I am trying to assign a group number to distinct groups of rows in a dataset that has changing data over time. The changing fields are tran_seq, prog_id, deg-id, cur_id, and enroll_status in my example. When any of those fields are different from the previous row, I need a new grouping number. When the fields are the same as the prior row, then the grouping number should stay the same. When I try ROW_NUMBER(), RANK(), or DENSE_RANK(), I get increasing values for the same group (e.g. the first 2 rows in example). I feel I need to ORDER BY start_date as it is temporal data.
+----+----------+---------+--------+--------+---------------+------------+------------+---------+
| | tran_seq | prog_id | deg_id | cur_id | enroll_status | start_date | end_date | desired |
+----+----------+---------+--------+--------+---------------+------------+------------+---------+
| 1 | 1 | 6 | 9 | 3 | ENRL | 2004-08-22 | 2004-12-11 | 1 |
| 2 | 1 | 6 | 9 | 3 | ENRL | 2006-01-10 | 2006-05-06 | 1 |
| 3 | 1 | 6 | 9 | 59 | ENRL | 2006-08-29 | 2006-12-16 | 2 |
| 4 | 2 | 12 | 23 | 45 | ENRL | 2014-01-21 | 2014-05-16 | 3 |
| 5 | 2 | 12 | 23 | 45 | ENRL | 2014-08-18 | 2014-12-05 | 3 |
| 6 | 2 | 12 | 23 | 45 | LOAP | 2015-01-20 | 2015-05-15 | 4 |
| 7 | 2 | 12 | 23 | 45 | ENRL | 2015-08-25 | 2015-12-11 | 5 |
| 8 | 2 | 12 | 23 | 45 | LOAP | 2016-01-12 | 2016-05-06 | 6 |
| 9 | 2 | 12 | 23 | 45 | ENRL | 2016-05-16 | 2016-08-05 | 7 |
| 10 | 2 | 12 | 23 | 45 | LOAJ | 2016-08-23 | 2016-12-02 | 8 |
| 11 | 2 | 12 | 23 | 45 | ENRL | 2017-01-18 | 2017-05-05 | 9 |
| 12 | 2 | 12 | 23 | 45 | ENRL | 2018-01-17 | 2018-05-11 | 9 |
+----+----------+---------+--------+--------+---------------+------------+------------+---------+
Once I have grouping numbers, I think I can group by those to get what I'm ultimately after: a timeline of different statuses with start dates and end dates. For the example data above, that would be:
+---+----------+---------+--------+--------+---------------+------------+------------+
| | tran_seq | prog_id | deg_id | cur_id | enroll_status | start_date | end_date |
+---+----------+---------+--------+--------+---------------+------------+------------+
| 1 | 1 | 6 | 9 | 3 | ENRL | 2004-08-22 | 2006-05-06 |
| 2 | 1 | 6 | 9 | 59 | ENRL | 2004-08-29 | 2006-12-16 |
| 3 | 2 | 12 | 23 | 45 | ENRL | 2014-01-21 | 2014-12-05 |
| 4 | 2 | 12 | 23 | 45 | LOAP | 2015-01-20 | 2015-05-15 |
| 5 | 2 | 12 | 23 | 45 | ENRL | 2015-08-25 | 2015-12-11 |
| 6 | 2 | 12 | 23 | 45 | LOAP | 2016-01-12 | 2016-05-06 |
| 7 | 2 | 12 | 23 | 45 | ENRL | 2016-05-16 | 2016-08-05 |
| 8 | 2 | 12 | 23 | 45 | LOAJ | 2016-08-23 | 2016-12-02 |
| 9 | 2 | 12 | 23 | 45 | ENRL | 2017-01-17 | 2018-05-06 |
+---+----------+---------+--------+--------+---------------+------------+------------+
This is a classic XY problem, in that you are asking for an intermediate step to a different solution, rather than asking about the solution itself.
