Related
This image shows how my raw table looks like:
Following are the conditions to get the transposed table from the image below:
Each row has a unique id
We only need columns for groups A,B,C in the group field and not others.
There could be single or multiple id for group A for the same app id, I need to get those rows for which date is minimum.
There could be single or multiple id for group B and C for the same app id, I need to get those rows for which date is maximum
The image below shows how my final table should look like:
Each row has a unique id
We only need columns for groups A,B,C in the group field and not others.
add this to your query
WHERE `GROUP` IN ('A','B','C')
There could be single or multiple id for group A for the same app id, I need to get those rows for which date is minimum.
add somewhere after the SELECT:
MIN(date) OVER (PARTIITON BY appid)
There could be single or multiple id for group B and C for the same app id, I need to get those rows for which date is maximum
change the added option on point 3 to:
CASE WHEN `group` IN ('B','C')
THEN MAX(date) OVER (PARTIITON BY appid)
ELSE MIN(date) OVER (PARTIITON BY appid)
END
Maybe this helps you to try and take a serious request of solving this yourself (and learn from it) in stead of asking for a solution and then do copy/paste...
BTW: Naming fiels with reserved words, like GROUP and DATE is not a very smart thing to do. A better name for the column GROUP might be CategoryGroup (or whatever this group is referring to)
I took a different approach to this. The SQL is longer but I think it's more auditable.
The main logic point is that I broke A and BC into 2 different subqueries, and used QUALIFY ROW_NUMBER() to choose the correct row, based on either ASC or DESC per your requirements.
I know you are using mysql and this might not work since I don't have an instance to test this one, but here is the SQL I got from building this logic in Rasgo, which I tested on Snowflake and it worked.
-- This splits the data into group A only
WITH CTE_A AS (
SELECT
*
FROM
{{ your_table }}
WHERE
my_group = 'A'
),
-- This splits the data into group B and C only
CTE_B AS (
SELECT
*
FROM
{{ your_table }}
WHERE
my_group IN('B', 'C')
),
-- Selecting from A only, it keeps the most recent row ASCENDING
CTE_A_FIRST AS (
SELECT
*
FROM
CTE_A QUALIFY ROW_NUMBER() OVER (
PARTITION BY APP_ID,
MY_GROUP
ORDER BY
MY_DATE ASC
) = 1
),
-- Selecting from A only, it keeps the most recent row DESCENDING
CTE_B_LAST AS (
SELECT
*
FROM
CTE_B QUALIFY ROW_NUMBER() OVER (
PARTITION BY APP_ID,
MY_GROUP
ORDER BY
MY_DATE DESC
) = 1
),
-- Here we just union A and BC back to one another
CTE_ABC AS (
SELECT
ID,
APP_ID,
MY_DATE,
MY_GROUP,
SCORE1,
SCORE2
FROM
CTE_B_LAST
UNION ALL
SELECT
ID,
APP_ID,
MY_DATE,
MY_GROUP,
SCORE1,
SCORE2
FROM
CTE_B
),
-- We pivot the date horizontally so we get a date for A B C
-- the MIN does not matter, since at this point, we only have 1
CTE_PVT_DATE AS (
SELECT
APP_ID,
B,
C,
A
FROM
(
SELECT
APP_ID,
MY_DATE,
MY_GROUP
FROM
CTE_ABC
) PIVOT (
MIN (MY_DATE) FOR MY_GROUP IN ('B', 'C', 'A')
) as p (APP_ID, B, C, A)
),
-- We pivot the SCORE1 horizontally so we get a date for A B C
-- the MIN does not matter, since at this point, we only have 1
CTE_PVT_SCORE1 AS (
SELECT
APP_ID,
B,
C,
A
FROM
(
SELECT
APP_ID,
SCORE1,
MY_GROUP
FROM
CTE_ABC
) PIVOT (
MIN (SCORE1) FOR MY_GROUP IN ('B', 'C', 'A')
) as p (APP_ID, B, C, A)
),
-- We pivot the SCORE2 horizontally so we get a date for A B C
-- the MIN does not matter, since at this point, we only have 1
CTE_PVT_SCORE2 AS (
SELECT
APP_ID,
B,
C,
A
FROM
(
SELECT
APP_ID,
SCORE2,
MY_GROUP
FROM
CTE_ABC
) PIVOT (
MIN (SCORE2) FOR MY_GROUP IN ('B', 'C', 'A')
) as p (APP_ID, B, C, A)
),
-- We join the subqueries above together on the APP_IDs
CTE_JOINED AS (
SELECT
t0.*,
t1.APP_ID as SCORE1_APP_ID,
t1.B as SCORE1_B,
t1.C as SCORE1_C,
t1.A as SCORE1_A,
t2.APP_ID as SCORE2_APP_ID,
t2.B as SCORE2_B,
t2.C as SCORE2_C,
t2.A as SCORE2_A
FROM
CTE_PVT_DATE t0
INNER JOIN CTE_PVT_SCORE1 t1 ON t0.APP_ID = t1.APP_ID
INNER JOIN CTE_PVT_SCORE2 t2 ON t0.APP_ID = t2.APP_ID
)
-- The final select is really just renaming ...
