I have this sql that finds rows with the same request_id and counts number of rows, then counts the number of rows with the given partner_id and request_id. This works fine in sql.
SQL:
INSERT INTO quote
(request_id, partner_id)
SELECT 1, 1
WHERE NOT EXISTS (
SELECT * FROM (
SELECT request_id,
count(*) AS total_quote_count,
sum(case when partner_id = 5582 then 1 else 0 end) AS this_partner_quote_count
FROM quote
WHERE request_id = 50090
GROUP BY request_id) s
WHERE total_quote_count> 2 AND this_partner_quote_count >0
)
I want the same thing typeORM but it seems a bit too complicated for typeORM.
I would appreciate if anyone can translate this SQL into typeorm.
I have 3 tables, Transaction, Transaction_Items and Transaction_History.
Where the Transaction is the parent table, while Transaction_Items and Transaction_History are the children tables, with one to many relationship.
When i try to join those tables together, if i have 2+ Transaction_History records, or 2+ Transaction_Items i get duplicated or triplicated record results.
This is the SQL query im currently using which works, but what worries me that in the future if i have to Join another one-to-many table, it will duplicate the results again.
I found a workaround for this, but i was just wondering if there is a better and cleaner way to do this ?
The results should be a PostgreSQL JSON array which will contain the Transaction_Items and Transaction_History
SELECT
TR.id AS transaction_id,
TR.transaction_number,
TR.status,
TR.status AS status,
to_json(TR_INV.list),
COUNT(TR_INV) item_cnt,
COUNT(THR) tr_cnt,
json_agg(THR)
FROM transaction_transaction AS TR
LEFT JOIN (
SELECT
array_agg(t) list, -- this is a workaround method
t.transaction_id
FROM (
SELECT
TR_INV.transaction_id transaction_id,
IT.id,
IT.stock_number,
CAT.key category_key,
ITP.description description,
ITP.serial_number serial_number,
ITP.color color,
ITP.manufacturer manufacturer,
ITP.inventory_model inventory_model,
ITP.average_cost average_cost,
ITP.location_in_store location_in_store,
ITP.firearm_caliber firearm_caliber,
ITP.federal_firearm_number federal_firearm_number,
ITP.sold_price sold_price
FROM transaction_transaction_item TR_INV
LEFT JOIN inventory_item IT ON IT.id = TR_INV.item_id
LEFT JOIN inventory_itemprofile ITP ON ITP.id = IT.current_profile_id
LEFT JOIN inventory_category CAT ON CAT.id = ITP.category_id
LEFT JOIN inventory_categorytype CAT_T ON CAT_T.id = CAT.category_type_id
) t
GROUP BY t.transaction_id
) TR_INV ON TR_INV.transaction_id = TR.id
LEFT JOIN transaction_transactionhistory THR ON THR.transaction_id = TR.id
AND (THR.audit_code_id = 44 OR THR.audit_code_id = 27 OR THR.audit_code_id = 28)
WHERE TR.store_id = 21
AND TR.transaction_type = 'Pawn_Loan' AND TR.date_made >= '2018-10-08'
GROUP BY TR.id, TR_INV.list
What you want to do can be achieved by not using joins, as shown below.
Because your actual tables have so many columns that I don't know and should not care. I just created the simplest forms of them for demonstration.
