Performance of Query 60 on TPC-DS Benchmark

Hi all,

Please see this case:

TPC-DS Query 60:

with ss as (
select
i_item_id,sum(ss_ext_sales_price) total_sales
from
     store_sales,
     date_dim,
customer_address,
item
where
i_item_id in (select
i_item_id
from
item
where i_category in ('Children'))
and ss_item_sk = i_item_sk
and ss_sold_date_sk = d_date_sk
and d_year = 1999
and d_moy = 9
and ss_addr_sk = ca_address_sk
and ca_gmt_offset = -6
group by i_item_id),
cs as (
select
i_item_id,sum(cs_ext_sales_price) total_sales
from
     catalog_sales,
     date_dim,
customer_address,
item
where
i_item_id in (select
i_item_id
from
item
where i_category in ('Children'))
and cs_item_sk = i_item_sk
and cs_sold_date_sk = d_date_sk
and d_year = 1999
and d_moy = 9
and cs_bill_addr_sk = ca_address_sk
and ca_gmt_offset = -6
group by i_item_id),
ws as (
select
i_item_id,sum(ws_ext_sales_price) total_sales
from
     web_sales,
     date_dim,
customer_address,
item
where
i_item_id in (select
i_item_id
from
item
where i_category in ('Children'))
and ws_item_sk = i_item_sk
and ws_sold_date_sk = d_date_sk
and d_year = 1999
and d_moy = 9
and ws_bill_addr_sk = ca_address_sk
and ca_gmt_offset = -6
group by i_item_id)
select
i_item_id
,sum(total_sales) total_sales
from (select * from ss
union all
select * from cs
union all
select * from ws) tmp1
group by i_item_id
order by i_item_id
,total_sales
limit 100;

The query plan and execution time:

QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=98552.85..98569.84 rows=100 width=49) (actual time=1383.955..1390.492 rows=100 loops=1)
-> Incremental Sort (cost=98552.85..98583.60 rows=181 width=49) (actual time=1383.954..1390.485 rows=100 loops=1)
Sort Key: item.i_item_id, (sum((sum(store_sales.ss_ext_sales_price))))
Presorted Key: item.i_item_id
Full-sort Groups: 4 Sort Method: quicksort Average Memory: 27kB Peak Memory: 27kB
-> GroupAggregate (cost=98552.71..98575.46 rows=181 width=49) (actual time=1383.795..1390.437 rows=101 loops=1)
Group Key: item.i_item_id
-> Merge Append (cost=98552.71..98572.29 rows=181 width=49) (actual time=1383.782..1390.362 rows=225 loops=1)
Sort Key: item.i_item_id
-> Finalize GroupAggregate (cost=46679.80..46689.63 rows=103 width=49) (actual time=840.270..846.360 rows=94 loops=1)
Group Key: item.i_item_id
-> Gather Merge (cost=46679.80..46687.88 rows=61 width=49) (actual time=840.260..846.296 rows=95 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=45679.79..45681.01 rows=61 width=49) (actual time=837.065..837.346 rows=318 loops=2)
Group Key: item.i_item_id
-> Sort (cost=45679.79..45679.94 rows=61 width=23) (actual time=837.050..837.090 rows=950 loops=2)
Sort Key: item.i_item_id
Sort Method: quicksort Memory: 186kB
Worker 0: Sort Method: quicksort Memory: 190kB
-> Nested Loop (cost=3433.99..45677.98 rows=61 width=23) (actual time=13.422..835.693 rows=2334 loops=2)
-> Parallel Hash Join (cost=3433.70..45634.97 rows=138 width=27) (actual time=13.315..807.438 rows=5426 loops=2)
Hash Cond: (store_sales.ss_sold_date_sk = date_dim.d_date_sk)
-> Nested Loop (cost=1383.92..42700.04 rows=337185 width=31) (actual time=5.641..777.130 rows=267191 loops=2)
-> Parallel Hash Semi Join (cost=1383.49..2781.24 rows=2107 width=21) (actual time=5.589..10.939 rows=1931 loops=2)
Hash Cond: (item.i_item_id = item_1.i_item_id)
-> Parallel Seq Scan on item (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.006..2.443 rows=9000 loops=2)
-> Parallel Hash (cost=1370.35..1370.35 rows=1051 width=17) (actual time=5.534..5.535 rows=893 loops=2)
Buckets: 2048 Batches: 1 Memory Usage: 144kB
-> Parallel Seq Scan on item item_1 (cost=0.00..1370.35 rows=1051 width=17) (actual time=0.019..5.219 rows=893 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 8107
-> Index Scan using store_sales_pkey on store_sales (cost=0.43..17.20 rows=175 width=18) (actual time=0.009..0.373 rows=138 loops=3862)
Index Cond: (ss_item_sk = item.i_item_sk)
-> Parallel Hash (cost=2049.55..2049.55 rows=18 width=4) (actual time=7.245..7.245 rows=15 loops=2)
Buckets: 1024 Batches: 1 Memory Usage: 40kB
-> Parallel Seq Scan on date_dim (cost=0.00..2049.55 rows=18 width=4) (actual time=5.373..7.183 rows=15 loops=2)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 36510
-> Index Scan using customer_address_pkey on customer_address (cost=0.29..0.31 rows=1 width=4) (actual time=0.005..0.005 rows=0 loops=10851)
Index Cond: (ca_address_sk = store_sales.ss_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
-> Finalize GroupAggregate (cost=32453.97..32458.96 rows=52 width=49) (actual time=389.645..389.890 rows=81 loops=1)
Group Key: item_2.i_item_id
-> Gather Merge (cost=32453.97..32458.07 rows=31 width=49) (actual time=389.639..389.839 rows=82 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=31453.96..31454.58 rows=31 width=49) (actual time=386.201..386.419 rows=302 loops=2)
Group Key: item_2.i_item_id
-> Sort (cost=31453.96..31454.03 rows=31 width=23) (actual time=386.185..386.211 rows=574 loops=2)
Sort Key: item_2.i_item_id
Sort Method: quicksort Memory: 102kB
Worker 0: Sort Method: quicksort Memory: 90kB
-> Nested Loop (cost=3433.98..31453.19 rows=31 width=23) (actual time=8.611..385.536 rows=1209 loops=2)
-> Parallel Hash Join (cost=3433.69..31431.56 rows=69 width=27) (actual time=8.559..371.927 rows=2784 loops=2)
Hash Cond: (catalog_sales.cs_sold_date_sk = date_dim_1.d_date_sk)