-- the magic has already happened
SELECT
A AS DATE_A,
B AS DATE_B,
C AS DATE_C,
APP_ID,
SCORE1_B,
SCORE1_C,
SCORE1_A,
SCORE2_B,
SCORE2_C,
SCORE2_A
FROM
CTE_JOINED
I'll roll out my attempt along several steps and then show you the full solution made up of these steps, so that you can understand it piece by piece, given the following definition of your input table:
CREATE TABLE tab(
id INT,
app_id INT,
date VARCHAR(20),
group VARCHAR(20),
score1 INT,
score2 INT
);
STEP 1. Formatting date using a proper DATE format ("YYYY-MM-DD"). For this purpose the function STR_TO_DATE can come in handy.
WITH formatted_tab AS (
SELECT id,
app_id,
STR_TO_DATE(date, '%m/%d/%Y') AS date,
group,
score1,
score2
FROM tab
)
STEP 2. Extracting the useful dates according to the group field. As long as you treat group "A" differently with respect to group "B" and "C" specifically, the idea here is to address each group with a different query, where
in the former case the MIN aggregation function is applied,
in the latter case the MAX aggregation function is applied,
Then the two output result sets are combined with a UNION operation.
(
SELECT app_id,
MIN(date) AS date,
group
FROM formatted_tab
WHERE group IN ('A')
GROUP BY app_id,
group
UNION
SELECT app_id,
MAX(date) AS date,
group
FROM formatted_tab
WHERE group IN ('B', 'C')
GROUP BY app_id,
group
) needed_dates
STEP 3. Getting back scores corresponding to group and date field. This is done with a simple INNER JOIN between the last generated table and the formatted table.
(
SELECT needed_dates.*,
formatted_tab.score1,
formatted_tab.score2
FROM needed_dates
INNER JOIN formatted_tab
ON needed_dates.app_id = formatted_tab.app_id
AND needed_dates.date = formatted_tab.date
AND needed_dates.group = formatted_tab.group
) needed_infos
STEP 4. Pivoting the table exploiting MySQL tools like:
the IF statement to retrieve the values corresponding to a specific group
the MAX aggregation function, to aggregate on the same group
These tools are applied for each group you specified ('A', 'B' and 'C').
SELECT app_id,
MAX(IF(group='A', date , NULL)) AS date_groupA,
MAX(IF(group='B', date , NULL)) AS date_groupB,
MAX(IF(group='C', date , NULL)) AS date_groupC,
MAX(IF(group='A', score1, NULL)) AS score1_groupA,
MAX(IF(group='A', score2, NULL)) AS score2_groupA,
MAX(IF(group='B', score1, NULL)) AS score1_groupB,
MAX(IF(group='B', score2, NULL)) AS score2_groupB,
MAX(IF(group='C', score1, NULL)) AS score1_groupC,
MAX(IF(group='C', score2, NULL)) AS score2_groupC
FROM needed_infos
GROUP BY app_id
Full attempt. This is the combination of the previous snippets. The only difference is the presence of backticks for the field names, that avoid MySQL to misunderstand them with MySQL private keywords like "date" (indicating the DATE type), "group" (use as keyword in the GROUP BY clause) or similar.
