In SQL server if you have nullParam=NULL in a where clause, it always evaluates to false. This is counterintuitive and has caused me many errors. I do understand the IS NULL and IS NOT NULL keywords are the correct way to do it. But why does SQL server behave this way?
Think of the null as "unknown" in that case (or "does not exist"). In either of those cases, you can't say that they are equal, because you don't know the value of either of them. So, null=null evaluates to not true (false or null, depending on your system), because you don't know the values to say that they ARE equal. This behavior is defined in the ANSI SQL-92 standard.
EDIT:
This depends on your ansi_nulls setting. if you have ANSI_NULLS off, this WILL evaluate to true. Run the following code for an example...
set ansi_nulls off
if null = null
print 'true'
else
print 'false'
set ansi_nulls ON
if null = null
print 'true'
else
print 'false'
How old is Frank? I don't know (null).
How old is Shirley? I don't know (null).
Are Frank and Shirley the same age?
Correct answer should be "I don't know" (null), not "no", as Frank and Shirley might be the same age, we simply don't know.
Here I will hopefully clarify my position.
That NULL = NULL evaluate to FALSE is wrong. Hacker and Mister correctly answered NULL.
Here is why. Dewayne Christensen wrote to me, in a comment to Scott Ivey:
Since it's December, let's use a
seasonal example. I have two presents
under the tree. Now, you tell me if I
got two of the same thing or not.
They can be different or they can be equal, you don't know until one open both presents. Who knows? You invited two people that don't know each other and both have done to you the same gift - rare, but not impossible §.
So the question: are these two UNKNOWN presents the same (equal, =)? The correct answer is: UNKNOWN (i.e. NULL).
This example was intended to demonstrate that "..(false or null, depending on your system).." is a correct answer - it is not, only NULL is correct in 3VL (or is ok for you to accept a system which gives wrong answers?)
A correct answer to this question must emphasize this two points:
three-valued logic (3VL) is counterintuitive (see countless other questions on this subject on Stackoverflow and in other forum to make sure);
SQL-based DBMSes often do not respect even 3VL, they give wrong answers sometimes (as, the original poster assert, SQL Server do in this case).
So I reiterate: SQL does not any good forcing one to interpret the reflexive property of equality, which state that:
for any x, x = x §§ (in plain English: whatever the universe of discourse, a "thing" is always equal to itself).
.. in a 3VL (TRUE, FALSE, NULL). The expectation of people would conform to 2VL (TRUE, FALSE, which even in SQL is valid for all other values), i.e. x = x always evaluate to TRUE, for any possible value of x - no exceptions.
Note also that NULLs are valid " non-values " (as their apologists pretend them to be) which one can assign as attribute values(??) as part of relation variables. So they are acceptable values of every type (domain), not only of the type of logical expressions.
And this was my point: NULL, as value, is a "strange beast". Without euphemism, I prefer to say: nonsense.
I think that this formulation is much more clear and less debatable - sorry for my poor English proficiency.
This is only one of the problems of NULLs. Better to avoid them entirely, when possible.
§ we are concerned about values here, so the fact that the two presents are always two different physical objects are not a valid objection; if you are not convinced I'm sorry, it is not this the place to explain the difference between value and "object" semantics (Relational Algebra has value semantics from the start - see Codd's information principle; I think that some SQL DBMS implementors don't even care about a common semantics).
§§ to my knowledge, this is an axiom accepted (in a form or another, but always interpreted in a 2VL) since antiquity and that exactly because is so intuitive. 3VLs (is a family of logics in reality) is a much more recent development (but I'm not sure when was first developed).
Side note: if someone will introduce Bottom, Unit and Option Types as attempts to justify SQL NULLs, I will be convinced only after a quite detailed examination that will shows of how SQL implementations with NULLs have a sound type system and will clarify, finally, what NULLs (these "values-not-quite-values") really are.
In what follow I will quote some authors. Any error or omission is
probably mine and not of the original authors.
