Imagine we system design situation - car rental service like zoom car. Here users can search cars in the particular date range. like user A comes and search between 27 March and 30 March. here his intention is booking for 3 days.
How we will persist this range booking in DB.Like entity structure. for example, where suppose we save a booking in this form:
class CarBooking{
Car car;
User user;``
Date startDate;
Date endDate;
Date booking date;
}
But Now another comes and want to search for available cars in date 28 March to 1 April . How will be managing this search logic? Like, will be search booking for each date (28 to 1) and see how many cars are not booked for all range?
Or some other entity structure search logic is needed
Its theoretical question discussing best design
Related
I want to know if it is possible to have 2 date range controls on my page. Each date range control would be connected to a different date (Purchase date / Consumption date) of our products).
Here is a simplified editable copy of the data studio report.
The Google Sheet source looks like:
ID
Purchase date
Consumption date
Product
Price
ABCD12
21/03/2022
09/11/2022
A
£50
EFGH34
22/03/2022
22/11/2022
B
£80
IJKL56
23/04/2022
15/11/2022
A
£50
MNOP78
24/03/2022
06/12/2022
A
£50
The output I'm looking for is to be able to filter data so that I can answer the question "how many products were purchased in March 2022 that have a consumption date in November 2022". The expected output is as follows:
ID
Purchase date
Consumption date
Product
Price
ABCD12
21/03/2022
09/11/2022
A
£50
EFGH34
22/03/2022
22/11/2022
B
£80
Supermetrics has a Date Picker that essentially does what I need it to do. But it has 2 downsides 1) it is bulky and does not work well with many years of data and 2) It does not allow breaking down to more than a monthly level.
Is there another way to make this happen with parameters?
Through this post I've gotten as far as getting a 'switch' for my graphs and tables between the two date datapoints, but that is not the solution I'm looking for.
actually you did find already a good solution by the 3rd party Add-one "Date Picker" from Supermetrics. An alternative route is to include two tables which only have the consumption date as a column. The user can then select these and do a cross filtering of the main table.
In the first table, the dimension has to be changed to "Year Month":
An alternative community visualisation to the Date Picker (based on the limitations cited in the question) would be the Range Slider.
Two Range Sliders could be used (one for each date field), however, the below will use one Date range control and one Range Slider to demonstrate that they can work together (as well as maintaining the original setup in the question):
1) Purchase date
1.1) Date range control
1.2) Table
Date Range Dimension: Purchase date
Dimension 1: ID
Dimension 2: Purchase date
Dimension 3: Consumption date
Dimension 4: Product
Metric: Price
2) Consumption date
2.1) Range Slider
Column to filter on: Consumption date
(Chart Interactions) Cross Filtering: ☑
Publicly editable Google Data Studio report (embedded Google Sheets data source) and a GIF to elaborate:
I have a list of guests that come to my events. The list contains 10 years worth of events and guests. I want to know how many new guests are brand new each year, such that they've never attended my events before. How do I calculate this in the calculated field?
For example, John attended my event in 2011, 2012, and 2013. This means John was my new guest in 2011, but not 2012 or 2013. Therefore, the count of new guests in 2011 increments by one.
It is safe to assume that John uses the same email each time he registers for my event. Therefore, my resulting table should look like this:
Email
First Year of Attendance
johndoe#live.com
2011
0) Summary
Use EITHER #1 OR #2
#1: Use if two fields are required (Email and First_Year)
#2: Use if three fields are required (Email, Year and First_Year)
1) Aggregate Year by MIN
If the goal is only to show the 2 fields in the Table, then simply add the Year field as a Metric and aggregate by MIN:
2) Data Blending
To display all three fields in a single Table, one approach is using a Self Data Blend; the below does the trick where Email represents the field with Emails (such as johndoe#live.com) and Year the field with Year values, which can be a Number field (for example 2011) or a Date field (such as 01 Jan 2011):
2.1) Data Blending
Data Source 1
Join Key: Email
Dimension: Year
Data Source 2
Join Key: Email
Metric: First_Year; Rename: Year; Aggregation: MIN
2.2) Table
Data Source: Blended Data Source
Dimension 1: Email
Dimension 2: First_Year
Dimension 3: Year
Editable Google Data Studio Report (Embedded Google Sheets Data Source) and a GIF to elaborate:
all:
I am trying to design a shared worksheet that measures salespeople performance over a period of time. In addition to calculating # of units, sales price, and profit, I am trying to calculate how many new account were sold in the month (ideally, I'd like to be able to change the timeframe so I can calculate larger time periods like quarter, year etc').
In essence, I want to find out if a customer was sold to in the 12 months before the present month, and if not, that I will see the customer number and the salesperson who sold them.