WITH `formatted_tab` AS (
SELECT `id`,
`app_id`,
STR_TO_DATE(`date`, '%m/%d/%Y') AS `date`,
`group`,
`score1`,
`score2`
FROM `tab`
)
SELECT `app_id`,
MAX(IF(`group`='A', `date` , NULL)) AS date_groupA,
MAX(IF(`group`='B', `date` , NULL)) AS date_groupB,
MAX(IF(`group`='C', `date` , NULL)) AS date_groupC,
MAX(IF(`group`='A', `score1`, NULL)) AS score1_groupA,
MAX(IF(`group`='A', `score2`, NULL)) AS score2_groupA,
MAX(IF(`group`='B', `score1`, NULL)) AS score1_groupB,
MAX(IF(`group`='B', `score2`, NULL)) AS score2_groupB,
MAX(IF(`group`='C', `score1`, NULL)) AS score1_groupC,
MAX(IF(`group`='C', `score2`, NULL)) AS score2_groupC
FROM ( SELECT needed_dates.*,
formatted_tab.score1,
formatted_tab.score2
FROM ( SELECT `app_id`,
MIN(`date`) AS `date`,
`group`
FROM `formatted_tab`
WHERE `group` IN ('A')
GROUP BY `app_id`,
`group`
UNION
SELECT `app_id`,
MAX(`date`) AS `date`,
`group`
FROM `formatted_tab`
WHERE `group` IN ('B', 'C')
GROUP BY `app_id`,
`group`
) needed_dates
INNER JOIN formatted_tab
ON needed_dates.app_id = formatted_tab.app_id
AND needed_dates.date = formatted_tab.date
AND needed_dates.group = formatted_tab.group
) needed_infos
GROUP BY `app_id`
You'll find a tested SQL Fiddle here.
Base: name, rate, dt.
Every day cost is inserted, I need to know the maximum cost without the first day. The first day of all products is different.
With all dates
SELECT `name`, MAX(rate) AS max
FROM `base`
GROUP BY `name`
This is similar to what I want, but not working.
SELECT `name`, MAX(rate) AS max, MIN(dt) AS min_dt
FROM `base`
WHERE `dt` > `min_dt`
GROUP BY `name`
Eample base
skirt, 6, 2018-10-10 00:00:00
skirt, 7, 2018-10-11 00:00:00
cap, 7, 2018-10-11 00:00:00
skirt, 8, 2018-10-12 00:00:00
cap, 6, 2018-10-12 00:00:00
Need
skirt, 8
cap, 6
One approach is to use an inline view to get the min_dt for each name. Then we can join and exclude the minimum date rows
Something like this:
SELECT b.name
, MAX(b.rate) AS `max`
FROM ( SELECT d.name
, MIN(d.dt) AS min_dt
FROM `base` d
GROUP
BY d.name
) m
JOIN `base` b
ON b.dt > m.min_dt
AND b.name = m.name
GROUP
BY b.name
There are other query patterns that will achieve an equivalent result. My preference would to avoid a correlated subquery, but something like this would also return the specified result:
SELECT b.name
, MAX(b.rate) AS `max`
FROM `base` b
WHERE b.dt > ( SELECT MIN(d.dt)
FROM `base` d
WHERE d.name = b.name
)
GROUP
BY b.name
(With both of these query forms, if there is only row in base for a given name, the query will not return a row for that name.)
You should try to exclude the min dt using subqueries:
SELECT name, MAX(rate) AS max
FROM (SELECT name, rate, dt
FROM base B
WHERE dt NOT IN (SELECT MIN(dt) FROM base WHERE name=B.name )) as A
GROUP BY name
Fiddle here
I trying to get the last 6 months of the min and max of prices in my table and display them as a group by months. My query is not returning the corresponding rows values, such as the date time for when the max price was or min..