CREATE TABLE transactions (
tid serial PRIMARY KEY,
name varchar(40) NOT NULL
);
CREATE TABLE transaction_histories (
hid serial PRIMARY KEY ,
tid integer REFERENCES transactions(tid),
history varchar(40) NOT NULL
);
CREATE TABLE transaction_items (
iid serial PRIMARY KEY ,
tid integer REFERENCES transactions(tid),
item varchar(40) NOT NULL
);
INSERT INTO transactions(tid,name) Values(1, 'transaction');
INSERT INTO transaction_histories(tid, history) Values(1, 'history1');
INSERT INTO transaction_histories(tid, history) Values(1, 'history2');
INSERT INTO transaction_items(tid, item) Values(1, 'item1');
INSERT INTO transaction_items(tid, item) Values(1, 'item2');
select
t.*,
(select count(*) from transaction_histories h where h.tid= t.tid) h_count ,
(select json_agg(h) from transaction_histories h where h.tid= t.tid) h ,
(select count(*) from transaction_items i where i.tid= t.tid) i_count ,
(select json_agg(i) from transaction_items i where i.tid= t.tid) i
from transactions t;
The dataset that I'm looking into has an id for the incident, but a few columns (a_dttm, b_dttm, and c_dttm) have dates and times that appear more than once. I looked into it and found that even though the ids are unique, there are entire rows that look almost identical.
So without having to go through 200 rows of potential identical rows, what can I write in postgres to search for rows that are identical in a_dttm, b_dttm, and c_dttm?
This is what I've been doing to select the identical rows one by one:
SELECT *
FROM data
WHERE a_dttm::timestamp = '2007-01-13 08:29:35'
order by a_dttm desc
I got the timestamp from another query.
I know if these three columns are completely identical, then the rows are for sure duplicates.
Try
select count(*), a_dttm, b_dttm, c_dttm
from data
group by a_ddtm, b_dttm, c_dttm;
This should tell you how many duplicates you have.
This will select all the rows for which (at least one) other row exists, with the same {a_dttm,b_dttm,c_dttm}, but with a different id:
SELECT *
FROM the_table t
WHERE EXISTS (
SELECT*
FROM the_table x
WHERE x.a_dttm = t.a_dttm -- same
AND x.b_dttm = t.b_dttm --same
AND x.c_dttm = t.x_dttm --same
AND x.id <> t.id -- different
);
Similar, but now actually DELETING (some of) theduplicates:
DELETE
FROM the_table t
WHERE EXISTS (
SELECT*
FROM the_table x
WHERE x.a_dttm = t.a_dttm -- same
AND x.b_dttm = t.b_dttm --same
AND x.c_dttm = t.x_dttm --same
AND x.id > t.id -- different (actually: with a higher id)
);
I've browsed around some other posts and managed to make my queries run a bit faster. However, I've come to a loss as to how to further optimize this query. I'm going to be using it on a website where it will execute the query when the page is loaded, but 5.5 seconds is far too long to wait for something that should be a lot more simple. The largest table has around 4,000,000 rows and the other ones are around 400,000 each.
Table Structure
match
id BIGINT PRIMARY KEY,
region TEXT,
matchType TEXT,
matchVersion TEXT
team
matchid BIGINT REFERENCES match(id),
id INTEGER,
PRIMARY KEY(matchid, id),
winner TEXT
champion
id INTEGER PRIMARY KEY,
version TEXT,
name TEXT
item
id INTEGER PRIMARY KEY,
name TEXT
participant
PRIMARY KEY(matchid, id),
id INTEGER NOT NULL,
matchid BIGINT REFERENCES match(id),
championid INTEGER REFERENCES champion(id),
teamid INTEGER,
FOREIGN KEY (matchid, teamid) REFERENCES team(matchid, id),
magicDamageDealtToChampions REAL,
damageDealtToChampions REAL,
item0 TEXT,
item1 TEXT,
item2 TEXT,
item3 TEXT,
item4 TEXT,
item5 TEXT,
highestAchievedSeasonTier TEXT
Query
select champion.name,
sum(case when participant.item0 = '3285' then 1::int8 else 0::int8 end) as it0,
sum(case when participant.item1 = '3285' then 1::int8 else 0::int8 end) as it1,
sum(case when participant.item2 = '3285' then 1::int8 else 0::int8 end) as it2,
sum(case when participant.item3 = '3285' then 1::int8 else 0::int8 end) as it3,
sum(case when participant.item4 = '3285' then 1::int8 else 0::int8 end) as it4,
sum(case when participant.item5 = '3285' then 1::int8 else 0::int8 end) as it5
from participant
left join champion
on champion.id = participant.championid
left join team
on team.matchid = participant.matchid and team.id = participant.teamid
left join match
on match.id = participant.matchid
where (team.winner = 'True' and matchversion = '5.14' and matchtype='RANKED_SOLO_5x5')
group by champion.name;
Output of EXPLAIN ANALYZE: http://explain.depesz.com/s/ZYX
What I've done so far
I've created separate indexes on match.region, participant.championid, and a partial index on team where winner = 'True' (since that is only what I am interested in). Note that enable_seqscan = on since when it's off the query is extremely slow. Essentially, the result I'm trying to get is something like this:
Champion |item0 | item1 | ... | item5
champ_name | num | num1 | ... | num5
...