As you included your overall end goal as a bit of an addendum however, here is how you can reach that without your intermediate step:
declare #t table(tran_seq int, prog_id int, deg_id int, cur_id int, enroll_status varchar(4), start_date date, end_date date, desired int)
insert into #t values
(1,6,9,3 ,'ENRL','2004-08-22','2004-12-11',1)
,(1,6,9,3 ,'ENRL','2006-01-10','2006-05-06',1)
,(1,6,9,59 ,'ENRL','2006-08-29','2006-12-16',2)
,(2,12,23,45,'ENRL','2014-01-21','2014-05-16',3)
,(2,12,23,45,'ENRL','2014-08-18','2014-12-05',3)
,(2,12,23,45,'LOAP','2015-01-20','2015-05-15',4)
,(2,12,23,45,'ENRL','2015-08-25','2015-12-11',5)
,(2,12,23,45,'LOAP','2016-01-12','2016-05-06',6)
,(2,12,23,45,'ENRL','2016-05-16','2016-08-05',7)
,(2,12,23,45,'LOAJ','2016-08-23','2016-12-02',8)
,(2,12,23,45,'ENRL','2017-01-18','2017-05-05',9)
,(2,12,23,45,'ENRL','2018-01-17','2018-05-11',9)
;
select tran_seq
,prog_id
,deg_id
,cur_id
,enroll_status
,min(start_date) as start_date
,max(end_date) as end_date
from(select *
,row_number() over (order by end_date) - row_number() over (partition by tran_seq,prog_id,deg_id,cur_id,enroll_status order by end_date) as grp
from #t
) AS g
group by tran_seq
,prog_id
,deg_id
,cur_id
,enroll_status
,grp
order by start_date;
Output
+----------+---------+--------+--------+---------------+------------+------------+
| tran_seq | prog_id | deg_id | cur_id | enroll_status | start_date | end_date |
+----------+---------+--------+--------+---------------+------------+------------+
| 1 | 6 | 9 | 3 | ENRL | 2004-08-22 | 2006-05-06 |
| 1 | 6 | 9 | 59 | ENRL | 2006-08-29 | 2006-12-16 |
| 2 | 12 | 23 | 45 | ENRL | 2014-01-21 | 2014-12-05 |
| 2 | 12 | 23 | 45 | LOAP | 2015-01-20 | 2015-05-15 |
| 2 | 12 | 23 | 45 | ENRL | 2015-08-25 | 2015-12-11 |
| 2 | 12 | 23 | 45 | LOAP | 2016-01-12 | 2016-05-06 |
| 2 | 12 | 23 | 45 | ENRL | 2016-05-16 | 2016-08-05 |
| 2 | 12 | 23 | 45 | LOAJ | 2016-08-23 | 2016-12-02 |
| 2 | 12 | 23 | 45 | ENRL | 2017-01-18 | 2018-05-11 |
+----------+---------+--------+--------+---------------+------------+------------+
I'm dipping my toes into SQL. I have the following table
+------+----+------+------+-------+
| Type | ID | QTY | Rate | Name |
+------+----+------+------+-------+
| B | 1 | 1000 | 21 | Jack |
| B | 2 | 2000 | 12 | Kevin |
| B | 1 | 3000 | 24 | Jack |
| B | 1 | 1000 | 23 | Jack |
| B | 3 | 200 | 13 | Mary |
| B | 2 | 3000 | 12 | Kevin |
| B | 4 | 4000 | 44 | Chris |
| B | 4 | 5000 | 43 | Chris |
| B | 3 | 1000 | 26 | Mary |
+------+----+------+------+-------+
I don't know how I would leverage Sum and Group by to achieve the following result.
+------+----+------+------+-------+------------+
| Type | ID | QTY | Rate | Name | Sum of QTY |
+------+----+------+------+-------+------------+
| B | 1 | 1000 | 21 | Jack | 5000 |
| B | 1 | 3000 | 24 | Jack | Null |
| B | 1 | 1000 | 23 | Jack | Null |
| B | 2 | 3000 | 12 | Kevin | 5000 |
| B | 2 | 3000 | 12 | Kevin | Null |
| B | 3 | 200 | 13 | Mary | 1200 |
| B | 3 | 1000 | 26 | Mary | Null |
| B | 4 | 4000 | 44 | Chris | 9000 |
| B | 4 | 5000 | 43 | Chris | Null |
+------+----+------+------+-------+------------+
Any help is appreciated!
You can use window function :
select t.*,
(case when row_number() over (partition by type, id order by name) = 1
then sum(qty) over (partition by type, id order by name)
end) as Sum_of_QTY
from table t;
I have three tables:
My products with their IDs and their features.
is a table with treatments of my products with a treatment-ID, a method, and a date. The treatments are done in batches of many products so there is a crosstable
with the products IDs and the treatment IDs and a bool value for the success of the treatment.
