Hi everyone I'm working on a school project, and for my project I chose to create an ecommerce system that can process recurring orders. This is for my final project, I'll be graduating in May with an associates in computer science.
Keep in mind this is no where a final solution and it's basically a jumping off point for this database design.
A little background on the business processes.
- Customer will order a product, and will specify during checkout whether it is a one time order or a weekly/monthly order.
- Customer will specify a location in which to pick up their order (this location is specific only to the order)
- If the value of the order > 25.00 then it is accepted otherwise it is rejected.
- This will populate the orders_test and order_products_test tables respectively
Person on the back end will have a report generated for deliveries for the day based on these two tables.
They will be able to print it off and it will generate a list of what items go to what location.
Based on the following criteria.
date_of_next_scheduled_delivery = current date
remaining_deliveries > 0
Once they are satisfied with the delivery list they will press "Process Deliveries" button.
This will adjust the order_products_test table as follows
Subtract 1 from remaining_deliveries
Insert current date into date_of_last_delivery_processed
Based on delivery_frequency (i.e. once, weekly, monthly) it will change the date_of_next_scheduled_delivery
status values in the order_products_test table can either be active, hold, or canceled, expired
I just would like some opinions if I am approaching this correctly or if I should scratch this approach and start over again.
A few thoughts, though not necessarily complete (there's a lot to your question, but hopefully these points help):
I don't think you need to keep track of remaining deliveries. You only have 2 options - a one time order, or a recurring order. In both cases, there's no sense in calculating remaining deliveries. It's never leveraged.
In terms of tracking the next delivery date, you can just keep track of the day of the order. If it's recurring -- monthly or weekly, regardless -- everything is calculable from that first date. Most DB systems (MySQL, SQL Server, Oracle, etc) support more than enough date computation flexibility so that you can calculate this on the fly, as opposed to maintaining such a known schedule.
If the delivery location is only specific to the order, I see no use in creating a separate table for it -- it's functionally dependent on the order, you should keep it in the same table as the order. For most e-commerce systems, this is not the case because they tend to associate a list of delivery locations with accounts, which they prompt you about when you order more than once (e.g., Amazon).
Given the above, I bet you can just get away with 2 of your 4 tables above -- Account and Order. But again, if delivery locations are associated with Accounts, I would indeed break that out. (but your question above doesn't suggest that)
Do not name your tables with a "_test" suffix -- it's confusing.
Related
I have an application which calls the database multiple times to achieve one simple goal.
A little information about this application; In short, the application scrapes data from a webpage & stores specific information from this page into a database. The important information in this query is: Player name, Position. There can be multiple sitting at one specific position, kill points & Class
Player name has every potential to change or remain the same every day
Regarding the Position, there can be multiple sitting in one position
Kill points has the potential to increase or remain the same every day
Class, there is only 2 possibilities that a name can be, Ex: A can change to B or remain A (same in reverse), but cannot be C,D,E,F
The player name can change at any particular day, Position can also change dependent on the kill point increase from the last update which spins back around to the goal. This is to search the database day by day, from the current date to as far back as 2021-02-22 starting at the most recent entry for a player name and back track to the previous day to check if that player name is still the same or has changed.
What is being used as a main reference to the change is the kill points. As the days go on, this number will either be the exact same or increase, it can never decrease.
So now onto the implementation of this application.
The first query which runs finds the most recent entry for the player name
SELECT TOP(1) * FROM [changes] WHERE [CharacterName]=#charname AND [Territory]=#territory AND [Archived]=0 ORDER BY [Recorded] DESC
Then continue to check the previous days entries with the following query:
SELECT TOP(1) * FROM [changes] WHERE [Territory]=#territory AND [CharacterName]=#charname AND [Recorded]=#searchdate AND ([Class] LIKE '%{Class}%' OR [Class] LIKE '%{GetOpposite(Class)}%' AND [Archived]=0 )
If no results are found, will then proceed to find an alternative name with the following query:
SELECT TOP(5) * FROM [changes] WHERE [Kills] <= #kills AND [Recorded]='{Data.Recorded.AddDays(-1):yyyy-MM-dd}' AND [Territory]=#territory AND [Mode]=#mode AND ([Class] LIKE #original OR [Class] LIKE #opposite) AND [Archived]=0 ORDER BY [Kills] DESC
The aim of the query above is to get the top 5 entries that are the closest possible matches & Then cross references with the day ahead
SELECT COUNT(*) FROM [changes] WHERE [CharacterName]=#CharacterName AND [Territory]=#Territory AND [Recorded]=#SearchedDate AND [Archived]=0
So with checking the day ahead, if the character name is not found in the day ahead, then this is considered to be the old player name for this specific character, else after searching all 5 of the results and they are all found to be present in the day aheads searches, then this name is considered to be new to the table.
