I am working on a multiple properties booking system and making me headache about the best practice schema design. Assume the site hosts for example 5000 properties where each of it is maintained by one user. Each property has a booking calendar. My current implementation is a two-table-system with one table for the available dates and the other for the unavailable dates, with a granularity of 1 day each.
property_dates_available (property_id, date);
property_dates_booked (property_id, date);
However, i feel unsure if this is a good solution. In another question i read about a single table solution with both states represented. But i wonder if it is a good idea to mix them up. Also, should the booking calendar be mapped for a full year with all its 365 days per year into the database table or was it better to map only the days a property is available for booking? I think of the dramatically increasing number of rows every year. Also i think of searching the database lately for available properties and am not sure if looking through 5000 * 365 rows might be a bad idea compared to i.e. only 5000 * av. 100 rows.
What would you generally recommend? Is this aspect ignorable? How to best practice implement this?
I don't see why you need a separate table for available dates. If you have a table for booked dates (property_id, date), then you can easily query this table to find out which properties are available for a given date
select properties.property_name
from properties where not exists
(select 1 from property_dates_booked
where properties.property_id = property_dates_booked
and property_dates_booked.date = :date)
:date being a parameter to the query
Only enter actual bookings into the property_dates_booked table (it would be easier to rename the table 'bookings'). If a property is not available for certain dates because of maintenance, then enter a booking for those dates where the customer is 'special' (maybe the 'customer' has a negative id).
Related
I am trying to create a business hours application using DynamoDB.
I saw lots of examples and schema designs for different databases but just can't find the right table design for DynamoDB.
Here are my requirements:
every business should have default working hours (Monday 08:00 - 14:00, 16:00 - 20:00) and special events (26/11/2020 shop is closed / opened between 10:00 - 14:00 due to Thanksgiving)
every day can have multiple work durations (08:00 - 14:00, 16:00 - 20:00)
Those are the operations I need to allow:
Create / edit working hours for each business (including special events)
Check whether a business (or a list of businesses) are open right now - by providing a list of business ids.
Get business (or list of businesses) working hours between 2 dates (for example 23/04/2020 - 25/04/2020) by providing a list of business ids and date range for each id
What I've tried:
Defined a table where business id is the partition key (HASH) and special dates / day of week is the sort key (RANGE).
The problem with this approach is that I cannot query by multiple business hours unless I use the scan api which is not recommended due to expensive operations.
Please advice what kind of table design I should use for this application.
You probably need to first construct your overarching logic outside of DynamoDB, do decide if a business is working or not, and only use quarries in Dynamo for a subset of that logic.
Lets say though we use DynamoDB for querying in regards to normal working hours, and not include logic like holidays and special cases, you can use that to filter after you access Dynamo. You can't construct one query in Dynamo to answer all your questions that is more like what you can do in SQL.
So lets say we have a Table/Subset of values which relate to the normal working day. So you have something like this:
Partition Key (PK): business, Range Key (RK): dayOfWeek, and attributes, opens & closes.
We can then create 2 GSIs:
PK dayOfWeek RK opens
PK dayOfWeek RK closes
Now we can do two queries if a store is open between 3-4pm on Monday:
PK == MONDAY & opens < 2 pm
PK == MONDAY & closes > 4 pm
And collect only the values which appear in both queries.
Obviously though, having a PK of day, is probably not a great idea, as you will only have 7 partitions. So what do you do? Well you probably have more criteria in your query than simply day, for example, the type of store, the city the store is located it, etc. That would mean then you would have a PK of something like: city-category-dayOfWeek.
Similarly on the sorting side, you might want higher rated stores to be the first option, so you might have something like: {rating}-{open} & {rating}-{closes}.
You will just have to get creative, firstly layout all the queries you have before you design your tables. I really like this video on table design, it's terrific.
I am currently working on a web application that stores information of Cooks in the user table. We have a functionality to search the cooks from our web application. If a cook is not available on May 3, 2016, we want to show the Not-Bookable or Not-Available message for that cook if user performs the search for May 3, 2016. The solution we have come up to is to create a table named CooksAvailability with following fields
ID, //Primary key, auto increment
IDCook, //foreign key to user's table
Date, //date he is available on
AvailableForBreakFast, //bool field
AvailableForLunch, //bool field
AvailableForDinner, //book field
BreakFastCookingPrice, //decimal nullable
LunchCookingPrice, //decimal nullable
DinnerCookingPrice //decimal nullable
With this schema, we are able to tell if the user is available for a specific date or not. But the problem with this approach is that it requires a lot of db space i.e if a cook is available for 280 days/year, there has to be 280 rows to reflect just one cook's availability.
