I'm not that experienced with databases. If I have a database table containing a lot of empty cells, what's the best way to leave them (e.g. so performance isn't degraded, memory is not consumed, if this is even possible)?
I know there's a "null" value. Is there a "none" value or equivalent that has no drawbacks? Or by just not filling the cell, it's considered empty, so there's nothing left to do? Sorry if it's silly question. Sometimes you don't know what you don't know...
Not trying to get into a discussion of normalizing the database. Just wondering what the conventional wisdom is for blank/empty/none cells.
Thanks
The convention is to use null to signify a missing value. That's the purpose of null in SQL.
Noted database researcher C. J. Date writes frequently about his objections to the handling of null in SQL at a logical level, and he would say any column that may be missing belongs in a separate table, so that the absence of a row corresponds to a missing value.
I'm not aware of any serious efficiency drawbacks of using null. Efficiency of any features depend on the specific database implementation you use. You haven't said if you use MySQL, Oracle, Microsoft SQL Server, or other.
MySQL's InnoDB storage engine, for example, doesn't store nulls among the columns of a row, it just stores the non-null columns. Other databases may do this differently. Likewise nulls in indexes should be handled efficiently, but it varies from product to product.
Use NULL. That's what it's for.
Normally databases are said to have rows and columns. If the column does not require a value, it holds nothing (aka NULL) until it is updated with a value. That is best practice for most databases, though not all databases have the NULL value--some use an empty string, but they are the exception.
With regard to space utilization -- disk is relative inexpensive these days, so worries about space consumption are no longer as prevalent as they once used to be, except in gargantuan databases, perhaps. You can get better performance out of a database if you use all fixed-size datatypes, but once you start allowing variable sized string (e.g. varchar, nvarchar) types, that optimization is no longer possible.
In brief, don't worry about performance for the time being, at least until you get your feet wet.
It is possible, but consider:
Are they supposed to be not-empty? Should you implement not null?
Is it a workflow -- so they are empty now, but most of them will be filled in the future?
If both are NO, then you may consider re-design. Edit your question and post the schema you have now.
There are several schools of thought in this. The first is to use null when the data is not known - that's what it's for.
The second is to not allow nulls and either separate out all the fields that could be null to relational tables or to create "fake" values to replace null. For varchar this would usually be the empty string but the problem arises as to what should be the fake value for a date field or or an numeric. Then you have to write code to exclude the fake data just like you have to write code to deal with the nulls.
Personally I prefer to use nulls with some judicious moving of data to child tables if the data is truly a different entity (and often these fields turn out to need the one-to-many structure of a parent-child relationship anyway, such as when you may or may not know the phone number of a person, put it in a separate phone table and then you will often discover you needed to store multiple phone numbers anyway).
Related
Imagine a web form with a set of check boxes (any or all of them can be selected). I chose to save them in a comma separated list of values stored in one column of the database table.
Now, I know that the correct solution would be to create a second table and properly normalize the database. It was quicker to implement the easy solution, and I wanted to have a proof-of-concept of that application quickly and without having to spend too much time on it.
I thought the saved time and simpler code was worth it in my situation, is this a defensible design choice, or should I have normalized it from the start?
Some more context, this is a small internal application that essentially replaces an Excel file that was stored on a shared folder. I'm also asking because I'm thinking about cleaning up the program and make it more maintainable. There are some things in there I'm not entirely happy with, one of them is the topic of this question.
In addition to violating First Normal Form because of the repeating group of values stored in a single column, comma-separated lists have a lot of other more practical problems:
Can’t ensure that each value is the right data type: no way to prevent 1,2,3,banana,5
Can’t use foreign key constraints to link values to a lookup table; no way to enforce referential integrity.
Can’t enforce uniqueness: no way to prevent 1,2,3,3,3,5
Can’t delete a value from the list without fetching the whole list.
Can't store a list longer than what fits in the string column.
Hard to search for all entities with a given value in the list; you have to use an inefficient table-scan. May have to resort to regular expressions, for example in MySQL:
idlist REGEXP '[[:<:]]2[[:>:]]' or in MySQL 8.0: idlist REGEXP '\\b2\\b'
Hard to count elements in the list, or do other aggregate queries.
Hard to join the values to the lookup table they reference.