Now with the date this application started to run up to today's date which is over 400 individual queries on the database to achieve one goal.
It is also worth a noting that this table grows by 14,400 - 14,500 Rows each and every day.
The overall question to this specific? Is it possible to bring all these queries into less calls onto the database, reduce queries & improve performance?
What you can do to improve performance will be based on what parts of the application stack you can manipulate. Things to try:
Store Less Data - Database content retrieval speed is largely based on how well the database is ordered/normalized and just how much data needs to be searched for each query. Managing a cache of prior scraped pages and only storing data when there's been a change between the current scrape and the last one would guarantee less redundant requests to the db.
Separate specific classes of data - Separating data into dedicated tables would allow you to query a specific table for a specific character, etc... effectively removing one where clause.
Reduce time between queries - Less incoming concurrent requests means less resource contention and faster response times to prior requests.
Use another data structure - The only reason you're using top() is because you need data ordered in some specific way (most-recent, etc...). If you just used a code data structure that keeps the data ordered and still easily-query-able you could then perhaps offload some sql requests to this structure instead of the db.
The suggestions above are not exhaustive, but what you do to improve performance is largely a function of what in the application stack you have the ability to modify.
I'm a pretty new programmer and I'm working on a project that I'm not sure how to make work. I'm hoping for some advice please.
Part of the project I'm working on will be used by a company to allow employees to sign up for lunch from their computers. I'm doing the project in MVC ASP.NET
The interface will look something like this:
----------------------
|1200 | Employee Dropdown Name 1
| Employee Dropdown Name 2
|---------------------
|1230 | Employee Dropdown Name 1
| Employee Dropdown Name 2
|---------------------
and on and on and on.
With this company, everything has to be recorded and stored. So, I already have a table with employee information. That will populate the drop down areas. Lunch times need to be stored in the database so it can be searched years down the line. So it has to be in a table.
The table get more tricky because not every time of the day is available for lunch (i.e. - no lunches after 0430 and before 0800).
My question is about how to create the future time slots in the database.
I could obviously make the table with all of these rows already in places for several years down the line. That's time-consuming, though, and I'll have to go back in in several years and fix it. Horrible idea.
What I'd LOVE to do is make it so every 24 hours, the database just automatically adds new rows with the next days times available - so just increment (at midnight, the program will just add the next day's times associated with that date (so at midnight on February 6, 2020, it will create February 7, 2020 0000, February 7, 2020 0030, etc. I've studied a lot but I'm still beside myself on how to make this work.
Thanks in advance everyone!!!
As I understand, you want to drive your interface from the database table so that the user can select Name 1 and Name 2 and a time slot and submit.
It sounds like you also want the available timeslots to be driven by the database also (ie, timeslot in table without names with it is availlable). This is not a good idea. As you mentioned, you would be inserting data that is not actually a record but a placeholder. That will be very confusing down the track when you come to query the data.
My approach would be to do the following:
* add NOT NULL constraints to all columns in your database (if your database supports this feature) or have your app complain very much about NULLS in any of the columns. There is no need for NULLS in your use case by the look of it.
the database should have a CHECK constraint that the time is within the allowable time range, and (assuming employees can not double book time slots) a CHECK constraint that there is no overlapping time slots, and also a UNIQUE constraint that ensures no duplicate times.... adjust to suit your needs.
your app populates times between 0800 and 1630 (8AM and 4:30PM) and also query the database for all records matching the current day so those booked slots can be removed from the list of available time slots... adjust to suit.
your app sends the user request of name and time slot to the DB. All the critical requirements are accepted or rejected by the DB schema and if there is something wrong, display an appropriate error in the app.
This way, your database is literally storing records of booked lunches.