This is too much space given the fact that we may have potentially thousands of cooks registered with our application. As you can see the CookingPrice fields for breakfast, lunch and dinner. it means a cook can charge different cooking rates for cooking on different dates and times.
Currently, we are looking for a smart solution that fulfils our requirements and consumes less space than our solution does.
You are storing a record for each day and the main mistake, which led you to this redundant design was that you did not separate the concepts enough.
I do not know whether a cook has an expected rate for a given meal, that is, a price one can assume in general if one has no additional information. If that is the case, then you can store these default prices in the table where you store the cooks.
Let's store the availability and the specific prices in different tables. If the availability does not have to store the prices, then you can store availability intervals. In the other table, where you store the prices, you need to store only the prices which deviate from the expected price. So, you will have defined availability intervals in a table, specific prices when the price differs from the expected one in the oter and default meal price values in the cook table, so, if there is no special price, the default price will be used.
To answer your question I should know more about the structure of the information.
For example if most cooks are available in a certain period, it could be helpful to organize your availability table with
avail_from_date - avail_to_date, instead of a row for each day.
this would reduce the amount of rows.
The different prices for breakfast, lunch and dinner could be stored better in the cooks table, if the prices are not different each day. Same is for the a availability for breakfast, lunch and dinner if this is not different each day.
But if your information structure makes it necessary to keep a record for every cook every day this would be 365 * 280 = 102,200 records for a year, this is not very much for a sql db in my eyes. If you put the indexes at the right place this will have a good performance.
There are a few questions that would help with the overall answer.
How often does availability change?
How often does price change?
Are there general patterns, e.g. cook X is available for breakfast and lunch, Monday - Wednesday each week?
Is there a normal availability / price over a certain period of time,
but with short-term overrides / differences?
If availability and price change at different speeds, I would suggest you model them separately. That way you only need to show what has changed, rather than duplicating data that is constant.
Beyond that, there's a space / complexity trade-off to make.
At one extreme, you could have a hierarchy of configurations that override each other. So, for cook X there's set A that says they can do breakfast Monday - Wednesday between dates 1 and 2. Then also for cook X there's set B that says they can do lunch on Thursday between dates 3 and 4. Assuming that dates go 1 -> 3 -> 4 -> 2, you can define whether set B overrides set A or adds to it. This is the most concise, but has quite a lot of business logic to work through to interpret it.
At the other extreme, you just say for cook X between date 1 and 2 this thing is true (an availability for a service, a price). You find all things that are true for a given date, possibly bringing in several separate records e.g. a lunch availability for Monday, a lunch price for Monday etc.
I'm trying to create a schema that will allow me to define times, when a supplier website is non operational (planned not unplanned).
I've gone for non-operational as opposed to operational because many suppliers work 24/7, so non-operting times represent the least number of rows.
For example, a supplier might not work:
On a Sunday
On a recognised holiday date - '1/1/2015'
On a Saturday after 5pm
I'm not overly confident with SQL Server, but have come up with a schema that 'does the job'. However, as we all know, there are good ways, not so good ways, and bad ways, that all work in a fashion, so would appreciate comments and advice on what I have to date.
One of the key features is to use data from WorkingDays and Holidays together to represent a WorkingPeriod entity.
I would appreciate coments no matter how small.
Holiday
Contains all recognised holidays - Easter Monday, Good Friday etc.
HolidayDate
Contains dates of holidays. For instance, this year Easter Monday is 6th Apr 2015.
WorkingDay
Sunday through to Monday, mapped to Asp.Net day of week enums.
WorkingPeriodType
A lookup table containing 2 rows - Holiday, or Day of Week
WorkingPeriod
Merges the Holiday table and the WorkingDay table to represent a single WorkingPeriod entity that can be used in the SupplierNonWorkingTimes table.
SupplierNonWorkingTimes
Contains the ID representing the WorkingDay/Holiday and the times of non- operation.
This is a very subjective question, as you've already observed there's no right and wrong, just different ways. I'm a database guy but I don't know your specific circumstances, so this is just some observations - you'll have to judge for yourself whether any of them are appropriate to you.
I like my naming to be crystal clear, it saves all the
misunderstanding by other people later on. If [WorkingDay] holds the
7 days of the week I would call it [WeekDay]. If you intend
[Holiday] to hold whole-day holidays I would call it [HolidayDay].
The main table [SupplierNonWorkingTime] is about 'non-working' so I
would call the [WorkingPeriod] table [NonWorkingPeriod]. The term
'period' always refers to a whole day, so I would replace 'period'
with 'day' (let's ignore start/stop time for now).