-> Nested Loop (cost=1383.92..28938.79 rows=168751 width=31) (actual time=3.845..356.671 rows=134113 loops=2)
-> Parallel Hash Semi Join (cost=1383.49..2781.24 rows=2107 width=21) (actual time=3.798..8.414 rows=1931 loops=2)
Hash Cond: (item_2.i_item_id = item_3.i_item_id)
-> Parallel Seq Scan on item item_2 (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.004..2.278 rows=9000 loops=2)
-> Parallel Hash (cost=1370.35..1370.35 rows=1051 width=17) (actual time=3.739..3.740 rows=893 loops=2)
Buckets: 2048 Batches: 1 Memory Usage: 176kB
-> Parallel Seq Scan on item item_3 (cost=0.00..1370.35 rows=1051 width=17) (actual time=0.024..3.448 rows=893 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 8107
-> Index Scan using catalog_sales_pkey on catalog_sales (cost=0.43..11.53 rows=88 width=18) (actual time=0.007..0.168 rows=69 loops=3862)
Index Cond: (cs_item_sk = item_2.i_item_sk)
-> Parallel Hash (cost=2049.55..2049.55 rows=18 width=4) (actual time=4.146..4.146 rows=15 loops=2)
Buckets: 1024 Batches: 1 Memory Usage: 40kB
-> Parallel Seq Scan on date_dim date_dim_1 (cost=0.00..2049.55 rows=18 width=4) (actual time=2.764..4.105 rows=15 loops=2)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 36510
-> Index Scan using customer_address_pkey on customer_address customer_address_1 (cost=0.29..0.31 rows=1 width=4) (actual time=0.005..0.005 rows=0 loops=5568)
Index Cond: (ca_address_sk = catalog_sales.cs_bill_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
-> Finalize GroupAggregate (cost=19418.92..19421.35 rows=26 width=49) (actual time=153.863..154.080 rows=52 loops=1)
Group Key: item_4.i_item_id
-> Gather Merge (cost=19418.92..19420.91 rows=15 width=49) (actual time=153.858..154.047 rows=53 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=18418.91..18419.21 rows=15 width=49) (actual time=150.236..150.343 rows=174 loops=2)
Group Key: item_4.i_item_id
-> Sort (cost=18418.91..18418.95 rows=15 width=23) (actual time=150.224..150.235 rows=245 loops=2)
Sort Key: item_4.i_item_id
Sort Method: quicksort Memory: 52kB
Worker 0: Sort Method: quicksort Memory: 42kB
-> Nested Loop (cost=3433.98..18418.62 rows=15 width=23) (actual time=8.291..149.887 rows=573 loops=2)
-> Parallel Hash Join (cost=3433.69..18407.53 rows=35 width=27) (actual time=7.812..143.442 rows=1329 loops=2)
Hash Cond: (web_sales.ws_sold_date_sk = date_dim_2.d_date_sk)
-> Nested Loop (cost=1383.92..16136.69 rows=84211 width=31) (actual time=3.658..134.414 rows=66762 loops=2)
-> Parallel Hash Semi Join (cost=1383.49..2781.24 rows=2107 width=21) (actual time=3.614..7.859 rows=1931 loops=2)
Hash Cond: (item_4.i_item_id = item_5.i_item_id)
-> Parallel Seq Scan on item item_4 (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.003..2.403 rows=9000 loops=2)
-> Parallel Hash (cost=1370.35..1370.35 rows=1051 width=17) (actual time=3.559..3.560 rows=893 loops=2)
Buckets: 2048 Batches: 1 Memory Usage: 144kB
-> Parallel Seq Scan on item item_5 (cost=0.00..1370.35 rows=1051 width=17) (actual time=0.022..3.323 rows=893 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 8107
-> Index Scan using web_sales_pkey on web_sales (cost=0.42..5.91 rows=43 width=18) (actual time=0.005..0.060 rows=35 loops=3862)
Index Cond: (ws_item_sk = item_4.i_item_sk)
-> Parallel Hash (cost=2049.55..2049.55 rows=18 width=4) (actual time=3.873..3.873 rows=15 loops=2)
Buckets: 1024 Batches: 1 Memory Usage: 40kB
-> Parallel Seq Scan on date_dim date_dim_2 (cost=0.00..2049.55 rows=18 width=4) (actual time=2.509..3.834 rows=15 loops=2)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 36510
-> Index Scan using customer_address_pkey on customer_address customer_address_2 (cost=0.29..0.32 rows=1 width=4) (actual time=0.005..0.005 rows=0 loops=2658)
Index Cond: (ca_address_sk = web_sales.ws_bill_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
Planning Time: 4.921 ms
Execution Time: 1390.888 ms
(113 rows)

