Hi Guys,
Next level of query is following:
If this works, I guess 90% of the problem will be solved.
SELECT
COUNT(DISTINCT TARGET_ID)
FROM
S_V_F_PROMOTION_HISTORY_EMAIL PH
INNER JOIN S_V_D_CAMPAIGN_HIERARCHY CH
ON PH.TOUCHPOINT_EXECUTION_ID =
CH.TOUCHPOINT_EXECUTION_ID
WHERE
1=1
AND SEND_DT >= '2014-03-13'
AND SEND_DT <= '2015-03-14'
In this query, I am joining two views which were made earlier with CTEs. I
have replaced the CTE's with subqueries. The view were giving me output in
around 5-10 minutes and now I am getting the same result in around 3-4
seconds.
But when I executed the query written above, I am again stuck. I am
attaching the query plan as well the link.
http://explain.depesz.com/s/REeu
I can see most of the time is spending inside a nested loop and total
costs comes out be cost=338203.81..338203.82.
How to take care of this? I need to run this query in a report so I cannot
create a table like select * from views and then join the table. If I do
that I am getting the answer of whole big query in some 6-7 seconds. But
that is not feasible. A report (Jasper can have only one single (big/small
query).
Let me know if you need any other information.
Thanks a ton!
Vivek
-----Original Message-----
From: Jim Nasby [mailto:[email protected]]
Sent: Tuesday, March 17, 2015 5:36 AM
To: Tomas Vondra; [email protected]; Scott Marlowe; Varadharajan
Mukundan
Cc: [email protected]
Subject: Re: [PERFORM] Performance issues
On 3/16/15 3:59 PM, Tomas Vondra wrote:
> On 16.3.2015 20:43, Jim Nasby wrote:
>> On 3/13/15 7:12 PM, Tomas Vondra wrote:
>>> (4) I suspect many of the relations referenced in the views are not
>>> actually needed in the query, i.e. the join is performed but
>>> then it's just discarded because those columns are not used.
>>> Try to simplify the views as much has possible - remove all the
>>> tables that are not really necessary to run the query. If two
>>> queries need different tables, maybe defining two views is
>>> a better approach.
>>
>> A better alternative with multi-purpose views is to use an outer join
>> instead of an inner join. With an outer join if you ultimately don't
>> refer to any of the columns in a particular table Postgres will
>> remove the table from the query completely.
>
> Really? Because a quick test suggests otherwise:
>
> db=# create table test_a (id int);
> CREATE TABLE
> db=# create table test_b (id int);
> CREATE TABLE
> db=# explain select test_a.* from test_a left join test_b using (id);
> QUERY PLAN
> ----------------------------------------------------------------------
> Merge Left Join (cost=359.57..860.00 rows=32512 width=4)
> Merge Cond: (test_a.id = test_b.id)
> -> Sort (cost=179.78..186.16 rows=2550 width=4)
> Sort Key: test_a.id
> -> Seq Scan on test_a (cost=0.00..35.50 rows=2550 width=4)
> -> Sort (cost=179.78..186.16 rows=2550 width=4)
> Sort Key: test_b.id
> -> Seq Scan on test_b (cost=0.00..35.50 rows=2550 width=4)
> (8 rows)
>
> Also, how would that work with duplicate rows in the referenced table?
