I have a sample spark dataframe:
df = [("1","5563","John","Smith","2020-12-15","M"),
("1","5563","John","Smith","2020-12-18","M"),
("2","5568","Jane","King","2020-12-15","F"),
("3","5574","Ernest","Goldberg","2021-10-12","M"),
("5","31","Joe","Hanson","2022-03-16","M"),
("1","5563","John","Smith","2021-01-02","M"),
("2","5568","Jane","King","2021-01-25","F")
]
columns = ['bldg_num",'person_id','first_name','last_name','intake_date','gender']
What I would like to happen is check the difference between intake_dates. If the datediff is less than 5, keep the later one. But if the datediff between the same id is 5 or more days, I keep both records.
I got the following initially ran using SQL:
SELECT *
FROM df
WHERE EXISTS (
SELECT *
FROM df df_v2
WHERE df.person_id = df_v2.person_id AND DATEDIFF(df.intake_date, df_v2.intake_date) => 5 )
HOwever, the above code filters it where the datediff is 5 or more. There are ids that may only have one intake_date that still needs to be kept.
There are links here that partially meets what I need to do, but so far, none is close enough: remove duplicates in list, but keep one copy Finding partial and exact duplicate from a SQL table I also thought of using the concept of returning/recurring customers Calculate recurring customer but I am lost in writing the code. From the df above, I am expecting to get the following output:
df2 = [
("1","5563","John","Smith","2020-12-18","M"),
("2","5568","Jane","King","2020-12-15","F"),
("3","5574","Ernest","Goldberg","2021-10-12","M"),
("5","31","Joe","Hanson","2022-03-16","M"),
("1","5563","John","Smith","2021-01-02","M"),
("2","5568","Jane","King","2021-01-25","F")
]
The first row will be dropped because it is less than 5 days to the next intake_date while still keeping id=5 and id=3 rows. I am thinking, the only way to do it is write a udf, but I am still new in that concept. SQL alternative is ok too, if it is possible.
Let me know if my question is a bit confusing, I will try to rephrase to make it clearer. Thank you
Related
I'm trying to create a Measure in Power BI using DAX that achieves the below.
The data set has four columns, Name, Month, Country and Value. I have duplicates so first I need to dedupe across all four columns, then group by Month and sum up the value. And then, I need to average across the Month to arrive at a single value. How would I achieve this in DAX?
I figured it out. Reply by #OscarLar was very close but nested SUMMARIZE causes problems because it cannot aggregate values calculated dynamically within the query itself (https://www.sqlbi.com/articles/nested-grouping-using-groupby-vs-summarize/).
I kept the inner SUMMARIZE from #OscarLar's answer changed the outer SUMMARIZE with a GROUPBY. Here's the code that worked.
AVERAGEX(GROUPBY(SUMMARIZE(Data, Data[Name], Data[Month], Data[Country], Data[Value]), Data[Month], "Month_Value", sumx(CURRENTGROUP(), Data[Value])), [Month_Value])
Not sure I completeley understood the question since you didn't provide example data or some DAX code you've already tried. Please do so next time.
I'm assuming parts of this can not (for reasons) be done using power query so that you have to use DAX. Then I think this will do what you described.
Create a temporary data table called Data_reduced in which duplicate rows have been removed.
Data_reduced =
SUMMARIZE(
'Data';
[Name];
[Month];
[Country];
[Value]
)
Then create the averaging measure like this
AveragePerMonth =
AVERAGEX(
SUMMARIZE(
'Data_reduced';
'Data_reduced'[Month];
"Sum_month"; SUM('Data_reduced'[Value])
);
[Sum_month]
)
Where Data is the name of the table.
I have a query (relationship between CONTRACT <-> ORDERS) that I decided to break up into 2 parts (contract and orders) so I can reuse in another stored procedure.
When I run the code before the break up, it took around 10 secs to run; however, when I use a function for getting the contract, then pump the data into a temp table first, then join to the other parts it takes 2m:30s - why the difference in time?