Here, if we apply the following patch:

diff --git a/src/backend/optimizer/path/joinpath.c b/src/backend/optimizer/path/joinpath.c
index 5be8da9e09..02d3b6dfc9 100644
--- a/src/backend/optimizer/path/joinpath.c
+++ b/src/backend/optimizer/path/joinpath.c
@@ -1202,7 +1202,6 @@ try_partial_hashjoin_path(PlannerInfo *root,
*/
initial_cost_hashjoin(root, &workspace, jointype, hashclauses,
outer_path, inner_path, extra, parallel_hash);
- if (!add_partial_path_precheck(joinrel, workspace.total_cost, NIL))
return;

/* Might be good enough to be worth trying, so let's try it. */

The query plan and execution time are much better:

QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Limit (cost=14368.57..67274.24 rows=100 width=49) (actual time=620.122..717.451 rows=100 loops=1)
-> Incremental Sort (cost=14368.57..110127.84 rows=181 width=49) (actual time=620.121..717.442 rows=100 loops=1)
Sort Key: item.i_item_id, (sum((sum(store_sales.ss_ext_sales_price))))
Presorted Key: item.i_item_id
Full-sort Groups: 4 Sort Method: quicksort Average Memory: 27kB Peak Memory: 27kB
-> GroupAggregate (cost=13836.61..110119.69 rows=181 width=49) (actual time=525.705..717.358 rows=101 loops=1)
Group Key: item.i_item_id
-> Merge Append (cost=13836.61..110116.53 rows=181 width=49) (actual time=518.454..717.227 rows=225 loops=1)
Sort Key: item.i_item_id
-> Finalize GroupAggregate (cost=4612.20..53673.79 rows=103 width=49) (actual time=209.830..322.526 rows=94 loops=1)
Group Key: item.i_item_id
-> Gather Merge (cost=4612.20..53672.05 rows=61 width=49) (actual time=206.661..322.418 rows=95 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=3612.19..52665.17 rows=61 width=49) (actual time=26.067..373.585 rows=274 loops=2)
Group Key: item.i_item_id
-> Nested Loop (cost=3612.19..52664.11 rows=61 width=23) (actual time=21.399..373.040 rows=798 loops=2)
-> Nested Loop (cost=3611.90..52621.09 rows=138 width=27) (actual time=21.309..362.794 rows=1882 loops=2)
-> Nested Loop (cost=3611.59..43612.90 rows=337185 width=31) (actual time=19.858..328.844 rows=94367 loops=2)
-> Merge Semi Join (cost=3611.16..3694.10 rows=2107 width=21) (actual time=19.780..20.884 rows=696 loops=2)
Merge Cond: (item.i_item_id = item_1.i_item_id)
-> Sort (cost=2051.70..2078.17 rows=10588 width=21) (actual time=10.082..10.432 rows=3195 loops=2)
Sort Key: item.i_item_id
Sort Method: quicksort Memory: 873kB
Worker 0: Sort Method: quicksort Memory: 407kB
-> Parallel Seq Scan on item (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.021..4.535 rows=9000 loops=2)
-> Sort (cost=1559.47..1563.93 rows=1786 width=17) (actual time=9.690..9.753 rows=950 loops=2)
Sort Key: item_1.i_item_id
Sort Method: quicksort Memory: 49kB
Worker 0: Sort Method: quicksort Memory: 49kB
-> Seq Scan on item item_1 (cost=0.00..1463.00 rows=1786 width=17) (actual time=0.012..8.720 rows=1786 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 16214
-> Index Scan using store_sales_pkey on store_sales (cost=0.43..17.20 rows=175 width=18) (actual time=0.010..0.418 rows=135 loops=1393)
Index Cond: (ss_item_sk = item.i_item_sk)
-> Memoize (cost=0.30..0.33 rows=1 width=4) (actual time=0.000..0.000 rows=0 loops=188734)
Cache Key: store_sales.ss_sold_date_sk
Cache Mode: logical
Hits: 22594 Misses: 1824 Evictions: 0 Overflows: 0 Memory Usage: 123kB