Right, I neglected to mention that the omitted table must also be unique
on the join key:
[email protected]=# create table a(a_id serial primary key); CREATE
TABLE [email protected]=# create table b(a_id int); CREATE TABLE
[email protected]=# explain analyze select a.* from a left join b
using(a_id);
QUERY PLAN
--------------------------------------------------------------------------
---------------------------------
Hash Right Join (cost=67.38..137.94 rows=2550 width=4) (actual
time=0.035..0.035 rows=0 loops=1)
Hash Cond: (b.a_id = a.a_id)
-> Seq Scan on b (cost=0.00..35.50 rows=2550 width=4) (never
executed)
-> Hash (cost=35.50..35.50 rows=2550 width=4) (actual
time=0.002..0.002 rows=0 loops=1)
Buckets: 4096 Batches: 1 Memory Usage: 32kB
-> Seq Scan on a (cost=0.00..35.50 rows=2550 width=4) (actual
time=0.001..0.001 rows=0 loops=1)
Planning time: 0.380 ms
Execution time: 0.086 ms
(8 rows)
[email protected]=# alter table b add primary key(a_id); ALTER TABLE
[email protected]=# explain analyze select a.* from a left join b
using(a_id);
QUERY PLAN
--------------------------------------------------------------------------
---------------------
Seq Scan on a (cost=0.00..35.50 rows=2550 width=4) (actual
time=0.001..0.001 rows=0 loops=1)
Planning time: 0.247 ms
Execution time: 0.029 ms
(3 rows)
[email protected]=# alter table a drop constraint a_pkey; ALTER
TABLE [email protected]=# explain analyze select a.* from a left
join b using(a_id);
QUERY PLAN
--------------------------------------------------------------------------
---------------------
Seq Scan on a (cost=0.00..35.50 rows=2550 width=4) (actual
time=0.001..0.001 rows=0 loops=1)
Planning time: 0.098 ms
Execution time: 0.011 ms
(3 rows)
--
Jim Nasby, Data Architect, Blue Treble Consulting Data in Trouble? Get it
in Treble! http://BlueTreble.com
Aggregate (cost=338203.81..338203.82 rows=1 width=8)
-> Nested Loop (cost=51266.84..338203.81 rows=1 width=8)
Join Filter:
(s_f_touchpoint_execution_status_history_1.touchpoint_execution_id =
s_f_touchpoint_execution_status_history.touchpoint_execution_id)
-> Nested Loop (cost=32782.19..317837.83 rows=1 width=32)
-> Nested Loop (cost=32781.90..317837.50 rows=1 width=40)
-> Nested Loop (cost=32781.61..317829.18 rows=1 width=24)
Join Filter: (base.touchpoint_execution_id =
s_f_touchpoint_execution_status_history_1.touchpoint_execution_id)
-> Unique (cost=32781.61..34495.50 rows=1 width=8)
-> Merge Join (cost=32781.61..34495.50
rows=1 width=8)
Merge Cond:
((s_f_touchpoint_execution_status_history_1.touchpoint_execution_id =
s_f_touchpoint_execution_status_history_1_1.tou
chpoint_execution_id) AND
(s_f_touchpoint_execution_status_history_1.creation_dt =
(max(s_f_touchpoint_execution_status_history_1_1.creation_dt))))
-> Sort (cost=19697.87..20098.14
rows=160107 width=16)
Sort Key:
s_f_touchpoint_execution_status_history_1.touchpoint_execution_id,
s_f_touchpoint_execution_status_history_1.crea
tion_dt
-> Seq Scan on
s_f_touchpoint_execution_status_history
s_f_touchpoint_execution_status_history_1 (cost=0.00..5857.68 rows
=160107 width=16)
Filter:
(touchpoint_execution_status_type_id = ANY ('{3,4,6}'::integer[]))
-> Sort (cost=13083.74..13254.76
rows=68410 width=16)
Sort Key:
s_f_touchpoint_execution_status_history_1_1.touchpoint_execution_id,
(max(s_f_touchpoint_execution_status_history
_1_1.creation_dt))
-> HashAggregate
(cost=6221.56..6905.66 rows=68410 width=16)
-> Seq Scan on
s_f_touchpoint_execution_status_history