The function takes less than a second to run and returns only one row i.e. details of one contract (contract_id is the parameter supplied to the function).
The part that is most effecting the performance the (ORDERS) largest table in the query has 4.1 million rows and joins to a few other tables however; if I just run the sub query for orders in isolation with a particular filter i.e. the contract id it takes less than a second to run and just happens to return zero records based for the contract I am testing on (due to filtering on the type of order it is looking for).
Base on the above information you would think 1 sec at most for the function + 1 sec at most to get the orders + summarize = 2 seconds at most, not 2 and half minutes!
Where am I going wrong, how do I begin to isolate the issue in time difference?
I know someone is going to tell me to paste the code but surely it is an issue of the database vs indexes perhaps vs how the compiler performs when dealing with raw code versus broken up code into parts. Is there an area of the code I can look at before having to post my whole code as I have tried variations of OUTER APPLY vs LEFT JOIN from the contract temp table to the orders subquery and both give me about the same result. Any ideas?
I don't think the issue was with the code but the network I was running it on. Although bizarre in the fact I had 2 versions of the proc running side by side and yesterday or rather before the weekend one was running in 10 secs and it is still running in 10 secs 3 days later and my new version (using the function) was taking anywhere between 2 to 3 minutes. This morning it is running at 2 or 3 seconds!! So I don't know if it is the fact I changed from declaring my table structure and using a table variable instead first to where previously I was using SELECT ... INTO #Contract made the difference or the network or precompiling has an affect. Whatever it is it no longer an issue. Should I delete this post?
I have a massive query log table in postgresql. I have been asked to get statistical data from it, but the table is sooooo massive. It has about ~170000000 rows in it.
So I've been asked a statistical data for last 6 months, that will have count of services for each day.
The issue is that since the table is so big, it will take forever to get this data.
Here's the current query I use:
SELECT ql.query_time::timestamp::date,count(ql.query_name),ql.query_name
FROM query_log ql
WHERE ql.query_time BETWEEN '2017-12-20 14:00:00.000'::timestamp AND '2018-06-20 14:00:00.000'::timestamp AND success=TRUE
GROUP BY ql.query_time::timestamp::date, ql.query_name;
Please make proposals how to make this query faster and and effective. I want to save the output into the CSV.
I've been thinking on looping through each day for past 6 months but dont know how to do it.
OH, ql.query_time is indexed.
Thx!
I will try to explain the problem on an abstract level first:
I have X amount of data as input, which is always going to have a field DATE. Before, the dates that came as input (after some process) where put in a table as output. Now, I am asked to put both the input dates and any date between the minimun date received and one year from that moment. If there was originally no input for some day between this two dates, all fields must come with 0, or equivalent.
Example. I have two inputs. One with '18/03/2017' and other with '18/03/2018'. I now need to create output data for all the missing dates between '18/03/2017' and '18/04/2017'. So, output '19/03/2017' with every field to 0, and the same for the 20th and 21st and so on.
I know to do this programmatically, but on powercenter I do not. I've been told to do the following (which I have done, but I would like to know of a better method):
Get the minimun date, day0. Then, with an aggregator, create 365 fields, each has that "day0"+1, day0+2, and so on, to create an artificial year.
After that we do several transformations like sorting the dates, union between them, to get the data ready for a joiner. The idea of the joiner is to do an Full Outer Join between the original data, and the data that is going to have all fields to 0 and that we got from the previous aggregator.
Then a router picks with one of its groups the data that had actual dates (and fields without nulls) and other group where all fields are null, and then said fields are given a 0 to finally be written to a table.
I am wondering how can this be achieved by, for starters, removing the need to add 365 days to a date. If I were to do this same process for 10 years intead of one, the task gets ridicolous really quick.