Since I'm still a beginner with respect to database design, I wouldn't be surprised if there is a flaw in my overall table design. I'm still leaning towards the query being absolutely inefficient, though. I've played with both inner joins and left joins -- there is no significant difference though. Additionally, match needs to be bigint (or something larger than integer, since it's too small).
Database design
I suggest:
CREATE TABLE matchversion (
matchversion_id int PRIMARY KEY
, matchversion text UNIQUE NOT NULL
);
CREATE TABLE matchtype (
matchtype_id int PRIMARY KEY
, matchtype text UNIQUE NOT NULL
);
CREATE TABLE region (
region_id int PRIMARY KEY
, region text NOT NULL
);
CREATE TABLE match (
match_id bigint PRIMARY KEY
, region_id int REFERENCES region
, matchtype_id int REFERENCES matchtype
, matchversion_id int REFERENCES matchversion
);
CREATE TABLE team (
match_id bigint REFERENCES match
, team_id integer -- better name !
, winner boolean -- ?!
, PRIMARY KEY(match_id, team_id)
);
CREATE TABLE champion (
champion_id int PRIMARY KEY
, version text
, name text
);
CREATE TABLE participant (
participant_id serial PRIMARY KEY -- use proper name !
, champion_id int NOT NULL REFERENCES champion
, match_id bigint NOT NULL REFERENCES match -- this FK might be redundant
, team_id int
, magic_damage_dealt_to_champions real
, damage_dealt_to_champions real
, item0 text -- or integer ??
, item1 text
, item2 text
, item3 text
, item4 text
, item5 text
, highest_achieved_season_tier text -- integer ??
, FOREIGN KEY (match_id, team_id) REFERENCES team
);
More normalization in order to get smaller tables and indexes and faster access. Create lookup-tables for matchversion, matchtype and region and only write a small integer ID in match.
Seems like the columns participant.item0 .. item5 and highestAchievedSeasonTier could be integer, but are defined as text?
The column team.winner seems to be boolean, but is defined as text.
I also changed the order of columns to be more efficient. Details:
Calculating and saving space in PostgreSQL
Query
Building on above modifications and for Postgres 9.3:
SELECT c.name, *
FROM (
SELECT p.champion_id
, count(p.item0 = '3285' OR NULL) AS it0
, count(p.item1 = '3285' OR NULL) AS it1
, count(p.item2 = '3285' OR NULL) AS it2
, count(p.item3 = '3285' OR NULL) AS it3
, count(p.item4 = '3285' OR NULL) AS it4
, count(p.item5 = '3285' OR NULL) AS it5
FROM matchversion mv
CROSS JOIN matchtype mt
JOIN match m USING (matchtype_id, matchversion_id)
JOIN team t USING (match_id)
JOIN participant p USING (match_id, team_id)
WHERE mv.matchversion = '5.14'
AND mt.matchtype = 'RANKED_SOLO_5x5'
AND t.winner = 'True' -- should be boolean
GROUP BY p.champion_id
) p
JOIN champion c USING (champion_id); -- probably just JOIN ?