My first impression was that your design is over-normalised. The
[WorkingPeriodType] table has 2 rows that will never change,
[WorkingDay] has 7. For these very low numbers I sometimes prefer a
char(1) with a check constraint. Normalisation is generally good,
but lots of JOINs for trivial queries is not so good. You could
eliminate [WorkingPeriodType] and [WorkingDay] but you've mentioned
.Net enums in your question so if you've got some sort of ORM in
your .Net code this level of normalisation might be right for you.
I'd add a Year field to the [HolidayDate] table, then the PK
becomes a better HolidayID+Year - unless you know somewhere that has
lots of Christmas' :)
I'd add an IsAllDay field to the [SupplierNonWorkingTime] table,
otherwise you have to use 'magic values' to represent 'all day' and
magic values are bad. There should be a check constraint to enforce
start/stop times can only be entered if IsAllDay = false.
Like I said, just my thoughts, hope it's helpful.
I have a large database which will only be updated once a year. Every year of data will use the same schema (the data will not be adding any new variables). There's a 'main' table where most of the customer information lives. To keep track of what happens from year to year, is it better design to put a field in the main customer table that says what year it is, or have a 'year' table that relates to the main customer table?
I recommend having a year field in the customer table, that way it is all together. You could even use a timestamp to automatically input the date of user sign up.
To really answer, we'd need to see your schema, but it is almost never the right choice to make a new table for a new year. You probably want to relate years to customers.
Usually you would split off your archive data because you are doing OLTP stuff on your current data, because you want to mostly work on current data, and sometimes look at old stuff. But you have very few updates it seems. I guess the main driver is your queries, and what they 'usually' do, and what performance you need to get out of them. Its probably easier for you to have everything in one table - with a year column. But if most of your queries are for the current year, and they are tight on performance you may want to look at splitting the current data out - either using physical tables, or partitioning of the table (depending on the DB some can do this for you, whilst still being a single table)
I'm writing an application that stores different types of records by user and day. These records are divided in categories.
When designing the database, we create a table User and then for each record type we create a table RecordType and a table Record.
Example:
To store data related to user events we have the following tables:
Event EventType
----- ---------
UserId Id
EventTypeId Name
Value
Day
Our boss pointed out (with some reason) that we're gonna store a lot of rows ( Users * Days ) and suggested an idea that seems a little crazy to me: Create a table with a column for each day of the year, like so:
EventTypeId | UserId | Year | 1 | 2 | 3 | 4 | ... | 365 | 366
This way we only have 1 row per user per year, but we're gonna get pretty big rows.
Since most ORMs (we're going with rails3 for this project) use select * to get the database records, aren't we optimizing something to "deoptimize" another?
What's the community thoughs about this?
This is a violation of First Normal Form. It's an example of repeating groups across columns.
Example of why this is bad: Write a query to find which day a given event occurred. You'll need a WHERE clause with 366 terms, separated by OR. This is tedious to write, and impossible to index.
Relational databases are designed to work well even if you have a lot of rows. Say you have 10000 users, and on average every user generates 10 events every day. After 10 years, you will have 10000*366*10*10 rows, or 366,000,000 rows. That's a fairly large database, but not uncommon.
If you design your indexes carefully to match the queries you run against this data, you should be able to have good performance for a long time. You should also have a strategy for partitioning or archiving old data.
That's breaks the DataBase normal forms principles
http://databases.about.com/od/specificproducts/a/normalization.htm
if it's applicable why don't you replace Day columns with a DateTime column in your event table with a default value (GetDate() we are talking about SQL)
then you could group by Date ...
I wouldn't do it. As long as you take the time to index the table appropriately, the database server should work well with tables that have lots of rows. If it's significantly slowing down your database performance, I'd start by making sure your queries aren't forcing a lot of full table scans.
Some other potential problems I see:
It probably will hurt ORM performance.
It's going to create maintainability problems on down the road. You probably don't want to be working with objects that have 366 fields for every day of the year, so there's probably going to have to be a lot of boilerplate glue code to keep track of.
Any query that wants to search against a range of dates is going to be an unholy mess.
It could be even more wasteful of space. These rows are big, and the number of rows you have to create for each customer is going to be the sum of the maximum number of times each different kind of event happened in a single day. Unless the rate at which all of these events happens is very constant and regular, those rows are likely to be mostly empty.
If anything, I'd suggest sharding the table based on some other column instead if you really do need to get the table size down. Perhaps by UserId or year?