Hard to fetch the list in sorted order.
Hard to choose a separator that is guaranteed not to appear in the values
To solve these problems, you have to write tons of application code, reinventing functionality that the RDBMS already provides much more efficiently.
Comma-separated lists are wrong enough that I made this the first chapter in my book: SQL Antipatterns, Volume 1: Avoiding the Pitfalls of Database Programming.
There are times when you need to employ denormalization, but as #OMG Ponies mentions, these are exception cases. Any non-relational “optimization” benefits one type of query at the expense of other uses of the data, so be sure you know which of your queries need to be treated so specially that they deserve denormalization.
"One reason was laziness".
This rings alarm bells. The only reason you should do something like this is that you know how to do it "the right way" but you have come to the conclusion that there is a tangible reason not to do it that way.
Having said this: if the data you are choosing to store this way is data that you will never need to query by, then there may be a case for storing it in the way you have chosen.
(Some users would dispute the statement in my previous paragraph, saying that "you can never know what requirements will be added in the future". These users are either misguided or stating a religious conviction. Sometimes it is advantageous to work to the requirements you have before you.)
There are numerous questions on SO asking:
how to get a count of specific values from the comma separated list
how to get records that have only the same 2/3/etc specific value from that comma separated list
Another problem with the comma separated list is ensuring the values are consistent - storing text means the possibility of typos...
These are all symptoms of denormalized data, and highlight why you should always model for normalized data. Denormalization can be a query optimization, to be applied when the need actually presents itself.
In general anything can be defensible if it meets the requirements of your project. This doesn't mean that people will agree with or want to defend your decision...
In general, storing data in this way is suboptimal (e.g. harder to do efficient queries) and may cause maintenance issues if you modify the items in your form. Perhaps you could have found a middle ground and used an integer representing a set of bit flags instead?
Yes, I would say that it really is that bad. It's a defensible choice, but that doesn't make it correct or good.
It breaks first normal form.
A second criticism is that putting raw input results directly into a database, without any validation or binding at all, leaves you open to SQL injection attacks.
What you're calling laziness and lack of SQL knowledge is the stuff that neophytes are made of. I'd recommend taking the time to do it properly and view it as an opportunity to learn.
Or leave it as it is and learn the painful lesson of a SQL injection attack.
I needed a multi-value column, it could be implemented as an xml field
It could be converted to a comma delimited as necessary
querying an XML list in sql server using Xquery.
By being an xml field, some of the concerns can be addressed.
With CSV: Can't ensure that each value is the right data type: no way to prevent 1,2,3,banana,5
With XML: values in a tag can be forced to be the correct type
With CSV: Can't use foreign key constraints to link values to a lookup table; no way to enforce referential integrity.
With XML: still an issue
With CSV: Can't enforce uniqueness: no way to prevent 1,2,3,3,3,5
With XML: still an issue
With CSV: Can't delete a value from the list without fetching the whole list.
With XML: single items can be removed
With CSV: Hard to search for all entities with a given value in the list; you have to use an inefficient table-scan.
With XML: xml field can be indexed
With CSV: Hard to count elements in the list, or do other aggregate queries.**
With XML: not particularly hard
With CSV: Hard to join the values to the lookup table they reference.**
With XML: not particularly hard
With CSV: Hard to fetch the list in sorted order.
With XML: not particularly hard
With CSV: Storing integers as strings takes about twice as much space as storing binary integers.
With XML: storage is even worse than a csv
With CSV: Plus a lot of comma characters.
With XML: tags are used instead of commas
In short, using XML gets around some of the issues with delimited list AND can be converted to a delimited list as needed
Yes, it is that bad. My view is that if you don't like using relational databases then look for an alternative that suits you better, there are lots of interesting "NOSQL" projects out there with some really advanced features.
Well I've been using a key/value pair tab separated list in a NTEXT column in SQL Server for more than 4 years now and it works. You do lose the flexibility of making queries but on the other hand, if you have a library that persists/derpersists the key value pair then it's not a that bad idea.
I would probably take the middle ground: make each field in the CSV into a separate column in the database, but not worry much about normalization (at least for now). At some point, normalization might become interesting, but with all the data shoved into a single column you're gaining virtually no benefit from using a database at all. You need to separate the data into logical fields/columns/whatever you want to call them before you can manipulate it meaningfully at all.