I want to select the min & max prices and the date time they both occurred and the rest of the data for that row...
(the reason why i have concat for report_term, as i need to print this with the dataset when displaying results. e.g. February 2018 -> ...., January 2018 -> ...)
SELECT metal_price_id, CONCAT(MONTHNAME(metal_price_datetime), ' ', YEAR(metal_price_datetime)) AS report_term, max(metal_price) as highest_gold_price, metal_price_datetime FROM metal_prices_v2
WHERE metal_id = 1
AND DATEDIFF(NOW(), metal_price_datetime) BETWEEN 0 AND 180
GROUP BY report_term
ORDER BY metal_price_datetime DESC
I have made an example, extract from my DB:
http://sqlfiddle.com/#!9/617bcb2/4/0
My desired result would be to see the min and max prices grouped by month, date of min, date of max.. and all in the last 6 months.
thanks
UPDATE.
The below code works, but it returns back rows from beyond the 180 days specified. I have just checked, and it is because it joining by the price which may be duplicated a number of times during the years.... see: http://sqlfiddle.com/#!9/5f501b/1
You could use twice inner join on the subselect for min and max
select a.metal_price_datetime
, t1.highest_gold_price
, t1.report_term
, t2.lowest_gold_price
,t2.metal_price_datetime
from metal_prices_v2 a
inner join (
SELECT CONCAT(MONTHNAME(metal_price_datetime), ' ', YEAR(metal_price_datetime)) AS report_term
, max(metal_price) as highest_gold_price
from metal_prices_v2
WHERE metal_id = 1
AND DATEDIFF(NOW(), metal_price_datetime) BETWEEN 0 AND 180
GROUP BY report_term
) t1 on t1.highest_gold_price = a.metal_price
inner join (
select a.metal_price_datetime
, t.lowest_gold_price
, t.report_term
from metal_prices_v2 a
inner join (
SELECT CONCAT(MONTHNAME(metal_price_datetime), ' ', YEAR(metal_price_datetime)) AS report_term
, min(metal_price) as lowest_gold_price
from metal_prices_v2
WHERE metal_id = 1
AND DATEDIFF(NOW(), metal_price_datetime) BETWEEN 0 AND 180
GROUP BY report_term
) t on t.lowest_gold_price = a.metal_price
) t2 on t2.report_term = t1.report_term
simplified version of what you should do so you can learn the working process.
You need calculate the min() max() of the periods you need. That is your first brick on this building.
you have tableA, you calculate min() lets call it R1
SELECT group_field, min() as min_value
FROM TableA
GROUP BY group_field
same for max() call it R2
SELECT group_field, max() as max_value
FROM TableA
GROUP BY group_field
Now you need to bring all the data from original fields so you join each result with your original table
We call those T1 and T2:
SELECT tableA.group_field, tableA.value, tableA.date
FROM tableA
JOIN ( ... .. ) as R1
ON tableA.group_field = R1.group_field
AND tableA.value = R1.min_value
SELECT tableA.group_field, tableA.value, tableA.date
FROM tableA
JOIN ( ... .. ) as R2
ON tableA.group_field = R2.group_field
AND tableA.value = R2.max_value
Now we join T1 and T2.
SELECT *
FROM ( .... ) as T1
JOIN ( .... ) as T2
ON t1.group_field = t2.group_field
So the idea is if you can do a brick, you do the next one. Then you also can add filters like last 6 months or something else you need.
In this case the group_field is the CONCAT() value
I am looking for queries, using which I can analyze a general employee database. This is for Data Analysis.