I would NOT go down the path of pre inserting as then it becomes more complex as some records are "real" and some are artificially generated records to drive a GUI...
If you can't do the time slot calculations in your app rather than in the DB, then at least use a separate table that is maintained by a worker thread in your app OR if your DB supports it, a Stored Procedure which returns a table of available time slots.
I would use the stored procedure if I was avoiding doing complex time calculations in my app (also avoids need to worry about time zones - if you make sure to only store and display UTC times in your DB).
Having in mind structure like this:
LunchTimeSlots (id, time_slot)
Employee (id, name, preferred_time_slot_id, etc)
Lunches(employee_id, time_slot_id, date)
You need a scheduled job to add records to the "Lunches" table every midnight. How to define the job depends on your database vendor. But most of the popular rdbms have this feature. (f.e. mssql)
Despite it's possible to do what you want with db schedulers or any other scheduler, i would recommend to avoid such db design. It's always better to write real facts to the database like a list of employees or fact that lunch was served
to employee at 1pm today.
Unlike real facts, virtual data can be always generated "on-the-fly" by sql queries. F.e. by joining employees to list of dates from today till year 2100, we can get planned lunches for all employees for next 80 years.
So I'm developing a database for an agency that manages many relief staff.
Relief workers set their availability for each day in one of three categories (day, evening, night).
We also need to be able to set some part-time relief workers as busy on weekly, biweekly, and in one instance, on a 9-week rotation. Since we're already developing recurring patterns of availability here, we might as well also give the relief workers the option of setting recurring availability days.
We also need to be able to query the database, and determine if an employee is available for a given day.
But here's the gotcha - we need to be able to use change data capture. So I'm not sure if calculating availability is the best option.
My SQL prototype table looks like this:
TABLE Availability Day
employee_id_fk | workday (DATETIME) | day | eve | night (all booleans)| worksite_code_fk (can be null)
I'm really struggling how to wrap my head around recurring events. I could create say, a years worth, of availability days following a pattern in 'x' day cycle. But how far ahead of time do we store information? I can see running into problems when we reach the end of the data set.
I was thinking of storing say, 6 months of information, then adding a server side task that runs monthly to keep the tables updated with 6 months of data, but my intuition is telling me this is a bad fix.
For absolutely flexibility in the future and keeping data from bloating my first thought would be something like
Calendar Dimension Table - Make it for like 100 years or Whatever you Want make it include day of week information etc.
Time Dimension Table - Hour, Minutes, every 15 what ever but only for 24 hour period
Shifts Table - 1 record per shift e.g. Day, Evening, and Night
Specific Availability Table - Relationship to Calendar & Time with Start & Stops recommend 1 record per day so even if they choose a range of 7 days split that to 1 record perday and 1 record per shift.
Recurring Availability Table - for day of week (1-7),Month,WeekOfYear, whatever you can think of. But again I am thinking 1 record per value so if they are available Mondays and Tuesday's that would be 2 rows. and if multiple shifts then it would be multiple rows.
Now and here is the perhaps the weird part, I would put a Available Column on the Specific and Recurring Availability Tables, maybe make it a tiny int and store something like 0 not available, 1 available, 2 maybe available, 3 available with notice.
If you want to take into account Availability with Notice you could add columns for that too such as x # of days. If you want full flexibility maybe that becomes a related table too.
The queries would be complex but you could use a stored procedure or a table valued function to handle it fairly routinely.
I need to scheduled events, tasks, appointments, etc. in my DB. Some of them will be one time appointments, and some will be reoccurring "To-Dos" which must be checked off. After looking a google's calendar layout and others, plus doing a lot of reading here is what I have so far.
Calendar table (Could be called schedule table I guess): Basic_Event Title, start/end, reoccurs info.
Calendar occurrence table: ties to schedule table, occurrence specific text, next occurrence date / time????
Looked here at how SQL Server does its jobs: http://technet.microsoft.com/en-us/library/ms178644.aspx
but this is slightly different.
Why two tables: I need to track status of each instance of the reoccurring task. Otherwise this would be much simpler...
so... on to the questions:
1) Does this seem like the proper way to go about it? Is there a better way to handle the multiple occurrence issue?