So far, I was able to calculate that by adding three columns that each calculate a part of the process (see screenshot below):
Column H (SoldLastYear) - Shows customers that were sold in the year before this current month: =IF(AND(B2>=(TODAY()-365),B2<(TODAY()-DAY(TODAY())+1)),D2,"")
Column I (SoldNow) - Shows the customers that were sold this month, and if they are NOT found in column H, show "New Cust": =IFNA(IF(B2>TODAY()-DAY(TODAY()),VLOOKUP(D2,H:H,1,FALSE),""),"New Cust")
Column J (NewCust) - If Column I shows "New Cust", show me the customer number: =IF(I2="New Cust",D2,"")
Column K (SalesName) - if Column I shows "New Cust", show me the salesperson name: =IF(I2="New Cust",C2,"")
Does anyone have an idea how I can make this more efficient? Could an array formula work here or will it be stuck in a loop since its referring to other lines in the same column?
Any help would be appreciated!!
EDIT: Here is what Im trying to achieve:
Instead of:
having Column H showing me what was sold in the 12 months before the 1st day of the current month (for today's date: 8/1/19-7/31/20);
Having Column I showing me what was sold in August 2020; and
Column I searching column H to see if that customer was sold in the timeframe specified in Column H
I want to have one column that does all three: One column that flags all sales made for the last 12 months from the beginning of the current month (so, 8/1/19 to 8/27/20), then compares sales made in current month (august) with the sales made before it, and lets me know the first time a customer shows up in current month IF it doesn't appear in the 12 months prior --> finds the new customers after a dormant period of 12 months.
Im really just trying to find a way to make the formula better and less-resource consuming. With a large dataset, the three columns (copied a few times for different timeframes) really slow down Excel...
Here is an example of the end result:
Example of final product
I use a DATE's master table for looking up dates and other values in order to control several events, intervals and calculations within my app. It has rows for every single day begining from 01/01/1990 to 12/31/2041.
One example of how I use this lookup table is:
A customer pawned an item on: JAN-31-2010
Customer returns on MAY-03-2010 to make an interest pymt to avoid forfeiting the item.
If he pays 1 months interest, the employee enters a "1" and the app looks-up the pawn
date (JAN-31-2010) in date master table and puts FEB-28-2010 in the applicable interest
pymt date. FEB-28 is returned because FEB-31's dont exist! If 2010 were a leap-year, it
would've returned FEB-29.
If customer pays 2 months, MAR-31-2010 is returned. 3 months, APR-30... If customer
pays more than 3 months or another period not covered by the date lookup table,
employee manually enters the applicable date.
Here's what the date lookup table looks like:
{ Copyright 1990:2010, Frank Computer, Inc. }
{ DBDATE=YMD4- (correctly sorted for faster lookup) }
CREATE TABLE datemast
(
dm_lookup DATE, {lookup col used for obtaining values below}
dm_workday CHAR(2), {NULL=Normal Working Date,}
{NW=National Holiday(Working Date),}
{NN=National Holiday(Non-Working Date),}
{NH=National Holiday(Half-Day Working Date),}
{CN=Company Proclamated(Non-Working Date),}
{CH=Company Proclamated(Half-Day Working Date)}
{several other columns omitted}
dm_description CHAR(30), {NULL, holiday description or any comments}
dm_day_num SMALLINT, {number of elapsed days since begining of year}
dm_days_left SMALLINT, (number of remaining days until end of year}
dm_plus1_mth DATE, {plus 1 month from lookup date}
dm_plus2_mth DATE, {plus 2 months from lookup date}
dm_plus3_mth DATE, {plus 3 months from lookup date}
dm_fy_begins DATE, {fiscal year begins on for lookup date}
dm_fy_ends DATE, {fiscal year ends on for lookup date}
dm_qtr_begins DATE, {quarter begins on for lookup date}
dm_qtr_ends DATE, {quarter ends on for lookup date}
dm_mth_begins DATE, {month begins on for lookup date}
dm_mth_ends DATE, {month ends on for lookup date}
dm_wk_begins DATE, {week begins on for lookup date}
dm_wk_ends DATE, {week ends on for lookup date}
{several other columns omitted}
)
IN "S:\PAWNSHOP.DBS\DATEMAST";
Is there a better way of doing this or is it a cool method?
This is a reasonable way of doing things. If you look into data warehousing, you'll find that those systems often use a similar system for the time fact table. Since there are less than 20K rows in the fifty-year span you're using, there isn't a huge amount of data.
There's an assumption that the storage gives better performance than doing the computations; that most certainly isn't clear cut since the computations are not that hard (though neither are they trivial) and any disk access is very slow in computational terms. However, the convenience of having the information in one table may be sufficient to warrant having to keep track of an appropriate method for each of the computed values stored in the table.
It depends on which database you are using. SQL Server has horrible support for temporal data and I almost always end up using a date fact table there. But databases like Oracle, Postgres and DB2 have really good support and it is typically more efficient to calculate dates on the fly for OLTP applications.