Joe Celko on SQL NULLs
I see Joe Celko often cited on this forum. Apparently he is a much respected author here. So, I said to myself: "what does he wrote about SQL NULLs? How does he explain NULLs numerous problems?". One of my friend has an ebook version of Joe Celko's SQL for smarties: advanced SQL programming, 3rd edition. Let's see.
First, the table of contents. The thing that strikes me most is the number of times that NULL is mentioned and in the most varied contexts:
3.4 Arithmetic and NULLs 109
3.5 Converting Values to and from NULL 110
3.5.1 NULLIF() Function 110
6 NULLs: Missing Data in SQL 185
6.4 Comparing NULLs 190
6.5 NULLs and Logic 190
6.5.1 NULLS in Subquery Predicates 191
6.5.2 Standard SQL Solutions 193
6.6 Math and NULLs 193
6.7 Functions and NULLs 193
6.8 NULLs and Host Languages 194
6.9 Design Advice for NULLs 195
6.9.1 Avoiding NULLs from the Host Programs 197
6.10 A Note on Multiple NULL Values 198
10.1 IS NULL Predicate 241
10.1.1 Sources of NULLs 242
...
and so on. It rings "nasty special case" to me.
I will go into some of these cases with excerpts from this book, trying to limit myself to the essential, for copyright reasons. I think these quotes fall within "fair use" doctrine and they can even stimulate to buy the book - so I hope that no one will complain (otherwise I will need to delete most of it, if not all). Furthermore, I shall refrain from reporting code snippets for the same reason. Sorry about that. Buy the book to read about datailed reasoning.
Page numbers between parenthesis in what follow.
NOT NULL Constraint (11)
The most important column constraint is the NOT NULL, which forbids
the use of NULLs in a column. Use this constraint routinely, and remove
it only when you have good reason. It will help you avoid the
complications of NULL values when you make queries against the data.
It is not a value; it is a marker that holds a place where a value might go.
Again this "value but not quite a value" nonsense. The rest seems quite sensible to me.
(12)
In short, NULLs cause a lot of irregular features in SQL, which we will discuss
later. Your best bet is just to memorize the situations and the rules for NULLs
when you cannot avoid them.
Apropos of SQL, NULLs and infinite:
(104) CHAPTER 3: NUMERIC DATA IN SQL
SQL has not accepted the IEEE model for mathematics for several reasons.
...
If the IEEE rules for math were allowed in
SQL, then we would need type conversion rules for infinite and a way to
represent an infinite exact numeric value after the conversion. People
have enough trouble with NULLs, so let’s not go there.
SQL implementations undecided on what NULL really means in particular contexts:
3.6.2 Exponential Functions (116)
The problem is that logarithms are undefined when (x <= 0). Some SQL
implementations return an error message, some return a NULL and DB2/
400; version 3 release 1 returned *NEGINF (short for “negative infinity”)
as its result.
Joe Celko quoting David McGoveran and C. J. Date:
6 NULLs: Missing Data in SQL (185)
In their book A Guide to Sybase and SQL Server, David McGoveran
and C. J. Date said: “It is this writer’s opinion than NULLs, at least as
currently defined and implemented in SQL, are far more trouble than
they are worth and should be avoided; they display very strange and
inconsistent behavior and can be a rich source of error and confusion.
(Please note that these comments and criticisms apply to any system
that supports SQL-style NULLs, not just to SQL Server specifically.)”
NULLs as a drug addiction:
(186/187)
In the rest of this book, I will be urging you not to use
them, which may seem contradictory, but it is not. Think of a NULL
as a drug; use it properly and it works for you, but abuse it and it can ruin
everything. Your best policy is to avoid NULLs when you can and use
them properly when you have to.
My unique objection here is to "use them properly", which interacts badly with
specific implementation behaviors.
6.5.1 NULLS in Subquery Predicates (191/192)
People forget that a subquery often hides a comparison with a NULL.
Consider these two tables:
...
The result will be empty. This is counterintuitive, but correct.