Tried this for monthly employee trend
SELECT
dt.FullDateAlternateKey as 'Date'
, count(1) as ActiveCount
FROM DimDate dt
LEFT JOIN (SELECT 'Active' as 'EmpStatus', * FROM DimEmployee) emp
-- regular active employees
ON (dt.FullDateAlternateKey between emp.StartDate and ISNULL(emp.EndDate,'9999-12-31'))
WHERE
dt.FullDateAlternateKey = EOMONTH(dt.FullDateAlternateKey)
GROUP BY
dt.FullDateAlternateKey
ORDER BY
1;
also found CTE use for finding employee hierarchy
WITH DirectReports (ManagerID, EmployeeID, Title, DeptID, Level)
AS
(
-- Anchor member definition
SELECT e.ParentEmployeeKey, e.EmployeeKey, e.Title, e.DepartmentName,
0 AS Level
FROM DimEmployee AS e
WHERE e.ParentEmployeeKey IS NULL
UNION ALL
-- Recursive member definition
SELECT e.ParentEmployeeKey, e.EmployeeKey, e.Title, e.DepartmentName,
Level + 1
FROM DimEmployee AS e
INNER JOIN DirectReports AS d
ON e.ParentEmployeeKey = d.EmployeeID
)
-- Statement that executes the CTE
SELECT ManagerID, EmployeeID, Title, DeptID, Level
FROM DirectReports
WHERE DeptID = 'Information Services' OR Level = 0
also, some good queries to analyze the sales data
-- Show each sales average for Group, Country, and Region all in one query
SELECT DISTINCT
t.SalesTerritoryGroup
, t.SalesTerritoryCountry
, t.SalesTerritoryRegion
, AVG(s.SalesAmount) OVER(PARTITION BY t.SalesTerritoryGroup ) as 'GroupAvgSales'
, AVG(s.SalesAmount) OVER(PARTITION BY t.SalesTerritoryCountry ) as 'CountryAvgSales'
, AVG(s.SalesAmount) OVER(PARTITION BY t.SalesTerritoryRegion ) as 'RegionAvgSales'
FROM FactInternetSales s
JOIN DimSalesTerritory t ON
s.SalesTerritoryKey = t.SalesTerritoryKey
WHERE
YEAR(s.OrderDate) = 2013
ORDER BY
1,2,3
Use additional aggregations to understand more about product sales such as the distribution of sales etc..
SELECT
cat.EnglishProductCategoryName 'Category'
, sub.EnglishProductSubcategoryName 'SubCategory'
, count(1) 'Count' -- How many sales where there?
, sum(s.SalesAmount) 'Sales' -- How much sales did we have?
, avg(s.SalesAmount) 'Avg_SalesAmount' -- What was the Avg sale amount?
, min(s.SalesAmount) 'Min_SaleAmount' -- What was the Min sale amount?
, max(s.SalesAmount) 'Max_SaleAmount' -- What was the Max sale amount
FROM FactInternetSales s
LEFT JOIN DimProduct p ON s.ProductKey = p.ProductKey
LEFT JOIN DimProductSubcategory sub ON p.ProductSubcategoryKey = sub.ProductSubcategoryKey
LEFT JOIN DimProductCategory cat ON sub.ProductCategoryKey = cat.ProductCategoryKey
-- must use group by in order for aggregation to work properly
GROUP BY
cat.EnglishProductCategoryName -- column aliases aren't allowed
, sub.EnglishProductSubcategoryName
ORDER BY
cat.EnglishProductCategoryName
, sub.EnglishProductSubcategoryName
-- Calculate the customer acquisition funnel
SELECT
c.FirstName
, c.LastName
, c.DateFirstPurchase
, DATEDIFF(d,c.DateFirstPurchase,getdate()) as 'DaysSinceFirstPurchase' -- How long have they been a customer?
FROM DimCustomer c
ORDER BY 3 DESC
-- Calculate a Monthly average of customer tenure
SELECT
EOMONTH(c.DateFirstPurchase) as 'MonthOfFirstPurchase' -- What month did they become a customer?
, DATEDIFF(d,EOMONTH(c.DateFirstPurchase),getdate()) as 'DaysSinceFirstPurchase' -- How long have they been a customer?
, COUNT(1) as 'CustomerCount' -- How manY customers are there for this month?