Each product can undergo many different treatments so there is a many-to-many relation. I now want to add to the product table (1.) for every product a value that shows the method of its most recent successful treatment if there is any.
I made a query that groups the crosstable's entries by product-ID but I don't know how to show the method and date of it's last treatment.
table 1:
| productID | size | weight | height | ... |
|-----------|:----:|-------:|--------|-----|
| 1 | 13 | 16 | 9 | ... |
| 2 | 12 | 17 | 12 | ... |
| 3 | 11 | 15 | 15 | ... |
| ... | ... | ... | ... | ... |
table 2:
| treatmentID | method | date |
|-------------|:--------:|-----------:|
| 1 | dye blue | 01.02.2016 |
| 2 | dye red | 01.02.2017 |
| 3 | dye blue | 01.02.2018 |
| ... | ... | ... |
table 3:
| productID | treatmentID | success |
|-----------|:-----------:|--------:|
| 1 | 1 | yes |
| 1 | 2 | yes |
| 1 | 3 | no |
| ... | ... | ... |
I need table 1 to be like:
table 1:
| productID | size | weight | height | latest succesful method |
|-----------|:----:|-------:|--------|-------------------------|
| 1 | 13 | 16 | 9 | dye red |
| 2 | 12 | 17 | 12 | ... |
| 3 | 11 | 15 | 15 | ... |
| ... | ... | ... | ... | ... |
My query:
SELECT table3.productID, table2.method
FROM table2 INNER JOIN table3 ON table2.treatmentID = table3.treatmentID
GROUP BY table3.productID, table2.method
HAVING (((table3.productID)=Max([table2].[date])))
ORDER BY table3.productID DESC;
but this does NOT show only one (the most recent) entry but all of them.
Simplest solution here would be to write either a subquery within your sql, or create a new query to act as a subquery(it will look like a table) to help indicate(or elminate) the records you want to see.
Using similar but potentially slightly different source data as you only gave one example.
Table1
| ProductID | Size | Weight | Height |
|-----------|------|--------|--------|
| 1 | 13 | 16 | 9 |
| 2 | 12 | 17 | 12 |
| 3 | 11 | 15 | 15 |
Table2
| TreatmentID | Method | Date |
|-------------|------------|----------|
| 1 | dye blue | 1/2/2016 |
| 2 | dye red | 1/2/2017 |
| 3 | dye blue | 1/2/2018 |
| 4 | dye yellow | 1/4/2017 |
| 5 | dye brown | 1/5/2018 |
Table3
| ProductID | TreatmentID | Success |
|-----------|-------------|---------|
| 1 | 1 | yes |
| 1 | 2 | yes |
| 1 | 3 | no |
| 2 | 4 | no |
| 2 | 5 | yes |
First order of business is to get the max(dates) and productIds of successful treatments.
We'll do this by aggregating the date along with the productIDs and "success".
SELECT Table3.productid, Max(Table2.Date) AS MaxOfdate, Table3.success
FROM Table2 INNER JOIN Table3 ON Table2.treatmentid = Table3.treatmentid
GROUP BY Table3.productid, Table3.success;
This should give us something along the lines of:
| ProductID | MaxofDate | Success |
|-----------|-----------|---------|
| 1 | 1/2/2018 | No |
| 1 | 1/2/2017 | Yes |
| 2 | 1/4/2017 | No |
| 2 | 1/8/2017 | Yes |
We'll save this query as a "regular" query. I named mine "max", you should probably use something more descriptive. You'll see "max" in this next query.
Next we'll join tables1-3 together but in addition we will also use this "max" subquery to link tables 1 and 2 by the productID and MaxOfDate to TreatmentDate where success = "yes" to find the details of the most recent SUCCESSFUL treatment.