If you have a fixed number of boolean fields, you could use a INT(1) NOT NULL (or BIT NOT NULL if it exists) or CHAR (0) (nullable) for each. You could also use a SET (I forget the exact syntax).
here is a simple question to which I would like an answer to:
We have a member table. Each member practices one, many or no sports. Initially we (the developers) created a [member] table, a [sports] table and a [member_sports] table, just as we have always done.
However our client here doesn't like this and wants to store all the sports that the member practices in a single varchar column, separated with a special character.
So if:
1 is football
2 is tennis
3 is ping-pong
4 is swimming
and I like swimming and ping-pong, my favourite sports will be stored into the varchar column as:
x3,x4
Now we don't want to just walk up to the client and claim that his system isn't right. We would like to back it up with proof that the operation to fetch the sports from [member_sports] is more efficient than simply storing the fields as a varchar.
Is there any documentation that can back our claims? Help!
Ask your client if they care about storing accurate information1 rather than random strings.
Then set them a series of challenges. First, ensure that the sport information is in the correct "domain". For the member_sports table, that is:
sport_id int not null
^
|--correct type
For their "store everything in a varchar column" solution, I guess you're writing a CHECK constraint. A regex would probably help here but there's no native support for regex in SQL Server - so you're either bodging it or calling out to a CLR function to make sure that only actual int values are stored.
Next, we not only want to make sure that the domain is correct but that the sports are actually defined in your system. For member_sports, that's:
CONSTRAINT FK_Member_Sports_Sports FOREIGN KEY (Sport_ID) references Sports (Sport_ID)
For their "store everything in a varchar column" I guess this is going to be a far more complex CHECK constraint using UDFs to query other tables. It's going to be messy and procedural. Plus if you want to prevent a row from being removed from sports while it's still referenced by any member, you're talking about a trigger on the sports table that has to query every row in members2`.
Finally, let's say that it's meaningless for the same sport to be recorded for a single member multiple times. For member_sports, that is (if it's not the PK):
CONSTRAINT UQ_Member_Sports UNIQUE (Member_ID,Sport_ID)
For their "store everything in a varchar column" it's another horrifically procedural UDF called from a CHECK constraint.
Even if the varchar variant performed better (unlikely since you need to be ripping strings apart and T-SQL's string manipulation functions are notoriously weak (see above re: regex)) for certain values of "performs better", how do they propose that the data is meaningful and not nonsense?
Writing the procedural variants that can also cope with nonsense is an even more challenging endeavour.
In case it's not clear from the above - I am a big fan of Declarative Referential Integrity (DRI). Stating what you want versus focussing on mechanisms is a huge part of why SQL appeals to me. You construct the right DRI and know that your data is always correct (or, at least, as you expect it to be)
1"The application will always do this correctly" isn't a good answer. If you manage to build an application and related database in which nobody ever writes some direct SQL to fix something, I guess you'll be the first.
But in most circumstances, there's always more than one application, and even if the other application is a direct SQL client only employed by developers, you're already beyond being able to trust that the application will always act correctly. And bugs in applications are far more likely than bugs in SQL database engine's implementations of constraints, which have been tested far more times than any individual application's attempt to enforce constraints.
2Let alone the far more likely query - find all members who are associated with a particular sport. A second index on member_sports makes this a trivial query3. No indexes help the "it's somewhere in this string" solution and you're looking at a table scan with no indexing opportunities.
3Any index that has sport_id first should be able to satisfy such a query.
Is it a common practice to use special naming conventions when you're denormalizing for performance?
For example, let's say you have a customer table with a date_of_birth column. You might then add an age_range column because sometimes it's too expensive to calculate that customer's age range on the fly. However, one could see this getting messy because it's not abundantly clear which values are authoritative and which ones are derived. So maybe you'd want to name that column denormalized_age_range or something.
Is it common to use a special naming convention for these columns? If so, are there established naming conventions for such a thing?