For instance, Oracle has a last_day() function to get the last day of a month and an add_months() function to, well, add months. Typically in Oracle I'll use a pipelined function that takes start and end dates and returns a nested table of dates.
The cool way of generating a rowset of dates in Oracle is to use the hierarchical query functionality, connect by. I have posted an example of this usage in another thread.
It gives a lot of flexibility without the PL/SQL overhead of a pipelined function.
OK, so I tested my app using 31 days/month to calculate interest rates & pawnshops are happy with it! Local Law prays as follows: From pawn or last int. pymt. date to 5 elapsed days, 5% interest on principal, 6 to 10 days = 10%, 11 to 15 days = 15%, and 16 days to 1 "month" = 20%.
So the interest table is now defined as follows:
NUMBER OF ELAPSED DAYS SINCE
PAWN DATE OR LAST INTEREST PYMT
FROM TO ACUMULATED
DAY DAY INTEREST
----- ---- ----------
0 5 5.00%
6 10 10.00%
11 15 15.00%
16 31 20.00%
32 36 25.00%
37 41 30.00%
42 46 35.00%
47 62 40.00%
[... until day 90 (forfeiture allowed)]
from day 91 to 999, daily prorate based on 20%/month.
Did something bad happen in the UK on MAR-13 or SEP-1752?
I'm creating a DW that will contain data on financial securities such as bonds and loans. These securities are associated with payment schedules. For example, a bond could pay quarterly, while a mortgage would usually pay monthly (sometimes biweekly). The payment schedule is created when the security is traded and, in the majority of cases, will remain unchanged. However, the design would need to accommodate those cases where it does change.
I'm currently attempting to model this data and I'm having difficulty coming up with a workable design. One of the most commonly queried fields is "next payment date". Users often want to know when a security will pay next. Therefore, I want to make it as easy as possible for them to get the next payment date and amount for each security.
Also, users often run historical queries in which case they'd want the next payment date and amount as of a specific point in time. For example, they may want to look back at 1/31/09 and query the next payment dates (which would usually be in February 2009 for mortgages). It's also common that they want to query a security's entire payment schedule, which might consist of 360 records (30 year mortgage x 12 payments/year).
Since the next payment date and amount would be changing each month or even biweekly, these fields wouldn't seem to fit into a slow-changing dimension very well. It would probably make more sense to use a fact table, but I'm unsure of how to model it. Any ideas would be greatly appreciated.
Next payment date is an example of a "fact-free fact table". There's no measure, just FK's between at least two dimensions: the security and time.
You can denormalize the security to have a type-1 SCD (overwritten with each load) that has a few important "next payment dates".
I think it's probably better, however, to carry a few important payment dates along with the facts. If you have a "current balance" fact table for loans, then you have an applicable date for this balance, and you can carry previous and next payment dates along with the balance, also.
For the whole payment schedule, you have a special fact-free fact table that just has applicable date and the sequence of payment dates on into the future. That way, when the schedule changes, you can pick the payment sequence as of some particular date.
I would use a table (securityid,startdate, paymentevery, period) it could also include enddate, paymentpershare
period would be 1 for days, 2 for weeks, 3 for months, 4 for years.
So for security 1 that started paying weekly on 3/1/2009, then the date changed to every 20 days on 4/2, then weekly after 5/1/2009, then to monthly on 7/1/2009, it would contain:
1,'3/1/2009',1,2
1,'4/2/2009',20,1
1,'5/1/2009',1,2
1,'7/1/2009',1,3
To get the actual dates, I'd use an algorithm like this:
To know the payment dates on security 1 from 3/5/2009 to 5/17/2008:
Find first entry before 3/5 = 3/1
Loop:
Get next date that's after 3/5 and before the next entry (4/2 - weekly) = 3/8
Get next date that's before next the entry (4/2) = 3/15
Get next date that's before next the entry (4/2) = 3/22
Get next date that's before next the entry (4/2) = 3/29
Next date >4/2 switch to next entry:
Loop:
Get next date that's after 4/2 and before the next entry (5/1 - every 20 days) = 4/22
Next date 5/12 is AFTER next entry 5/1, switch to next entry
Loop:
Get next date that's after 5/1 and before the lastdate (5/17 - weekly) = 5/8
Get next date that's before the lastdate = 5/15
Next date > 5/17
The dates between 3/5/2009 and 5/17/2008 would be 3/8,3/15,3/22,3/29,4/22,5/8,5/15
Why not store the next payment date as the amount of days from the date of the current payment?
Further clarification:
There would be a fact for every past payment linked to some date dimension. Each one of these facts will have a field next payment in which will be an integer. The idea is that the date of the current payment + next payment in will be the date of the next payment fact. This should be able to cater for everything.