Worker 0: Hits: 162492 Misses: 1824 Evictions: 0 Overflows: 0 Memory Usage: 123kB
-> Index Scan using date_dim_pkey on date_dim (cost=0.29..0.32 rows=1 width=4) (actual time=0.003..0.003 rows=0 loops=3648)
Index Cond: (d_date_sk = store_sales.ss_sold_date_sk)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 1
-> Index Scan using customer_address_pkey on customer_address (cost=0.29..0.31 rows=1 width=4) (actual time=0.005..0.005 rows=0 loops=3763)
Index Cond: (ca_address_sk = store_sales.ss_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
-> Finalize GroupAggregate (cost=4612.19..35681.87 rows=52 width=49) (actual time=182.160..244.227 rows=81 loops=1)
Group Key: item_2.i_item_id
-> Gather Merge (cost=4612.19..35680.99 rows=31 width=49) (actual time=181.280..244.127 rows=82 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=3612.18..34677.49 rows=31 width=49) (actual time=25.641..237.968 rows=228 loops=2)
Group Key: item_2.i_item_id
-> Nested Loop (cost=3612.18..34676.95 rows=31 width=23) (actual time=23.407..237.595 rows=421 loops=2)
-> Nested Loop (cost=3611.89..34655.32 rows=69 width=27) (actual time=21.110..231.354 rows=1001 loops=2)
-> Nested Loop (cost=3611.59..29851.66 rows=168751 width=31) (actual time=20.479..211.999 rows=48619 loops=2)
-> Merge Semi Join (cost=3611.16..3694.10 rows=2107 width=21) (actual time=20.406..21.491 rows=713 loops=2)
Merge Cond: (item_2.i_item_id = item_3.i_item_id)
-> Sort (cost=2051.70..2078.17 rows=10588 width=21) (actual time=10.180..10.521 rows=3232 loops=2)
Sort Key: item_2.i_item_id
Sort Method: quicksort Memory: 869kB
Worker 0: Sort Method: quicksort Memory: 411kB
-> Parallel Seq Scan on item item_2 (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.018..4.695 rows=9000 loops=2)
-> Sort (cost=1559.47..1563.93 rows=1786 width=17) (actual time=10.219..10.283 rows=952 loops=2)
Sort Key: item_3.i_item_id
Sort Method: quicksort Memory: 49kB
Worker 0: Sort Method: quicksort Memory: 49kB
-> Seq Scan on item item_3 (cost=0.00..1463.00 rows=1786 width=17) (actual time=0.016..9.364 rows=1786 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 16214
-> Index Scan using catalog_sales_pkey on catalog_sales (cost=0.43..11.53 rows=88 width=18) (actual time=0.009..0.254 rows=68 loops=1426)
Index Cond: (cs_item_sk = item_2.i_item_sk)
-> Memoize (cost=0.30..0.33 rows=1 width=4) (actual time=0.000..0.000 rows=0 loops=97238)
Cache Key: catalog_sales.cs_sold_date_sk
Cache Mode: logical
Hits: 10090 Misses: 1812 Evictions: 0 Overflows: 0 Memory Usage: 122kB
Worker 0: Hits: 83505 Misses: 1831 Evictions: 0 Overflows: 0 Memory Usage: 123kB
-> Index Scan using date_dim_pkey on date_dim date_dim_1 (cost=0.29..0.32 rows=1 width=4) (actual time=0.002..0.002 rows=0 loops=3643)
Index Cond: (d_date_sk = catalog_sales.cs_sold_date_sk)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 1
-> Index Scan using customer_address_pkey on customer_address customer_address_1 (cost=0.29..0.31 rows=1 width=4) (actual time=0.006..0.006 rows=0 loops=2002)
Index Cond: (ca_address_sk = catalog_sales.cs_bill_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
-> Finalize GroupAggregate (cost=4612.19..20758.50 rows=26 width=49) (actual time=126.461..150.410 rows=52 loops=1)
Group Key: item_4.i_item_id