Edit: Here's another, more realistic example of when denormalization would give you a performance gain. This is from a real-life case. Let's say you're writing an app that keeps track of college courses at all the colleges in the US. You need to be able to show, for each degree, how many credits you graduate with if you choose that degree. A degree's credit count is actually ridiculously complicated to calculate and it takes a long time (more than one second per degree). If you have a report comparing 100 different degrees, it wouldn't be practical to calculate the credit count on the fly. What I did when I came across this problem was I added a credit_count column to our degree table and calculated each degree's credit count up front. This solved the performance problem.
I've seen column names use the word "derived" when they represent that kind of value. I haven't seen a generic style guide for other kinds of denormalization.
I should add that in every case I've seen, the derived value is always considered secondary to the data from which it is derived.
In some programming languages, eg Java, variable names with the _ prefix are used for private methods or variables. Private means it should not be modified/invoked by any methods outside the class.
I wonder if this convention can be borrowed in naming derived database columns.
In Postgres, column names can start with _, eg _average_product_price.
It can convey the meaning that you can read this column, but don't write it because it's derived.
I'm in the same situation right now, designing a database schema that can benefit from denormalisation of central values. For example, table partitioning requires the partition key to exist in the table. So even if the data can be retrieved by following some levels of foreign keys, I need the data right there in most tables.
Maybe the suffix "copy" could be used for this. Because after all, the data is just a copy of some other location where the primary data is stored. Since it's a word, it can work with all naming conventions, like .NET PascalCase which can be mapped to SQL snake_case, e. g. CompanyIdCopy and company_id_copy. And it's a short word so you don't have to write too much. And it's not an abbreviation so you don't have to spell it or ever wonder what it means. ;-)
I could also think of the suffix "cache" or "cached" but a cache is usually filled on demand and invalidated some time later, which is usually not the case with denormalised columns. That data should exist at all times and never be outdated or missing.
The word "derived" is just a bit longer than "copy". I know that one special DBMS, an expensive one, has a column name limit of 30 characters, so that could be an issue.
If all of the values required to derive the calculation are in the table already, then it is extremely unlikely that you will gain any meaningful (or even measurable) performance benefit by persisting these calculated values.
I realize this doesn't answer the question directly, but it would seem that the premise is faulty: if such conditions existed for the question to apply, then you don't need to denormalize it to begin with.
We have have a table layout with property names in one table, and values in a second table, and items in a third. (Yes, we're re-implementing tables in SQL.)
We join all three to get a value of a property for a specific item.
Unfortunately the values can have multiple data types double, varchar, bit, etc. Currently the consensus is to stringly type all the values and store the type name in the column next to the value.
tblValues
DataTypeName nvarchar
Is there a better, cleaner way to do this?
Clarifications:
Our requirements state that we must add new "attributes" at run time without modifying the db schema
I would prefer not to use EAV, but that is the direction we are headed right now.
This system currently exists in SQL server using a traditional db design, but I can't see a way to fulfill our requirement of not modifying the db schema without moving to EAV.
There are really only two patterns for implementing an 'EAV model' (assuming that's what you want to do):
Implement it as you've described, where you explicitly store the property value type along with the value, and use that to convert the string values stored into the appropriate 'native' types in the application(s) that access the DB.
Add a separate column for each possible datatype you might store as a property value. You could also include a column that indicates the property value type, but it wouldn't be strictly necessary.
Solution 1 is a simpler design, but it incurs the overhead of converting the string values stored in the table into the appropriate data type as needed.
Solution 2 has the benefit of storing values as the appropriate native type, but it will necessarily require more, though not necessarily much more, space. This may be moot if there aren't a lot of rows in this table. You may want to add a check constraint that only allows one non-NULL value in the different value columns, or if you're including a type column (so as to avoid checking for non-NULL values in the different value columns), prevent mismatches between the value stored in the type column and which value column contains the non-NULL value.
As HLGEM states in her answer, this is less preferred than a standard relational design, but I'm more sympathetic to the use of EAV model table designs for data such as application option settings.
Well don't do that! You lose all the values of having datatypes if you do. You can't properly constrain them (and will, I guarantee it, get bad data eventually) and you have to cast them back to the proper type to use in mathematical or date calculations. All in all a performance loser.
Your whole design will not scale well. Read up on why you don't want to use EAV tables in a relational database. It is not only generally slower but unusually difficult to query especially for reporting.