SELECT table1.productid, table1.size, table1.weight, table1.height, Table2.method
FROM ((table1 INNER JOIN [max] ON table1.productid = max.productid)
INNER JOIN Table2 ON max.MaxOfdate = Table2.date) INNER JOIN Table3 ON
(Table2.treatmentid = Table3.treatmentid) AND (table1.productid = Table3.productid)
WHERE (((max.success)="yes"));
The design will look something like this:
Design
(ps. you can add queries to your design query editor by clicking on the "Queries" tab when you are adding tables to your query design. They act just like tables, just be careful as very detailed queries tend to bog down Access)
Running this query should give us our final results.
| ProductID | Size | Weight | Height | Method |
|-----------|------|--------|--------|-----------|
| 1 | 13 | 16 | 9 | dye red |
| 2 | 12 | 17 | 12 | dye brown |
I have a table named stock and sales as below :
Stock Table :
+--------+----------+---------+
| Stk_ID | Stk_Name | Stk_Qty |
+--------+----------+---------+
| 1001 | A | 20 |
| 1002 | B | 50 |
+--------+----------+---------+
Sales Table :
+----------+------------+------------+-----------+
| Sales_ID | Sales_Date | Sales_Item | Sales_Qty |
+----------+------------+------------+-----------+
| 2001 | 2016-07-15 | A | 5 |
| 2002 | 2016-07-20 | B | 7 |
| 2003 | 2016-07-23 | A | 4 |
| 2004 | 2016-07-29 | A | 2 |
| 2005 | 2016-08-03 | B | 15 |
| 2006 | 2016-08-07 | B | 10 |
| 2007 | 2016-08-10 | A | 5 |
+----------+------------+------------+-----------+
With the table above, how can I find the available stock Ava_Stk for each stock after every sales?
Ava_Stk is expected to subtract Sales_Qty from Stk_Qty after every sales.
+----------+------------+------------+-----------+---------+
| Sales_ID | Sales_Date | Sales_Item | Sales_Qty | Ava_Stk |
+----------+------------+------------+-----------+---------+
| 2001 | 2016-07-15 | A | 5 | 15 |
| 2002 | 2016-07-20 | B | 7 | 43 |
| 2003 | 2016-07-23 | A | 4 | 11 |
| 2004 | 2016-07-29 | A | 2 | 9 |
| 2005 | 2016-08-03 | B | 15 | 28 |
| 2006 | 2016-08-07 | B | 10 | 18 |
| 2007 | 2016-08-10 | A | 5 | 4 |
+----------+------------+------------+-----------+---------+
Thank you!
You want a cumulative sum and to subtract it from the stock table. In SQL Server 2012+:
select s.*,
(st.stk_qty -
sum(s.sales_qty) over (partition by s.sales_item order by sales_date)
) as ava_stk
from sales s join
stock st
on s.sales_item = st.stk_name;
Does anyone know where the Invoice data is stored in Magento database?
For example, I've found that the order data is stored in sales_order, sales_flat_order, sales_flat_order_item.
I've also found out that the main invoice data is stored in sales_order_entity, sales_order_entity_decimal and sales_order_entity_int. Through that I can change the subtotal and totals of the invoice in the system.
But! I don't know where to find the items data? For orders, that data is in sales_flat_order_item, but my sales_flat_invoice_item table is empty?!
http://img809.imageshack.us/img809/1921/invoicey.jpg
I will tell you what I know for 1.4.0.1 which is the version i currently develop for, it may or may not be the same for whatever version you are using.
Also, why are you in the database anyways? Magento has made models for you to use so that you don't have to work in the database. Regardless I will describe how I find whatever attribute I'm looking for ...
For starters I'm assuming that your already logged into the database via a mysql client, run
SELECT `entity_type_id`,`entity_type_code`,`entity_table` FROM `eav_entity_type`
which will get you something like ...
+----------------+----------------------+----------------------------------+
| entity_type_id | entity_type_code | entity_table |
+----------------+----------------------+----------------------------------+
| 1 | customer | customer/entity |
| 2 | customer_address | customer/address_entity |
| 3 | catalog_category | catalog/category |
| 4 | catalog_product | catalog/product |
| 5 | quote | sales/quote |
| 6 | quote_item | sales/quote_item |
| 7 | quote_address | sales/quote_address |
| 8 | quote_address_item | sales/quote_entity |
| 9 | quote_address_rate | sales/quote_entity |
| 10 | quote_payment | sales/quote_entity |
| 11 | order | sales/order |
| 12 | order_address | sales/order_entity |
| 13 | order_item | sales/order_entity |
| 14 | order_payment | sales/order_entity |
| 15 | order_status_history | sales/order_entity |
| 16 | invoice | sales/order_entity |
| 17 | invoice_item | sales/order_entity |
| 18 | invoice_comment | sales/order_entity |
| 19 | shipment | sales/order_entity |
| 20 | shipment_item | sales/order_entity |
| 21 | shipment_comment | sales/order_entity |
| 22 | shipment_track | sales/order_entity |
| 23 | creditmemo | sales/order_entity |
| 24 | creditmemo_item | sales/order_entity |
| 25 | creditmemo_comment | sales/order_entity |
+----------------+----------------------+----------------------------------+
We want to know more about the "invoice_item" entity so lets see what attributes it has ... run
SELECT `attribute_id`,`entity_type_id`,`attribute_code`,`backend_type` FROM `eav_attribute` WHERE `entity_type_id`=17;
and you'll get something like ...