-> Gather Merge (cost=4612.19..20758.06 rows=15 width=49) (actual time=126.445..150.331 rows=53 loops=1)
Workers Planned: 1
Workers Launched: 1
-> Partial GroupAggregate (cost=3612.18..19756.36 rows=15 width=49) (actual time=20.098..142.150 rows=184 loops=2)
Group Key: item_4.i_item_id
-> Nested Loop (cost=3612.18..19756.10 rows=15 width=23) (actual time=18.958..141.896 rows=258 loops=2)
-> Nested Loop (cost=3611.89..19745.01 rows=35 width=27) (actual time=18.933..138.234 rows=588 loops=2)
-> Nested Loop (cost=3611.59..17049.55 rows=84211 width=31) (actual time=17.422..126.542 rows=28404 loops=2)
-> Merge Semi Join (cost=3611.16..3694.10 rows=2107 width=21) (actual time=17.373..18.498 rows=840 loops=2)
Merge Cond: (item_4.i_item_id = item_5.i_item_id)
-> Sort (cost=2051.70..2078.17 rows=10588 width=21) (actual time=8.744..9.131 rows=3858 loops=2)
Sort Key: item_4.i_item_id
Sort Method: quicksort Memory: 817kB
Worker 0: Sort Method: quicksort Memory: 463kB
-> Parallel Seq Scan on item item_4 (cost=0.00..1343.88 rows=10588 width=21) (actual time=0.019..4.199 rows=9000 loops=2)
-> Sort (cost=1559.47..1563.93 rows=1786 width=17) (actual time=8.624..8.683 rows=951 loops=2)
Sort Key: item_5.i_item_id
Sort Method: quicksort Memory: 49kB
Worker 0: Sort Method: quicksort Memory: 49kB
-> Seq Scan on item item_5 (cost=0.00..1463.00 rows=1786 width=17) (actual time=0.011..7.950 rows=1786 loops=2)
Filter: (i_category = 'Children'::bpchar)
Rows Removed by Filter: 16214
-> Index Scan using web_sales_pkey on web_sales (cost=0.42..5.91 rows=43 width=18) (actual time=0.008..0.122 rows=34 loops=1681)
Index Cond: (ws_item_sk = item_4.i_item_sk)
-> Memoize (cost=0.30..0.33 rows=1 width=4) (actual time=0.000..0.000 rows=0 loops=56807)
Cache Key: web_sales.ws_sold_date_sk
Cache Mode: logical
Hits: 3438 Misses: 1557 Evictions: 0 Overflows: 0 Memory Usage: 105kB
Worker 0: Hits: 49988 Misses: 1824 Evictions: 0 Overflows: 0 Memory Usage: 123kB
-> Index Scan using date_dim_pkey on date_dim date_dim_2 (cost=0.29..0.32 rows=1 width=4) (actual time=0.002..0.002 rows=0 loops=3381)
Index Cond: (d_date_sk = web_sales.ws_sold_date_sk)
Filter: ((d_year = 1999) AND (d_moy = 9))
Rows Removed by Filter: 1
-> Index Scan using customer_address_pkey on customer_address customer_address_2 (cost=0.29..0.32 rows=1 width=4) (actual time=0.006..0.006 rows=0 loops=1176)
Index Cond: (ca_address_sk = web_sales.ws_bill_addr_sk)
Filter: (ca_gmt_offset = '-6'::numeric)
Rows Removed by Filter: 1
Planning Time: 4.561 ms
Execution Time: 718.016 ms
(128 rows)

I think the key difference is that the patch disables the usage of Hash Join, which incurs a worse performance.

I also tried to execute `set enable_hashjoin = off;` and also observed the performance improvement.

Environment:
For the benchmark, I used 1 GB data, and my entire data folder can be downloaded here: https://drive.google.com/file/d/1iK5gfyKudfn2BczpoZbNRY_IAD_rITZu/view?usp=sharing
The connection string is: postgresql://ubuntu:ubuntu(at)127(dot)0(dot)0(dot)1:5432/tpcds"
tpch=# select version();
version
--------------------------------------------------------------------------------------------------
PostgreSQL 17.0 on x86_64-pc-linux-gnu, compiled by gcc (Ubuntu 13.2.0-23ubuntu4) 13.2.0, 64-bit
(1 row)

Best regards,

Jinsheng Ba

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