I was wondering about an XOR type of operation, or some other function that would cut the number of steps that need to be done for what I (maybe wrongly) feel is a simple task. Currently I now need 5 steps just to know which dates are missing between two dates, a minimun and one year from that point.
I have tried to be as clear as posible but if I failed at any point please let me know!
Im not sure what the aggregator is supposed to do?
The same with the 'full outer' join? A normal join on a constant port is fine :) c
Can you calculate the needed number of 'dublicates' before the 'joiner'? In that case a lookup configured to return 'all rows' and a less-than-or-equal predicate can help make the mapping much more readable.
In any case You will need a helper table (or file) with a sequence of numbers between 1 and the number of potential dublicates (or more)
I use our time-dimension in the warehouse, which have one row per day from 1753-01-01 and 200000 next days, and a primary integer column with values from 1 and up ...
You've identified you know how to do this programmatically and to be fair this problem is more suited to that sort of solution... but that doesn't exclude powercenter by any means, just feed the 2 dates into a java transformation, apply some code to produce all dates between them and for a record to be output for each. Java transformation is ideal for record generation
You've identified you know how to do this programmatically and to be fair this problem is more suited to that sort of solution... but that doesn't exclude powercenter by any means, just feed the 2 dates into a java transformation, apply some code to produce all dates between them and for a record to be output for each. Java transformation is ideal for record generation
Ok... so you could override your source qualifier to achieve this in the selection query itself (am giving Oracle based example as its what I'm used to and I'm assuming your data in is from a table). I looked up the connect syntax here
SQL to generate a list of numbers from 1 to 100
SELECT (MIN(tablea.DATEFIELD) + levquery.n - 1) AS Port1 FROM tablea, (SELECT LEVEL n FROM DUAL CONNECT BY LEVEL <= 365) as levquery
(Check if the query works for you - haven't access to pc to test it at the minute)
Tough problem I am working on here.
I have a table of CustomerIDs and CallDates. I want to measure whether there is a 'repeat call' within a certain period of time (up to 30 days).
I plan on creating a parameter called RepeatTime which is a range from 0 - 30 days, so the user can slide a scale to see the number/percentage of total repeats.
In Excel, I have this working. I sort CustomerID in order and then sort CallDate from earliest to latest. I then have formulas like:
=IF(AND(CurrentCustomerID = FutureCustomerID, FutureCallDate - CurrentCallDate <= RepeatTime), 1,0)
CurrentCustomerID = the current row, and the FutureCustomerID = the following row (so it is saying if the customer ID is the same).
FutureCallDate = the following row and the CurrentCallDate = the current row. It is subtracting the future call time from the first call time to measure the time in between.
The goal is to be able to see, dynamically, how many customers called in for a specific reason within maybe 4 hours or 1 day or 5 days, etc. All of the way up until 30 days (this is our actual metric but it is good to see the calls which are repeats within a shorter time frame so we can investigate).
I had a similar problem, see here for detailed version Array calculation in Tableau, maxif routine
In your case, that is basically the same thing as mine, so you could apply that solution, but I find it easier to understand the one I'm about to give, I would do:
1) Create a calculated field called RepeatTime:
DATEDIFF('day',MAX(CallDates),LOOKUP(MAX(CallDates),-1))
This will calculated how many days have passed since the last call to the current. You can add a IFNULL not to get Null values for the first entry.
2) Drag CustomersID, CallDates and RepeatTime to the worksheet (can be on the marks tab, don't need to be on rows or column).
3) Configure the table calculation of RepeatTIme, Compute using Advanced..., partitioning CustomersID, Adressing CallDates
Also Sort by Field CallDates, Maximum, Ascending.
This will guarantee the table calculation works properly
4) Now you have a base that you can use for what you need. You can either export it to csv or mdb and connect to it.
The best approach, actually, is to have this RepeatTime field calculated outside Tableau, on your database, so it's already there when you connect to it. But this is a way to use Tableau to do the calculation for you.
Unfortunately there's no direct way to do this directly with your database.