2) How often / how should I trigger creation of the occurrences? I really don't want to create a bunch of occurrences... BUT... What if the user wants to view next year's calendar...
Makes sense to have your schedule definition for a task in one table and then a separate table to record each instance of that separately - that's the approach I've taken in the past.
And with regards to creating the occurrences, there's probably no need to create them all up front. Especially when you consider tasks that repeat indefinitely! Again, the approach I've used in the past is to only create the next occurrence. When that instance is actioned, the next instance is then calculated and created.
This leaves the issue of viewing future occurrences. For this, you can start of with the initial/next scheduled occurrence and just calculate the future occurrences on-the-fly at display time.
While this isn't an exact answer to your question I've solved this problem before in SQL Server (though database here is irrelevant) by modeling a solution based on Unix's cron.
Instead of string parsing we used integer columns in a table to store the various time units.
We had events which could be scheduled; they could either point to a one-time schedule table that represented a distinct point in time (a date/time) or to the recurring schedule table which is modelled after cron.
Additionally remember to model your solution correctly. An event has a duration but the duration is unrelated to the schedule (but an event's duration may impact the schedule by causing conflicts). Do not try to model duration as part of your schedule.
In the past when we've done this, we had 2 tables:
1) Schedules -> Includes recurrence information
2) Exceptions -> Edit/changes to specific instances
Using SQL, it's possible to get the list of "Schedules" that have at least one instance in a given date range. Then you can expand in the GUI where each instance lies.
Lets say I have a website that sells widgets. I would like to do something similar to a tag cloud tracking best sellers. However, due to constantly aquiring and selling new widgets, I would like the sales to decay on a weekly time scale.
I'm having problems puzzling out how store and manipulate this data and have it decay properly over time so that something that was an ultra hot item 2 months ago but has since tapered off doesn't show on top of the list over the current best sellers. What would be the logic and database design for this?
Part 1: You have to have tables storing the data that you want to report on. Date/time sold is obviously key. If you need to work in decay factors, that raises the question: for how long is the data good and/or relevant? At what point in time as the "value" of the data decayed so much that you no longer care about it? When this point is reached for any given entry in the database, what do you do--keep it there but ensure it gets factored out of all subsequent computations? Or do you archive it--copy it to a "history" table and delete it from your main "sales" table? This is relevant, as it has to be factored into your decay formula (as well as your capacity planning, annual reporting requirements, and who knows what all else.)
Part 2: How much thought has been given to the decay formula that you want to use? There's no end of detail you can work into this. Options and factors to wade through include but are not limited to:
Simple age-based. Everything before the cutoff date counts as 1; everything after counts as 0. Sum and you're done.
What's the cutoff date? Precisly 14 days ago, to the minute? Midnight as of two Saturdays ago from (now)?
Does the cutoff date depend on the item that was sold? If some items are hot but some are not, does that affect things? What if you want to emphasize some things (the expensive/hard to sell ones) over others (the fluff you'd sell anyway)?
Simple age-based decays are trivial, but can be insufficient. Time to go nuclear.
Perhaps you want some kind of half-life, Dr. Freeman?
Everything sold is "worth" X, where the value of X is either always the same or varies on the item sold. And the value of X can decay over time.
Perhaps the value of X decreased by one-half every week. Or ever day. Or every month. Or (again) it may vary depending on the item.
If you do half-lifes, the value of X may never reach zero, and you're stuck tracking it forever (which is why I wrote "part 1" first). At some point, you probably need some kind of cut-off, some point after which you just don't care. X has decreased to one-tenth the intial value? Three months have passed? Either/or but the "range" depends on the inherent valud of the item?
My real point here is that how you calculate your decay rate is far more important than how you store it in the database. So long as the data's there that the formalu needs to do it's calculations, you should be good. And if you only need the last month's data to do this, you should perhaps move everything older to some kind of archive table.
you could just count the sales for the last month/week/whatever, and sort your items according to that.
if you want you can always add the total amonut of sold items into your formula.
You might have a table which contains the definitions of the pointing criterion (most sales, most this, most that, etc.), then for a given period, store in another table the attribution of points for each of the criterion defined in the criterion table. Obviously, a historical table will be used to store the score for each sellers for a given period or promotion, call it whatever you want.
Does it help a little?