Perhaps a noSQL database would better suit your needs or a proper relational design and NOT an EAV design. Is it really too hard to figure out what fields each table would really need or are your developers just lazy? Are you sacrificing performance for flexibility - a flexibility that most users will hate? Especially when it means bad performance? Have you ever used a database designed that way to try to do anything?
Normally is it good practice to set all database columns as NOT NULL or not ? Justify your answer.
No. It's a good idea to set columns to NULL where appropriate.
I kind of disagree with the "where appropriate" rule. It is actually rather safe to set any column to be NOT NULL; and then later modify the columns to allow NULL values when you need them. On the other hand, if you allow NULL values first and then later decide you don't want to allow them, it can potentially be much more difficult to do this.
It may make your database table/column descriptions quite ugly if you do this excessively, but when in doubt, go ahead and restrict the data.
Relational theory has it that NULL is evil.
However, your question kind of referred to practice.
So, to the extent that you want your practices to conform to the heavenly ideals of theory, yes, avoid NULL as if it were the plague, Cholera and AIDS all-in-one.
To the extent that these crappy implementations called "SQL DBMSs" do not leave you any other choice, yes, (sniff) use them.
EDIT
Someone mentioned "business rules" as the guideline for "appropriateness" in the accepted answer, and some others upvoted that remark. That is total crap. Business rules can always do without NULLs and the only guideline to "appropriateness" is the very deficiencies of any SQL system that makes it a non-relational system to boot.
The inventor of the NULL reference (1965) recently called it his "billion-dollar mistake": https://www.infoq.com/presentations/Null-References-The-Billion-Dollar-Mistake-Tony-Hoare/
Languages such as Scala, SML, and Haskell are non-NULL by default: NULL is called "Option" or "Maybe" and require special syntax and checks.
Since the time databases were invented, allowing NULL by default has been considered more and more dangerous and undesirable. Should databases follow? Probably.
Go with NOT NULL when you can.
I'm a newbie and my answer may be totally asinine, but here's my personal take on the subject.
In my humble opinion, I don't see the problem with allowing ALL fields except primary/foreign keys to be nullable. I know many of you cringed as soon as I said that, and I'm sure I heard someone cry out, "Heretic! Burn him at the stake!" But here's my reasoning:
Is it really the job of the database to enforce rules about what values should and should not be permitted - except of course as needed to enforce things like referential integrity and to control storage consumption (by having things like max chars set)? Wouldn't it be easier and better to enforce all "null vs. not null" rules at the code level prior to storing the values in the database?
After all, it's the job of the code to validate all values prior to them being stored in the database anyway, right? So why should the database try to usurp the code's authority by also setting up rules about what values are valid? (In a way, using not null constraints except where absolutely necessary almost feels like a violation of the idea of "separation of concerns.") Furthermore, any time a constraint is enforced at the database level, it must necessarily be enforced at the code level also to prevent the code from "blowing up." So why do twice as much work?
At least for me, it seems like things work out better when my database is allowed to simply be a "dumb data storage container" because inevitably in the past when I've tried to use "NOT NULL" to enforce a business rule which made sense to me at the time, I end up wishing I hadn't and end up going back and removing the constraint.
Like I said, I realize I'm a newbie and if there's something I'm overlooking, let me know - and try not to butcher me up too bad :) Thanks.
If you can't know the value at insert time, you really must have a null allowed. For instance, suppose you havea record that includes two fields, begin date and end date. You know begin date when the record is inserted but not the end date. Creating a fake date to put in this field just to avoid nulls is dumb to say the least.
In real life at least as much harm is caused by forcing data entry into a field as by not forcing it. If you havea an email field and don't know the customer's email, then the user has to make something up to put into the required field. Likely what they make up may not be what you would want them to make up something like "thisistupid#ass.com". Sometimes this bad info gets provided back to the client or to a vendor in a data feed and your company looks really really stupid. I know as I process a lot of these feeds coming in from our customers. Nice things in the email field have included, "his secretary is the fat blonde", "this guy is a jerk" etc.
From my perspective, while it may be better for the database, it's not better for the user. Once you get into more interactive applications, you want to be able to persist the data in an interim state, so most of your fields will probably be null at that point.
It depends on what you're trying to do, but for many applications it's a good idea to avoid NULLs where possible — and the most foolproof way to do this is to use NOT NULL.