s_f_touchpoint_execution_status_history_1_1 (cost=0.00..4766
.04 rows=291104 width=16)
-> Seq Scan on s_f_promotion_history base
(cost=0.00..283333.66 rows=1 width=32)
Filter: ((send_dt >= '2014-03-13
00:00:00'::timestamp without time zone) AND (send_dt <= '2015-03-14
00:00:00'::timestamp without time
zone))
-> Index Scan using idx_s_d_touchpoint_execution_id on
s_d_touchpoint_execution tpe (cost=0.29..8.31 rows=1 width=16)
Index Cond: (touchpoint_execution_id =
s_f_touchpoint_execution_status_history_1.touchpoint_execution_id)
-> Index Only Scan using s_d_touchpoint_pkey on s_d_touchpoint
tp (cost=0.29..0.32 rows=1 width=8)
Index Cond: ((touchpoint_id = tpe.touchpoint_id) AND
(channel_type_id = 1))
-> Nested Loop (cost=18484.65..20365.97 rows=1 width=24)
-> Hash Join (cost=18484.52..20365.80 rows=1 width=26)
Hash Cond:
((s_f_touchpoint_execution_status_history_1_2.touchpoint_execution_id =
s_f_touchpoint_execution_status_history.touchpoint_execution_id)
AND ((max(s_f_touchpoint_execution_status_history_1_2.creation_dt)) =
s_f_touchpoint_execution_status_history.creation_dt))
-> HashAggregate (cost=6221.56..6905.66 rows=68410
width=16)
-> Seq Scan on
s_f_touchpoint_execution_status_history
s_f_touchpoint_execution_status_history_1_2 (cost=0.00..4766.04 rows=291104
width=16
)
-> Hash (cost=12262.93..12262.93 rows=2 width=26)
-> Hash Join (cost=6259.57..12262.93 rows=2
width=26)
Hash Cond:
(s_f_touchpoint_execution_status_history.touchpoint_execution_id =
tp_exec.touchpoint_execution_id)
-> Seq Scan on
s_f_touchpoint_execution_status_history (cost=0.00..5493.80 rows=135878
width=16)
Filter:
(touchpoint_execution_status_type_id = ANY ('{3,4}'::integer[]))
-> Hash (cost=6259.55..6259.55 rows=1
width=10)
-> Nested Loop Left Join
(cost=1955.27..6259.55 rows=1 width=10)
-> Nested Loop
(cost=1954.99..6259.24 rows=1 width=18)
-> Nested Loop
(cost=1954.71..6258.92 rows=1 width=34)
Join Filter:
(camp_exec.campaign_id = wave.campaign_id)
-> Nested Loop
(cost=1954.42..6254.67 rows=13 width=42)
-> Hash Join
(cost=1954.13..6249.67 rows=13 width=42)
Hash Cond:
((tp_exec.touchpoint_id = tp_1.touchpoint_id) AND (wave_exec.wave_id =
tp_1.wave_id))
-> Hash
Join (cost=1576.83..4595.51 rows=72956 width=40)
Hash
Cond: (tp_exec.wave_execution_id = wave_exec.wave_execution_id)
->
Seq Scan on s_d_touchpoint_execution tp_exec (cost=0.00..1559.56 rows=72956
width=34)
->
Hash (cost=1001.37..1001.37 rows=46037 width=24)
-> Seq Scan on s_d_wave_execution wave_exec (cost=0.00..1001.37 rows=46037
width=24)
-> Hash
(cost=212.72..212.72 rows=10972 width=18)
->
Seq Scan on s_d_touchpoint tp_1 (cost=0.00..212.72 rows=10972 width=18)
-> Index Scan
using s_d_campaign_execution_idx on s_d_campaign_execution camp_exec
(cost=0.29..0.37 row
s=1 width=18)
Index
Cond: (campaign_execution_id = wave_exec.campaign_execution_id)
-> Index Scan using
s_d_wave_pkey on s_d_wave wave (cost=0.29..0.31 rows=1 width=16)
Index Cond:
(wave_id = wave_exec.wave_id)
-> Index Scan using
s_d_campaign_pkey on s_d_campaign camp (cost=0.29..0.32 rows=1 width=10)
Index Cond:
(campaign_id = camp_exec.campaign_id)
-> Index Only Scan using
s_d_content_pkey on s_d_content content (cost=0.28..0.30 rows=1 width=8)
Index Cond: (content_id =
tp_exec.content_id)
-> Index Only Scan using d_channel_pk on s_d_channel_type
channel (cost=0.13..0.15 rows=1 width=2)
Index Cond: (channel_type_id = tp_1.channel_type_id)
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