FROM DimCustomer c
GROUP BY EOMONTH(c.DateFirstPurchase)
ORDER BY 2 DESC
-- Show the top product Sub Categories for each year
SELECT
count(DISTINCT s.SalesOrderNumber) 'OrderCount' -- use 1 instead of a field for faster performance
, RANK() OVER (PARTITION BY YEAR(s.OrderDate) ORDER BY sum(s.SalesAmount) DESC) 'SalesRank'
, sum(s.SalesAmount) 'TotalSales'
, cat.EnglishProductCategoryName 'Category'
, sub.EnglishProductSubcategoryName 'SubCategory'
, YEAR(s.OrderDate) 'Year'
FROM FactInternetSales s
INNER JOIN DimProduct p ON s.ProductKey = p.ProductKey
INNER JOIN DimProductSubcategory sub ON p.ProductSubcategoryKey = sub.ProductSubcategoryKey
INNER JOIN DimProductCategory cat ON sub.ProductCategoryKey = cat.ProductCategoryKey
-- must use group by in order for aggregation to work properly
GROUP BY
cat.EnglishProductCategoryName -- column aliases aren't allowed
, sub.EnglishProductSubcategoryName
, YEAR(s.OrderDate)
ORDER BY YEAR(s.OrderDate), SUM(s.SalesAmount) DESC;
-- first, create weekly sales totals
SELECT SUM(s.SalesAmount) 'WeeklySales'
, DATEPART(ww, s.OrderDate) as 'WeekNum'
FROM FactInternetSales s
WHERE YEAR(s.OrderDate) = 2013
GROUP BY
DATEPART(ww, s.OrderDate)
ORDER BY
DATEPART(ww, s.OrderDate) ASC
-- use that subquery as our source and calculate the moving average
SELECT
AVG(WeeklySales) OVER (ORDER BY WeekNum ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) as AvgSales
, WeeklySales as 'TotalSales'
, WeekNum
FROM (
SELECT SUM(s.SalesAmount) 'WeeklySales'
, DATEPART(ww, s.OrderDate) as 'WeekNum'
FROM FactInternetSales s
WHERE YEAR(s.OrderDate) = 2013
GROUP BY
DATEPART(ww, s.OrderDate)
) AS s
GROUP BY
WeekNum, WeeklySales
ORDER BY
WeekNum ASC
-- Running Total
SELECT
SUM(MonthlySales) OVER (PARTITION BY SalesYear ORDER BY SalesMonth ROWS UNBOUNDED PRECEDING) as YTDSales
, MonthlySales as 'MonthlySales'
, SalesYear
, SalesMonth
FROM (
SELECT SUM(s.SalesAmount) 'MonthlySales'
, MONTH(s.OrderDate) as 'SalesMonth'
, year(s.OrderDate) as 'SalesYear'
FROM FactInternetSales s
GROUP BY
MONTH(s.OrderDate)
, year(s.OrderDate)
) AS s
GROUP BY
SalesMonth, SalesYear, MonthlySales
ORDER BY
SalesYear, SalesMonth ASC
-- Get Prev Year Sales
WITH MonthlySales (YearNum, MonthNum, Sales)
AS
(
SELECT d.CalendarYear, d.MonthNumberOfYear, SUM(s.SalesAmount)
FROM DimDate d
JOIN FactInternetSales s ON d.DateKey = s.OrderDateKey
GROUP BY d.CalendarYear, d.MonthNumberOfYear
)
-- Get Current Year and join to CTE for previous year
SELECT
d.CalendarYear
, d.MonthNumberOfYear
, ms.Sales PrevSales
, SUM(s.SalesAmount) CurrentSales
FROM DimDate d
JOIN FactInternetSales s ON d.DateKey = s.OrderDateKey
JOIN MonthlySales ms ON
d.CalendarYear-1 = ms.YearNum AND
d.MonthNumberOfYear = ms.MonthNum
GROUP BY
d.CalendarYear
, d.MonthNumberOfYear
, ms.Sales
ORDER BY
1 DESC, 2 DESC
-- Now calculate the % change Year over Year
WITH MonthlySales (YearNum, MonthNum, Sales)