The problem is that the meaning of NULL is open to interpretation. It could mean “no value belongs here,” or it could mean “we haven't got the value yet, so we should keep asking the user for it.” If you are using it, you'll want to read up on SQL's 3-valued logic, and functions such as COALESCE, etc.
Nevertheless, as Cletus and others have said, if you use NULL appropriately it can be useful.
In business apps I was always removing my NOT NULLS because the users did not like being forced to enter data that they didn't know. It depends on the table but I set most of my fields to NULL and only set the bare minimum number of fields to NOT NULL.
If your data can actually BE "unknown", and it's important to record that fact, then yes, use a NULL. Bear in mind that sometimes you need to differentiate between "unknown" and "not relevant" - for example, a DateTime field in one of my databases can either be the SQL Server minimum date (not applicable), NULL (unknown), or any other date (known value).
For fields which don't really have business rules depending on them - I'm talking about "Comments", "Description", "Notes" columns here - then I set them to default to empty strings, as (a) it saves dealing with nulls, and (b) they are never "unknown" - they just aren't filled in, which logically is a known empty value.
E.g.:
CREATE TABLE Computer (
Id INT IDENTITY PRIMARY KEY
, Name NVARCHAR(16) NOT NULL
, ...[other fields]...
, Comments NVARCHAR(255) NOT NULL
CONSTRAINT DF_Computer_Comments DEFAULT (N'')
)
If you don't supply a value to Comments, it defaults to empty.
Short answer: it depends on what you are storing.
I can see a table (or two) having all NOT NULLS or all NULLS. But an entire database?
Only for columns where not having a value doesn't make any sense.
Nulls can be very handy; for one thing, they compress beautifully. They can be a nasty surprise when you don't expect them, though, so if you can't have a Student without a First Name -- make that column NOT NULL. (Middle names, on the other hand... maybe you want to have a default empty string, maybe not -- decent arguments both ways)
You should not forget to set not null where needed, use check constraints if applicable, not forget about unique constraints, create proper indexes and brush your teeth after every meal and before going to bed:)
In most cases you can use not null and you should use not null. It is easier to change not null->null than in opposite direction, but for example in Oracle empty string is treated as null, so it is obvious that you can't use it all the time.
What's the alternative?
I found this question as a result of a discussion at work. Our question was:
Should we have a nullable foreign key or an association table with unique constraints?
The context was that sometimes there is an association and sometimes there isn't. (EG: Unplanned vs. planned schedules)
For me, a combination of nullable foreign key with a 'set field to null on delete' was equivalent to the association table but had two advantages:
More understandable (the schema was already complex)
Easier to find 'unplanned' schedules with an 'xxx is null' query (vs. not exists query)
In summary, sometimes 'null' (the absence of information) actually means something. Try to have non-null, but there are exceptions.
FWIW, we were using Scala / Squeryl so, in code, the field was an 'Option' and quite safe.
My take is that if you want to have flexible and "ambiguous" tables to some extent, just use NoSQL, as it is precisely built for that purpose. Otherwise, having a NULL value in a row is just acceptable as it maybe some piece of optional data, like Address 2, or home phone number and that kind of things.
In my opinion, making Foreign keys nullable break one of the main reasons we use relational databases. As you want your data to be as tightly related and consistent as possible.
It depends (on the datatype)
Think about this, If the immediate technology that interacts with database is Python I shall make everything NOT NULL with a proper DEFAULT.
However the above makes sense if the column is VARCHAR with default as empty string.
What about NUMERIC, It is hard to come up with default values where NULL can convey more details other than simply set to DEFAULT=0
For BOOLEAN still NULL makes some sense, and so on.
Similar argument can be carried out for various datatypes like spatial data types.
IMO, using NULLable option must be minimized. The application should designate a suitable value for the "non-existent" state. In Peoplesoft I think, the application puts a 0 for Numericals and a space for Char columns where a value does not exist.
One could argue why the so-called suitable value couldn't be NULL.
Because SQL implementation treats nulls totally differently.
For e.g.
1 = NULL and 0 = NULL both result in false!
NULL = NULL is false!
NULL value in GROUP BY and other aggregate functions also create unexpected results.