTable_2019082717064260437142".col_23, "ImportTable_2019082717064260437142".col_24, "ImportTable_2019082717064260437142".col_25, "ImportTable_2019082717064260437142".col_26, "ImportTable_2019082717064260437142".col_27, "ImportTable_2019082717064260437142".col_28, (date_part('day'::text, ("ImportTable_2019082717064260437142".col_0 - "ImportTable_2019082717064260437142".col_9))), (to_char("ImportTable_2019082717064260437142".col_0, 'YYYY-MM-DD'::text)), "ImportTable_2019082717064260437142".col_30
19. 0.000 0.000 ↓ 0.0

Gather (cost=1,000.00..78,709.06 rows=2,849 width=913) (actual rows= loops=)

  • Workers Planned: 2
20. 0.000 0.000 ↓ 0.0

Parallel Seq Scan on "ImportTable_2019082717064260437142" (cost=0.00..77,424.16 rows=1,187 width=913) (actual rows= loops=)

  • Filter: ((date_part('day'::text, (col_0 - col_9)) >= '0'::double precision) AND (date_part('day'::text, (col_0 - col_9)) <= '99'::double precision) AND (date_part('day'::text, (col_0 - col_9)) >= '0'::double precision) AND (date_part('day'::text, (col_0 - col_9)) <= '99'::double precision) AND (date_part('day'::text, (col_0 - col_9)) >= '0'::double precision) AND (date_part('day'::text, (col_0 - col_9)) <= '99'::double precision))