Since champion.name is not defined UNIQUE, it's probably wrong to GROUP BY it. It's also inefficient. Use participant.championid instead (and join to champion later if you need the name in the result).
All instances of LEFT JOIN are pointless, since you have predicates on the left tables anyway and / or use the column in GROUP BY.
Parentheses around AND-ed WHERE conditions are not needed.
In Postgres 9.4 or later you could use the new aggregate FILTER syntax instead. Details and alternatives:
How can I simplify this game statistics query?
Index
The partial index on team you already have should look like this to allow index-only scans:
CREATE INDEX on team (matchid, id) WHERE winner -- boolean
But from what I see, you might just add a winner column to participant and drop the table team completely (unless there is more to it).
Also, that index is not going to help much, because (telling from your query plan) the table has 800k rows, half of which qualify:
rows=399999 ... Filter: (winner = 'True'::text) ... Rows Removed by Filter: 399999
This index on match will help a little more (later) when you have more different matchtypes and matchversions:
CREATE INDEX on match (matchtype_id, matchversion_id, match_id);
Still, while 100k rows qualify out of 400k, the index is only useful for an index only scan. Otherwise, a sequential scan will be faster. An index typically pays for about selecting 5 % of the table or less.
Your main problem is that you are obviously running a test case with hardly realistic data distribution. With more selective predicates, indexes will be used more readily.
Aside
Make sure you have configured basic Postgres settings like random_page_cost or work_mem etc.
enable_seqscan = on goes without saying. This is only turned off for debugging or locally as a desperate measure of last resort.
I'd try using
count(*) filter (where item0 = '3285' ) as it0
for your counts instead of sums.
Also, why are you left joining your last 2 tables, then having a where statement. That defeats the purpose and a regular inner join is faster
select champion.name,
count(*) filter( where participant.item0 = 3285) as it0,
count(*) filter( where participant.item1 = 3285) as it1,
count(*) filter( where participant.item2 = 3285) as it2,
count(*) filter( where participant.item3 = 3285) as it3,
count(*) filter( where participant.item4 = 3285) as it4,
count(*) filter( where participant.item5 = 3285) as it5
from participant
join champion on champion.id = participant.championid
join team on team.matchid = participant.matchid and team.id = participant.teamid
join match on match.id = participant.matchid
where (team.winner = 'True' and matchversion = '5.14' and matchtype='RANKED_SOLO_5x5')
group by champion.name;
I'm looking to pivot my data on location_id having multiple rows instead of one. I have multiple locations and I need an easier way to pivot my data and calculate several totals without having to hard code the location_name every time. Was not sure how to approach this and would appreciate a push in the right direction. DB2 database.
SELECT RowNumber () over (PARTITION BY DIM_LOCATION_ID ORDER BY LOCATION_NAME),
DIM_LOCATION_ID,
LOCATION_NAME,
(
SELECT
SUM(SALES_AMOUNT) AS SALES_SUMMARY
FROM FACT_TABLE
WHERE LOCATION_NAME = 'DENVER'
) AS SALES_SUMMARYDV,
(
SELECT
SUM(SALES_AMOUNT) AS SALES_SUMMARY
FROM FACT_TABLE
WHERE LOCATION_NAME = 'CHICAGO'
) AS SALES_SUMMARYCH,
(
SELECT
SUM(ORDERS) AS SUMMARY_ORDERS
FROM FACT_TABLE
WHERE LOCATION_NAME = 'DENVER'
) AS ORDER_SUMMARYDV,
(
SELECT
SUM(ORDERS) AS SUMMARY_ORDERS
FROM FACT_TABLE
WHERE LOCATION_NAME = 'CHICAGO'
) AS ORDER_SUMMARYCH
FROM FACT_TABLE
GROUP BY DIM_LOCATION_ID, LOCATION_NAME
ORDER BY LOCATION_NAME
DESIRED RESULTS: