Matching data from two tables with unequal number of rows - sql

I have 2 tables, and I am trying to match up the data but all the answers have suggested right joins or full joins, which are not available on SQLite.
Table 1:
╔═════════╦═════════╦══════╗
║ Company ║ Product ║ Cost ║
╠═════════╬═════════╬══════╣
║ A ║ abc ║ 100 ║
║ B ║ abc ║ 150 ║
║ F ║ xyz ║ 250 ║
║ G ║ xyz ║ 300 ║
╚═════════╩═════════╩══════╝
However I have a list of more companies (with same products)
Table 2:
╔═════════╦═════════╗
║ Product ║ Company ║
╠═════════╬═════════╣
║ abc ║ A ║
║ abc ║ B ║
║ abc ║ C ║
║ abc ║ D ║
║ abc ║ E ║
║ abc ║ F ║
║ abc ║ G ║
║ xyz ║ A ║
║ xyz ║ B ║
║ xyz ║ C ║
║ xyz ║ D ║
║ xyz ║ E ║
║ xyz ║ F ║
║ xyz ║ G ║
╚═════════╩═════════╝
How do I match them up so they look like this?
Table 3:
╔═════════╦═════════╦══════╗
║ Product ║ Company ║ Cost ║
╠═════════╬═════════╬══════╣
║ abc ║ A ║ 100 ║
║ abc ║ B ║ 150 ║
║ abc ║ C ║ null ║
║ abc ║ D ║ null ║
║ abc ║ E ║ null ║
║ abc ║ F ║ null ║
║ abc ║ G ║ null ║
║ xyz ║ A ║ null ║
║ xyz ║ B ║ null ║
║ xyz ║ C ║ null ║
║ xyz ║ D ║ null ║
║ xyz ║ E ║ null ║
║ xyz ║ F ║ 250 ║
║ xyz ║ G ║ 300 ║
╚═════════╩═════════╩══════╝
When I use this code,
SELECT Company, t.Product, Cost
FROM table1 as t INNER JOIN table2 as f ON t.product = f.product
WHERE t.company = f.company
it only returns [Company] with an associated [Product] and [Cost], but does not return [Company] with null values.
When I use
SELECT Company, t.Product, Cost
FROM table1 as t INNER JOIN table2 as f ON t.company = f.company
then my output looks like
╔═══════════╦═══════════╦═════════╗
║ t.Company ║ f.Company ║ Product ║
╠═══════════╬═══════════╬═════════╣
║ A ║ A ║ abc ║
║ B ║ A ║ abc ║
║ F ║ A ║ abc ║
║ G ║ A ║ abc ║
║ A ║ B ║ abc ║
║ B ║ B ║ abc ║
║ F ║ B ║ abc ║
║ G ║ B ║ abc ║
║ A ║ C ║ abc ║
║ B ║ C ║ abc ║
║ F ║ C ║ abc ║
║ G ║ C ║ abc ║
╚═══════════╩═══════════╩═════════╝
Any help will be much appreciated. Thank you!

SQLite does support LEFT OUTER JOIN, which should do the job just fine:
select two.product, two.company, one.cost from two
left outer join one on
((one.company = two.company) and (one.product = two.product));
(where two is your "table 2" and one is your "table 1")
Running this in SQLite with the above data:
abc|A|100
abc|B|150
abc|C|
abc|D|
abc|E|
abc|F|
abc|G|
xyz|A|
xyz|B|
xyz|C|
xyz|D|
xyz|E|
xyz|F|250
xyz|G|300

Related

Multiple Full Joins On a User Table in a Single Query

I have a schema of a database where we have a a user table and several fact tables.
The schema is goes something like this:
╔════════════╗
║ USERS ║
╠════════════╣
║ sales_id ║ PK
║ traveler_id║ PK
╚════════════╝
╔════════════╗
║ Sales ║
╠════════════╣
║ sales_id ║ --> FK to User sales_id PK
║ date ║
║ metric_1 ║
║ metric_2 ║
╚════════════╝
╔════════════╗
║ Travels ║
╠════════════╣
║ traveler_id║ --> FK to User traveler_id PK
║ date ║
║ metric_3 ║
║ metric_4 ║
╚════════════╝
I'm trying to join them as a single fact table to get all the metrics and their dates in a inqle query. It would return something like below:
╔════════════╦══════════════╦══════════╦══════════╦══════════╦══════════╦══════════╗
║ sales_id ║ traveler_id ║ date ║ metric_1 ║ metric_2 ║ metric_3 ║ metric_4 ║
╠════════════╬══════════════╬══════════╬══════════╬══════════╬══════════╬══════════╣
║ 1 ║ A ║ Jan ║ x1 ║ a1 ║ null ║ null ║
║ 1 ║ A ║ Feb ║ x2 ║ a2 ║ null ║ null ║
║ 1 ║ A ║ Jan ║ x3 ║ a3 ║ b1 ║ null ║
║ 2 ║ B ║ Mar ║ x4 ║ a4 ║ null ║ c1 ║
╚════════════╩══════════════╩══════════╩══════════╩══════════╩══════════╩══════════╝
Here is the sql I wrote:
select
coalesce(s.date, t.date) as date,
s.sales_id,
t.traveler_id,
max(coalesce(s.metric_1, 0)) as metric_1,
max(coalesce(s.metric_2, 0)) as metric_2,
max(coalesce(t.metric_3, 0)) as metric_3,
max(coalesce(t.metric_4, 0)) as metric_4,
from users as u
full join sales as s on (u.sales_id = s.sales_id)
full join travels as t on (u.traveler_id = t.traveler_id)
group by 1, 2, 3
However, my joining logic ends up returning the same values for the rows that are as null.
╔════════════╦══════════════╦══════════╦══════════╦══════════╦══════════╦══════════╗
║ sales_id ║ traveler_id ║ date ║ metric_1 ║ metric_2 ║ metric_3 ║ metric_4 ║
╠════════════╬══════════════╬══════════╬══════════╬══════════╬══════════╬══════════╣
║ 1 ║ A ║ Jan ║ x1 ║ a1 ║ b1 ║ null ║
║ 1 ║ A ║ Feb ║ x2 ║ a2 ║ b1 ║ null ║
║ 1 ║ A ║ Jan ║ x3 ║ a3 ║ b1 ║ null ║
║ 2 ║ B ║ Mar ║ x4 ║ a4 ║ null ║ null ║
╚════════════╩══════════════╩══════════╩══════════╩══════════╩══════════╩══════════╝
My suspicion is that it's due to the second full join. I would assume I could break these down into two other joins, and match their id, but I'm having a hard time trying to understand how to incorporate the dates, properly.
select
coalesce(s.date, t.date) as date,
s.sales_id,
t.traveler_id,
max(coalesce(s.metric_1, 0)) as metric_1,
max(coalesce(s.metric_2, 0)) as metric_2,
max(coalesce(t.metric_3, 0)) as metric_3,
max(coalesce(t.metric_4, 0)) as metric_4
from users as u
left join sales as s on (u.sales_id = s.sales_id and s.date = coalesce(s.date, t.date))
left join travels as t on (u.traveler_id = t.traveler_id and t.date = coalesce(s.date, t.date))
group by 1, 2, 3
This is what the table looks like:
╔════════════╦══════════════╦══════════╦══════════╦══════════╦══════════╦══════════╗
║ sales_id ║ traveler_id ║ date ║ metric_1 ║ metric_2 ║ metric_3 ║ metric_4 ║
╠════════════╬══════════════╬══════════╬══════════╬══════════╬══════════╬══════════╣
║ 1 ║ A ║ Jan ║ x1 ║ a1 ║ b1 ║ null ║
║ 1 ║ A ║ Feb ║ x2 ║ a2 ║ null ║ null ║
║ 1 ║ A ║ Jan ║ x3 ║ a3 ║ b1 ║ null ║
║ 2 ║ B ║ Mar ║ x4 ║ a4 ║ null ║ c1 ║
╚════════════╩══════════════╩══════════╩══════════╩══════════╩══════════╩══════════╝

SQL Unions and aggregation with multiple tables

I have three tables as below.
Table 1:
╔═════════════════════╗
║ Country_table ║
╠══════════════╦══════╣
║ Country_Name ║ Code ║
╠══════════════╬══════╣
║ India ║ 1 ║
╠══════════════╬══════╣
║ UK ║ 2 ║
╠══════════════╬══════╣
║ france ║ 3 ║
╠══════════════╬══════╣
║ germany ║ 4 ║
╚══════════════╩══════╝
Table 2 :
╔════════════════════════════════════════════════════════════════════════════════╗
║ Trade_Details ║
╠═════════╦═══════════╦═════════════╦═══════════╦══════════╦════════╦════════════╣
║ TradeID ║ ProductID ║ FromCountry ║ ToCountry ║ Curruncy ║ Amount ║ Date ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T1 ║ P1 ║ 1 ║ 3 ║ INR ║ 10 ║ 01/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T2 ║ P2 ║ 3 ║ 2 ║ USD ║ 11 ║ 10/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T3 ║ P1 ║ 1 ║ 4 ║ GBP ║ 12 ║ 20/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T4 ║ P2 ║ 2 ║ 3 ║ INR ║ 13 ║ 21/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T5 ║ P1 ║ 1 ║ 4 ║ USD ║ 14 ║ 22/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T6 ║ P2 ║ 4 ║ 2 ║ GBP ║ 15 ║ 23/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T7 ║ P1 ║ 3 ║ 1 ║ INR ║ 16 ║ 24/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T8 ║ P2 ║ 3 ║ 1 ║ USD ║ 17 ║ 25/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T9 ║ P1 ║ 2 ║ 3 ║ GBP ║ 18 ║ 26/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T10 ║ P2 ║ 1 ║ 4 ║ INR ║ 19 ║ 27/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T11 ║ P1 ║ 3 ║ 1 ║ USD ║ 20 ║ 28/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T12 ║ P2 ║ 1 ║ 1 ║ GBP ║ 21 ║ 29/01/2020 ║
╠═════════╬═══════════╬═════════════╬═══════════╬══════════╬════════╬════════════╣
║ T13 ║ P1 ║ 2 ║ 2 ║ INR ║ 22 ║ 30/01/2020 ║
╚═════════╩═══════════╩═════════════╩═══════════╩══════════╩════════╩════════════╝
Table 3:
╔═══════════════════════════════════════════════════════╗
║ TradeStatus_Table ║
╠═════════╦════════════╦═══════════════════╦════════════╣
║ TradeID ║ StatusCode ║ StatusDescription ║ Date ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T1 ║ inProcess ║ Reached HUB1 ║ 01/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T1 ║ inProcess ║ Reached HUB2 ║ 01/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T1 ║ inProcess ║ Reached HUB3 ║ 01/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T1 ║ delivered ║ delivered ║ 01/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T2 ║ inProcess ║ Reached HUB1 ║ 10/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T2 ║ inProcess ║ Reached HUB2 ║ 10/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T2 ║ inProcess ║ Reached HUB3 ║ 10/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T2 ║ Returned ║ returned to home ║ 10/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T3 ║ inProcess ║ Reached HUB1 ║ 20/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T3 ║ inProcess ║ Reached HUB2 ║ 20/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T3 ║ inProcess ║ Reached HUB3 ║ 20/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T3 ║ inProcess ║ Reached HUB4 ║ 20/01/2020 ║
╠═════════╬════════════╬═══════════════════╬════════════╣
║ T3 ║ inProcess ║ Reached HUB5 ║ 20/01/2020 ║
╚═════════╩════════════╩═══════════════════╩════════════╝
Output tables :
Delivered : This column represents the total number of transactions final status as either delivered or returned.
InProcess : This column represents the total number of transactions doesn't contains final status as either delivered or returned.
╔═════════════════════════════════════════════════════════════════════╗
║ Report 1 (example) ║
╠═════════════╦═══════════╦═══════════╦═══════════╦═══════════════════╣
║ FromCountry ║ ToCountry ║ Delivered ║ inProcess ║ Description ║
╠═════════════╬═══════════╬═══════════╬═══════════╬═══════════════════╣
║ India ║ UK ║ 1 ║ 1 ║ total transactions║
╠═════════════╬═══════════╬═══════════╬═══════════╬═══════════════════╣
║ UK ║ India ║ 2 ║ 1 ║ total transactions║
╠═════════════╬═══════════╬═══════════╬═══════════╬═══════════════════╣
║ France ║ India ║ 2 ║ 1 ║ total transactions║
╚═════════════╩═══════════╩═══════════╩═══════════╩═══════════════════╝
No of Trades : This column contains Total number of transactions were made between from country and to country.
Total Trade Value :- This column contains Total sum of value of the transactions made between from country and to country based on currency type.
╔═══════════════════════════════════════════════════════════════════════╗
║ Report 2 (example) ║
╠═════════════╦═══════════╦══════════════╦══════════╦═══════════════════╣
║ FromCountry ║ ToCountry ║ No of Trades ║ Currency ║ Total Trade Value ║
╠═════════════╬═══════════╬══════════════╬══════════╬═══════════════════╣
║ India ║ UK ║ 2 ║ INR ║ 1000 ║
╠═════════════╬═══════════╬══════════════╬══════════╬═══════════════════╣
║ India ║ UK ║ 1 ║ USD ║ 10 ║
╠═════════════╬═══════════╬══════════════╬══════════╬═══════════════════╣
║ UK ║ India ║ 2 ║ GBP ║ 10 ║
╠═════════════╬═══════════╬══════════════╬══════════╬═══════════════════╣
║ France ║ India ║ 1 ║ INR ║ 20 ║
╚═════════════╩═══════════╩══════════════╩══════════╩═══════════════════╝
I have tried many combinations but not able to figure out the required output. Please help me on this.
first query, But not able to accommodate inprocess message count.
select source.Country_name, destination.country_name, count(*)
from Trade_Details, Country_table source, Country_table destination
where date > '2020/01/01 00:00:00'
and date <'2020/02/01/ 00:00:00'
and FromCountry = source.code
and ToCountry =destination.code
group by source.Country_name, destination.country_name
2nd query ,
select source.Country_name as source, destination.country_name as destination, count(*) as inprocessCount
from Trade_Details a1, Country_table source, Country_table destination
where date > '2020/01/01 00:00:00'
and date <'2020/02/01/ 00:00:00'
and FromCountry = source.code
and ToCountry =destination.code
and 0=(select count(*) from Trade_Details a2 where (a2.StatusCode='delivered' or a2.StatusCode='Returned') and a1.TradeID=a2.TradeID)
group by source.Country_name, destination.country_name
QUESTION :
Basically i would like merge both query outputs to get it in a single query. But failed to achive. If possible could you please help me on these two reports.
regards,
Ks
Report 1 Solution Here !!!
SELECT source.country_name AS FromCountry,
destination.country_name AS ToCountry,
Count(Trdsts1.statuscode) AS Delivered,
Count(Trdsts2.statuscode) AS inProcess,
'no of messages' AS Description
FROM trade_details Trddts,
tradestatus_table Trdsts1,
tradestatus_table Trdsts2,
country_table source,
country_table destination
WHERE Trddts.date > '2020/01/01 00:00:00'
AND Trddts.date < '2020/02/01/ 00:00:00'
AND Trdsts1.tradeid = Trddts.tradeid
AND Trdsts2.tradeid = Trddts.tradeid
AND Trdsts1.statuscode = "delivered"
AND Trdsts2.statuscode ="inprocess"
AND source.code = Trddts.fromcountry
AND destination.code = Trddts.tocountry

Sorting and Number results based in order, using multiple columns "order by" criteria

all
I've been trying to do, with data following the structure:
╔══════════════╦═══════════╦══╗
║ Alphabetical ║ Numerical ║ ║
╠══════════════╬═══════════╬══╣
║ A ║ 15 ║ ║
║ A ║ 30 ║ ║
║ E ║ 100 ║ ║
║ C ║ 45 ║ ║
║ F ║ 25 ║ ║
║ C ║ 65 ║ ║
║ B ║ 25 ║ ║
║ F ║ 35 ║ ║
║ C ║ 100 ║ ║
║ A ║ 10 ║ ║
║ C ║ 20 ║ ║
║ B ║ 5 ║ ║
║ E ║ 10 ║ ║
║ F ║ 85 ║ ║
║ D ║ 30 ║ ║
║ F ║ 1 ║ ║
╚══════════════╩═══════════╩══╝
To get the following:
╔══════════════╦══════╦═════════╗
║ Alphabetical ║ Rank ║ Numeric ║
╠══════════════╬══════╬═════════╣
║ A ║ 1 ║ 30 ║
║ A ║ 2 ║ 15 ║
║ A ║ 3 ║ 10 ║
║ B ║ 1 ║ 25 ║
║ B ║ 2 ║ 5 ║
║ C ║ 1 ║ 100 ║
║ C ║ 2 ║ 65 ║
║ C ║ 3 ║ 45 ║
║ C ║ 4 ║ 20 ║
║ D ║ 1 ║ 30 ║
║ E ║ 1 ║ 100 ║
║ E ║ 2 ║ 10 ║
║ F ║ 1 ║ 85 ║
║ F ║ 2 ║ 35 ║
║ F ║ 3 ║ 25 ║
║ F ║ 4 ║ 1 ║
╚══════════════╩══════╩═════════╝
Basically, to order the alphabetical field in ascending order, the numerical field in descending order and get the order or rank by using the order used for the numerical field, grouped by the alphabetical field.
I have only achieved it if I limit it to one specific value in the Alphabetical column, by using something like:
select ordered_src.*, ROWNUM Rank from (select src.* from Source src where alphabetical = 'A' order by Numeric desc) ordered_src;
But I have no idea how to get the result shown above. Any idea? Also, is there any alternative that will work also in mysql/mssql/etc?
Thanks!
Use row_number():
select s.*,
row_number() over (partition by alphabetical order by numerical desc) as rank
from source s
order by alphabetical, rank;

Best way to select calculated values from another table in the same query postgres?

Hi i have postgres database and four tables
vehicles -> trips
vehicles -> component_values -> component_types
vehicles:
╔════╦══════════════════════════╦════════════════════════╦════════════════╦═════════╗
║ id ║ slug ║ name ║ manufacturer ║ model ║
╠════╬══════════════════════════╬════════════════════════╬════════════════╬═════════╣
║ 1 ║ manufacturer-x-model-3 ║ Manufacturer X Model 3 ║ Manufacturer X ║ Model 3 ║
║ 2 ║ manufacturer-x-model-1 ║ Manufacturer X Model 1 ║ Manufacturer X ║ Model 1 ║
║ 3 ║ manufacturer-x-model-1-1 ║ Manufacturer X Model 1 ║ Manufacturer X ║ Model 1 ║
╚════╩══════════════════════════╩════════════════════════╩════════════════╩═════════╝
trips:
╔═════╦════════════╦═════════════╦═════════════╦═════════════════╗
║ id ║ vehicle_id ║ name ║ mileage ║ recorded_at ║
╠═════╬════════════╬═════════════╬═════════════╬═════════════════╣
║ 1 ║ 1 ║ 10386735 ║ 386734.997 ║ 2/25/2014 13:56 ║
║ 2 ║ 1 ║ 11771530.14 ║ 771530.14 ║ 3/1/2014 19:41 ║
║ 3 ║ 1 ║ 121112028.4 ║ 1112028.39 ║ 3/5/2014 3:23 ║
║ 4 ║ 1 ║ 131406814.9 ║ 1406814.892 ║ 3/8/2014 20:56 ║
║ 5 ║ 1 ║ 141933528.7 ║ 1933528.711 ║ 3/13/2014 0:19 ║
║ 6 ║ 1 ║ 152256488.6 ║ 2256488.579 ║ 3/16/2014 21:15 ║
╚═════╩════════════╩═════════════╩═════════════╩═════════════════╝
component_values:
╔════╦═══════════════════╦═════════╦════════════╦════════════╦═════════════╦═════════════╗
║ id ║ component_type_id ║ trip_id ║ vehicle_id ║ mileage ║ damage ║ damage_eff ║
╠════╬═══════════════════╬═════════╬════════════╬════════════╬═════════════╬═════════════╣
║ 1 ║ 1 ║ 1 ║ 1 ║ 386734.997 ║ 0.002260565 ║ 0.002225831 ║
║ 2 ║ 2 ║ 1 ║ 1 ║ 386734.997 ║ 0.002260306 ║ 0.002238006 ║
║ 3 ║ 3 ║ 1 ║ 1 ║ 386734.997 ║ 0.002261288 ║ 0.002266295 ║
║ 4 ║ 4 ║ 1 ║ 1 ║ 386734.997 ║ 0.002054489 ║ 0.002060029 ║
║ 5 ║ 5 ║ 1 ║ 1 ║ 386734.997 ║ 0.002052669 ║ 0.002107272 ║
║ 6 ║ 6 ║ 1 ║ 1 ║ 386734.997 ║ NULL ║ NULL ║
║ 7 ║ 7 ║ 1 ║ 1 ║ 386734.997 ║ NULL ║ NULL ║
║ 8 ║ 1 ║ 2 ║ 1 ║ 771530.14 ║ 0.004792952 ║ 0.0048514 ║
║ 9 ║ 2 ║ 2 ║ 1 ║ 771530.14 ║ 0.004792404 ║ 0.004710451 ║
║ 10 ║ 3 ║ 2 ║ 1 ║ 771530.14 ║ 0.004794486 ║ 0.004805461 ║
╚════╩═══════════════════╩═════════╩════════════╩════════════╩═════════════╩═════════════╝
component_types:
╔════╦═════════════════════════════════════╦════════════════╦══════════════════════╗
║ id ║ slug ║ manufacturer ║ name ║
╠════╬═════════════════════════════════════╬════════════════╬══════════════════════╣
║ 6 ║ manufacturer-d-battery ║ Manufacturer D ║ Battery ║
║ 2 ║ manufacturer-b-differential-1 ║ Manufacturer B ║ Differential 1 ║
║ 3 ║ manufacturer-c-driveshaft-1 ║ Manufacturer C ║ Driveshaft 1 ║
║ 5 ║ manufacturer-c-gearbox-output-shaft ║ Manufacturer C ║ Gearbox output shaft ║
║ 1 ║ manufacturer-a-motor-1 ║ Manufacturer A ║ Motor 1 ║
║ 4 ║ manufacturer-c-gearbox-input-shaft ║ Manufacturer C ║ Gearbox input shaft ║
║ 7 ║ usage-profile ║ ║ Usage profile ║
╚════╩═════════════════════════════════════╩════════════════╩══════════════════════╝
and i'm trying to get the vehicles with the latest trip dates and mileage and also the heights and lowest damaged component for each vehicle
so the result will be like:
╔════════════╦══════════════════╦══════════════════════════╦═════════════════════════════════╦════════════════════════════════╦════════════════════════════════╦═══════════════════════════════╗
║ vehicle_id ║ latest_trip_date ║ latest_trip_date_mileage ║ heights_damaged_component_value ║ heights_damaged_component_name ║ lowest_damaged_component_value ║ lowest_damaged_component_name ║
╠════════════╬══════════════════╬══════════════════════════╬═════════════════════════════════╬════════════════════════════════╬════════════════════════════════╬═══════════════════════════════╣
║ 1 ║ 4/19/2014 3:27 ║ 4844305.912 ║ 0.029372972 ║ Gearbox input shaft ║ 0.002052669 ║ Gearbox output shaft ║
║ 2 ║ 5/19/2014 16:13 ║ 5567945.164 ║ 0.029405924 ║ Driveshaft 1 ║ 0.001864137 ║ Gearbox output shaft ║
║ 3 ║ 4/28/2014 12:55 ║ 5286175.763 ║ 0.030745029 ║ Driveshaft 1 ║ 0.001957685 ║ Differential 1 ║
║ 4 ║ 2/25/2014 3:32 ║ 5398006.007 ║ 0.030495792 ║ Driveshaft 1 ║ 0.001814434 ║ Differential 1 ║
║ 5 ║ 4/25/2014 9:51 ║ 5179558.475 ║ 0.032060074 ║ Gearbox input shaft ║ 0.001936431 ║ Differential 1 ║
║ 6 ║ 5/9/2014 7:43 ║ 5234355.804 ║ 0.030576454 ║ Gearbox input shaft ║ 0.002254191 ║ Gearbox output shaft ║
║ 7 ║ 6/21/2014 18:09 ║ 5705722.416 ║ 0.029957374 ║ Driveshaft 1 ║ 0.001653441 ║ Gearbox output shaft ║
║ 8 ║ 4/23/2014 5:25 ║ 5590470.028 ║ 0.031900163 ║ Driveshaft 1 ║ 0.002083733 ║ Gearbox output shaft ║
║ 9 ║ 3/28/2014 20:37 ║ 5598159.883 ║ 0.031208918 ║ Driveshaft 1 ║ 0.00162805 ║ Differential 1 ║
║ 10 ║ 5/24/2014 19:27 ║ 5020795.001 ║ 0.02962505 ║ Gearbox input shaft ║ 0.001729646 ║ Differential 1 ║
╚════════════╩══════════════════╩══════════════════════════╩═════════════════════════════════╩════════════════════════════════╩════════════════════════════════╩═══════════════════════════════╝
i already tried this query
select
vehicles.id as vehicle_id,
latest_trips.recorded_at as latest_trip_date,
latest_trips.mileage as latest_trip_date_mileage,
heights_damaged_components.damage as heights_damaged_component_value,
heights_damaged_components.name as heights_damaged_component_name,
lowest_damaged_components.damage as lowest_damaged_component_value,
lowest_damaged_components.name as lowest_damaged_component_name
from vehicles
left join (
SELECT t.id, t.vehicle_id, t.mileage, t.recorded_at
FROM public.trips t
inner JOIN (SELECT vehicle_id, MAX(recorded_at) as latest_trip_date FROM public.trips GROUP BY vehicle_id)
tm ON t.vehicle_id = tm.vehicle_id AND t.recorded_at = tm.latest_trip_date
)
as latest_trips on latest_trips.vehicle_id = vehicles.id
left join (
select ct.name, hd.component_type_id, hd.vehicle_id, hd.damage
from public.component_values as hd
INNER JOIN (
SELECT vehicle_id,
MAX(damage) as heights_damaged_component
FROM public.component_values
GROUP BY vehicle_id
)
hdm ON hd.vehicle_id = hdm.vehicle_id AND hd.damage = hdm.heights_damaged_component
join public.component_types as ct on ct.id = hd.component_type_id
)
as heights_damaged_components on heights_damaged_components.vehicle_id = vehicles.id
left join (
select ct.name, ld.component_type_id, ld.vehicle_id, ld.damage
from public.component_values as ld
INNER JOIN (
SELECT vehicle_id, MIN(damage) as lowest_damaged_component
FROM public.component_values
GROUP BY vehicle_id
)
ldm ON ld.vehicle_id = ldm.vehicle_id AND ld.damage = ldm.lowest_damaged_component
join public.component_types as ct on ct.id = ld.component_type_id
) as lowest_damaged_components on lowest_damaged_components.vehicle_id = vehicles.id
but i have like 10000 vehicles and big trips and component_values and this query give me the result in like 3 to 6 seconds, is their a batter way to do this with better performance and time?
can i use GENERATED columns in my case and how ?
please any help and many many thanks in advance.

Combination of SQL Rows

I have a table with following structure and i want to populate data to another table from this table with table structure table2
Table1
╔═══════╦═══════╗
║ State ║ City ║
╠═══════╬═══════╣
║ A ║ AA ║
║ B ║ BB ║
║ C ║ CC ║
║ D ║ DD ║
╚═══════╩═══════╝
Table2
╔═══════════╦═════════╦═══════════╦═══════════╗
║ SrcState1 ║ SrcCity ║ DestState ║ DestCity ║
╠═══════════╬═════════╬═══════════╬═══════════╣
║ A ║ AA ║ B ║ BB ║
║ A ║ AA ║ C ║ CC ║
║ A ║ AA ║ D ║ DD ║
║ ║ ║ ║ ║
║ B ║ BB ║ A ║ AA ║
║ B ║ BB ║ C ║ CC ║
║ B ║ BB ║ D ║ DD ║
║ ║ ║ ║ ║
║ C ║ CC ║ A ║ AA ║
║ C ║ CC ║ B ║ BB ║
║ C ║ CC ║ D ║ DD ║
║ D ║ DD ║ A ║ AA ║
║ D ║ DD ║ B ║ BB ║
║ D ║ DD ║ C ║ CC ║
╚═══════════╩═════════╩═══════════╩═══════════╝
EDIT
Actually it should look like this:
╔═══════════╦═════════╦═══════════╦═══════════╗
║ SrcState1 ║ SrcCity ║ DestState ║ DestCity ║
╠═══════════╬═════════╬═══════════╬═══════════╣
║ A ║ AA ║ B ║ BB ║
║ A ║ AA ║ C ║ CC ║
║ A ║ AA ║ D ║ DD ║
║ ║ ║ ║ ║
║ B ║ BB ║ C ║ CC ║
║ B ║ BB ║ D ║ DD ║
║ ║ ║ ║ ║
║ C ║ CC ║ D ║ DD ║
╚═══════════╩═════════╩═══════════╩═══════════╝
I think you need a query like this:
SELECT *
FROM Table1 t1
INNER JOIN
Table1 t2 ON t1.[state] < t2.[state];
or for a better solution:
;WITH t AS(
SELECT *,
ROW_NUMBER() OVER (ORDER BY [State]) As rn
FROM Table1)
SELECT *
FROM t t1
INNER JOIN
t t2 ON t1.rn < t2.rn;
You can use CROSS JOIN to generate Cartesian Product tab x tab and exclude in WHERE tab.state <> tab.state:
This solution works for original question.
SqlFiddleDemo
CREATE TABLE tab(state NVARCHAR(10), City NVARCHAR(10));
INSERT INTO tab(state, city)
VALUES ('A', 'AA'), ('B', 'BB'), ('C', 'CC'), ('D', 'DD');
SELECT
a.state
,a.city
,b.state
,b.city
FROM tab a
CROSS JOIN tab b
WHERE a.state <> b.state
ORDER BY a.state, a.city, b.state, b.city
For future state your question clearly if you want to get something like below use shA.t Solution:
╔═══════════╦═════════╦═══════════╦═══════════╗
║ SrcState1 ║ SrcCity ║ DestState ║ DestCity ║
╠═══════════╬═════════╬═══════════╬═══════════╣
║ A ║ AA ║ B ║ BB ║
║ A ║ AA ║ C ║ CC ║
║ A ║ AA ║ D ║ DD ║
║ ║ ║ ║ ║
║ B ║ BB ║ C ║ CC ║
║ B ║ BB ║ D ║ DD ║
║ ║ ║ ║ ║
║ C ║ CC ║ D ║ DD ║
╚═══════════╩═════════╩═══════════╩═══════════╝