+--------------+----------------+----------------------------------+--------------+
| attribute_id | entity_type_id | attribute_code | backend_type |
+--------------+----------------+----------------------------------+--------------+
| 349 | 17 | additional_data | text |
| 340 | 17 | base_cost | decimal |
| 346 | 17 | base_discount_amount | decimal |
| 345 | 17 | base_price | decimal |
| 679 | 17 | base_price_incl_tax | decimal |
| 348 | 17 | base_row_total | decimal |
| 681 | 17 | base_row_total_incl_tax | decimal |
| 347 | 17 | base_tax_amount | decimal |
| 567 | 17 | base_weee_tax_applied_amount | decimal |
| 568 | 17 | base_weee_tax_applied_row_amount | decimal |
| 579 | 17 | base_weee_tax_disposition | decimal |
| 580 | 17 | base_weee_tax_row_disposition | decimal |
| 337 | 17 | description | text |
| 342 | 17 | discount_amount | decimal |
| 336 | 17 | name | varchar |
| 334 | 17 | order_item_id | int |
| 333 | 17 | parent_id | static |
| 341 | 17 | price | decimal |
| 678 | 17 | price_incl_tax | decimal |
| 335 | 17 | product_id | int |
| 339 | 17 | qty | decimal |
| 344 | 17 | row_total | decimal |
| 680 | 17 | row_total_incl_tax | decimal |
| 338 | 17 | sku | varchar |
| 343 | 17 | tax_amount | decimal |
| 571 | 17 | weee_tax_applied | text |
| 569 | 17 | weee_tax_applied_amount | decimal |
| 570 | 17 | weee_tax_applied_row_amount | decimal |
| 577 | 17 | weee_tax_disposition | decimal |
| 578 | 17 | weee_tax_row_disposition | decimal |
+--------------+----------------+----------------------------------+--------------+
the last column (backend_type) combined with the table for the entity (entity_table) is where the attribute for that entity will be so attribute "additional_data" should be in sales_order_entity_text with an attribute_id of 349.
Armed with this information now we just need to find an invoice, I'll use an example from a test install of mine. Lets look for the "base_price" of an invoice item.
First lets find all the items that are associated to the invoice (in my case invoice entity_id of 1303954)
SELECT * FROM `sales_order_entity` WHERE `entity_type_id`=17 AND `parent_id`=1303954;
which gives 2 items
+-----------+----------------+------------------+--------------+-----------+----------+---------------------+---------------------+-----------+
| entity_id | entity_type_id | attribute_set_id | increment_id | parent_id | store_id | created_at | updated_at | is_active |
+-----------+----------------+------------------+--------------+-----------+----------+---------------------+---------------------+-----------+
| 1303955 | 17 | 0 | | 1303954 | NULL | 2011-06-01 14:10:48 | 2011-06-01 14:10:48 | 1 |
| 1303956 | 17 | 0 | | 1303954 | NULL | 2011-06-01 14:10:48 | 2011-06-01 14:10:48 | 1 |
+-----------+----------------+------------------+--------------+-----------+----------+---------------------+---------------------+-----------+
Lets choose the first one and find the 'base_price'
SELECT * FROM `sales_order_entity_decimal` WHERE `attribute_id`=345 AND `entity_id`=1303955;
Which gives us ....
+----------+----------------+--------------+-----------+---------+
| value_id | entity_type_id | attribute_id | entity_id | value |
+----------+----------------+--------------+-----------+---------+
| 7361390 | 17 | 345 | 1303955 | 31.2500 |
+----------+----------------+--------------+-----------+---------+
Which of course its just a simple update to change it.
Again if you can do it via a Magento model I would highly suggest you do it that way, but if manual is the only way to go then well I hope this helped :)