AS
(
SELECT d.CalendarYear, d.MonthNumberOfYear, SUM(s.SalesAmount)
FROM DimDate d
JOIN FactInternetSales s ON d.DateKey = s.OrderDateKey
GROUP BY d.CalendarYear, d.MonthNumberOfYear
)
-- Get Current Year and join to CTE for previous year
SELECT
d.CalendarYear
, d.MonthNumberOfYear
, ms.Sales PrevSales
, SUM(s.SalesAmount) CurrentSales
, (SUM(s.SalesAmount) - ms.Sales) / SUM(s.SalesAmount) 'PctGrowth'
FROM DimDate d
JOIN FactInternetSales s ON d.DateKey = s.OrderDateKey
JOIN MonthlySales ms ON
d.CalendarYear-1 = ms.YearNum AND
d.MonthNumberOfYear = ms.MonthNum
GROUP BY
d.CalendarYear
, d.MonthNumberOfYear
, ms.Sales
ORDER BY
1 DESC, 2 DESC
SQL Fiddle
Table scheme:
CREATE TABLE company
(`company_id` int,`name` varchar(30))
;
INSERT INTO company
(`company_id`,`name`)
VALUES
(1,"Company A"),
(2,"Company B")
;
CREATE TABLE price
(`company_id` int,`price` int,`time` timestamp)
;
INSERT INTO price
(`company_id`,`price`,`time`)
VALUES
(1,50,'2015-02-21 02:34:40'),
(2,60,'2015-02-21 02:35:40'),
(1,70,'2015-02-21 05:34:40'),
(2,120,'2015-02-21 05:35:40'),
(1,150,'2015-02-22 02:34:40'),
(2,130,'2015-02-22 02:35:40'),
(1,170,'2015-02-22 05:34:40'),
(2,190,'2015-02-22 05:35:40')
I'm using Cron Jobs to fetch company prices. In concatenating the price history for each company, how can I make sure that only the last one in each day is included? In this case, I want all of the price records around 05:30am concatenated.
This is the result I'm trying to get (I have used Date(time) to only get the dates from the timestamps):
COMPANY_ID PRICE TIME
1 70|170 2015-02-21|2015-02-22
2 120|190 2015-02-21|2015-02-22
I have tried the following query but it doesn't work. The prices don't correspond to the dates and I don't know how to exclude all of the 2:30 am records before applying the Group_concat function.
SELECT company_id,price,trend_date FROM
(
SELECT company_id, GROUP_CONCAT(price SEPARATOR'|') AS price,
GROUP_CONCAT(trend_date SEPARATOR'|') AS trend_date
FROM
(
SELECT company_id,price,
DATE(time) AS trend_date
FROM price
ORDER BY time ASC
)x1
GROUP BY company_id
)t1
Can anyone show me how to get the desired result?
Ok, so this should work as intended:
SELECT p.company_id,
GROUP_CONCAT(price SEPARATOR '|') as price,
GROUP_CONCAT(PriceDate SEPARATOR '|') as trend_date
FROM price as p
INNER JOIN (SELECT company_id,
DATE(`time`) as PriceDate,
MAX(`time`) as MaxTime
FROM price
GROUP BY company_id,
DATE(`time`)) as t
ON p.company_id = t.company_id
AND p.`time` = t.MaxTime
GROUP BY p.company_id
Here is the modified sqlfiddle.
This is a bit unorthodox but I think it solves your problem:
SELECT company_id,
GROUP_CONCAT(price SEPARATOR'|'),
GROUP_CONCAT(trend_date SEPARATOR'|')
FROM (
SELECT *
FROM (
SELECT company_id,
DATE(`time`) `trend_date`,
price
FROM price
ORDER BY `time` DESC
) AS a
GROUP BY company_id, `trend_date`
) AS b
GROUP BY company_id