(separator)
6.5.2 Standard SQL Solutions (193)
SQL-92 solved some of the 3VL (three-valued logic) problems by adding
a new predicate of the form:
<search condition> IS [NOT] TRUE | FALSE | UNKNOWN
But UNKNOWN is a source of problems in itself, so that C. J. Date,
in his book cited below, reccomends in chapter 4.5. Avoiding Nulls in SQL:
Don't use the keyword UNKNOWN in any context whatsoever.
Read "ASIDE" on UNKNOWN, also linked below.
6.8 NULLs and Host Languages (194)
However, you should know how NULLs are handled when they have
to be passed to a host program. No standard host language for
which an embedding is defined supports NULLs, which is another
good reason to avoid using them in your database schema.
(separator)
6.9 Design Advice for NULLs (195)
It is a good idea to declare all your base tables with NOT NULL
constraints on all columns whenever possible. NULLs confuse people
who do not know SQL, and NULLs are expensive.
Objection: NULLs confuses even people that know SQL well,
see below.
(195)
NULLs should be avoided in FOREIGN KEYs. SQL allows this “benefit
of the doubt” relationship, but it can cause a loss of information in
queries that involve joins. For example, given a part number code in
Inventory that is referenced as a FOREIGN KEY by an Orders table, you
will have problems getting a listing of the parts that have a NULL. This is
a mandatory relationship; you cannot order a part that does not exist.
(separator)
6.9.1 Avoiding NULLs from the Host Programs (197)
You can avoid putting NULLs into the database from the Host Programs
with some programming discipline.
...
Determine impact of missing data on programming and reporting:
Numeric columns with NULLs are a problem, because queries
using aggregate functions can provide misleading results.
(separator)
(227)
The SUM() of an empty set is always NULL. One of the most common
programming errors made when using this trick is to write a query that
could return more than one row. If you did not think about it, you might
have written the last example as: ...
(separator)
10.1.1 Sources of NULLs (242)
It is important to remember where NULLs can occur. They are more than
just a possible value in a column. Aggregate functions on empty sets,
OUTER JOINs, arithmetic expressions with NULLs, and OLAP operators
all return NULLs. These constructs often show up as columns in
VIEWs.
(separator)
(301)
Another problem with NULLs is found when you attempt to convert
IN predicates to EXISTS predicates.
(separator)
16.3 The ALL Predicate and Extrema Functions (313)
It is counterintuitive at first that these two predicates are not the same in SQL:
...
But you have to remember the rules for the extrema functions—they
drop out all the NULLs before returning the greater or least values. The
ALL predicate does not drop NULLs, so you can get them in the results.
(separator)
(315)
However, the definition in the standard is worded in the
negative, so that NULLs get the benefit of the doubt.
...
As you can see, it is a good idea to avoid NULLs in UNIQUE
constraints.
Discussing GROUP BY:
NULLs are treated as if they were all equal to each other, and
form their own group. Each group is then reduced to a single
row in a new result table that replaces the old one.
This means that for GROUP BY clause NULL = NULL does not
evaluate to NULL, as in 3VL, but it evaluate to TRUE.
SQL standard is confusing:
The ORDER BY and NULLs (329)
Whether a sort key value that is NULL is considered greater or less than a
non-NULL value is implementation-defined, but...
... There are SQL products that do it either way.
In March 1999, Chris Farrar brought up a question from one of his
developers that caused him to examine a part of the SQL Standard that
I thought I understood. Chris found some differences between the
general understanding and the actual wording of the specification.
And so on. I think is enough by Celko.
C. J. Date on SQL NULLs
C. J. Date is more radical about NULLs: avoid NULLs in SQL, period.
In fact, chapter 4 of his SQL and Relational Theory: How to Write Accurate
SQL Code is titled "NO DUPLICATES, NO NULLS", with subchapters
"4.4 What's Wrong with Nulls?" and "4.5 Avoiding Nulls in SQL" (follow the link:
thanks to Google Books, you can read some pages on-line).
Fabian Pascal on SQL NULLs
From its Practical Issues in Database Management - A Reference
for the Thinking Practitioner (no excerpts on-line, sorry):
10.3 Pratical Implications
10.3.1 SQL NULLs
... SQL suffers from the problems inherent in 3VL as well as from many
quirks, complications, counterintuitiveness, and outright errors [10, 11];
among them are the following:
Aggregate functions (e.g., SUM(), AVG()) ignore NULLs (except for COUNT()).
A scalar expression on a table without rows evaluates incorrectly to NULL, instead of 0.
The expression "NULL = NULL" evaluates to NULL, but is actually invalid in SQL; yet ORDER BY treats NULLs as equal (whatever they precede or follow "regular" values is left to DBMS vendor).
The expression "x IS NOT NULL" is not equal to "NOT(x IS NULL)", as is the case in 2VL.
...
All commercially implemented SQL dialects follow this 3VL approach, and, thus,
not only do they exibits these problems, but they also have spefic implementation
problems, which vary across products.
The answers here all seem to come from a CS perspective so I want to add one from a developer perspective.
For a developer NULL is very useful. The answers here say NULL means unknown, and maybe in CS theory that's true, don't remember, it's been a while. In actual development though, at least in my experience, that happens about 1% of the time. The other 99% it is used for cases where the value is not UNKNOWN but it is KNOWN TO BE ABSENT.
For example:
Client.LastPurchase, for a new client. It is not unknown, it is known that he hasn't made a purchase yet.
When using an ORM with a Table per Class Hierarchy mapping, some values are just not mapped for certain classes.
When mapping a tree structure a root will usually have Parent = NULL
And many more...
I'm sure most developers at some point wrote WHERE value = NULL,
didn't get any results, and that's how they learned about IS NULL syntax. Just look how many votes this question and the linked ones have.
SQL Databases are a tool, and they should be designed the way which is easiest for their users to understand.
Just because you don't know what two things are, does not mean they're equal. If when you think of NULL you think of “NULL” (string) then you probably want a different test of equality like Postgresql's IS DISTINCT FROM AND IS NOT DISTINCT FROM
From the PostgreSQL docs on "Comparison Functions and Operators"
expression IS DISTINCT FROM expression
expression IS NOT DISTINCT FROM expression
For non-null inputs, IS DISTINCT FROM is the same as the <> operator. However, if both inputs are null it returns false, and if only one input is null it returns true. Similarly, IS NOT DISTINCT FROM is identical to = for non-null inputs, but it returns true when both inputs are null, and false when only one input is null. Thus, these constructs effectively act as though null were a normal data value, rather than "unknown".
Maybe it depends, but I thought NULL=NULL evaluates to NULL like most operations with NULL as an operand.
At technet there is a good explanation for how null values work.
Null means unknown.
Therefore the Boolean expression
value=null
does not evaluate to false, it evaluates to null, but if that is the final result of a where clause, then nothing is returned. That is a practical way to do it, since returning null would be difficult to conceive.
It is interesting and very important to understand the following:
If in a query we have
where (value=#param Or #param is null) And id=#anotherParam
and
value=1
#param is null
id=123
#anotherParam=123
then
"value=#param" evaluates to null
"#param is null" evaluates to true
"id=#anotherParam" evaluates to true
So the expression to be evaluated becomes
(null Or true) And true
We might be tempted to think that here "null Or true" will be evaluated to null and thus the whole expression becomes null and the row will not be returned.
This is not so. Why?
Because "null Or true" evaluates to true, which is very logical, since if one operand is true with the Or-operator, then no matter the value of the other operand, the operation will return true. Thus it does not matter that the other operand is unknown (null).
So we finally have true=true and thus the row will be returned.
Note: with the same crystal clear logic that "null Or true" evaluates to true, "null And true" evaluates to null.
Update:
Ok, just to make it complete I want to add the rest here too which turns out quite fun in relation to the above.
"null Or false" evaluates to null, "null And false" evaluates to false. :)
The logic is of course still as self-evident as before.
MSDN has a nice descriptive article on nulls and the three state logic that they engender.
In short, the SQL92 spec defines NULL as unknown, and NULL used in the following operators causes unexpected results for the uninitiated:
= operator NULL true false
NULL NULL NULL NULL
true NULL true false
false NULL false true
and op NULL true false
NULL NULL NULL false
true NULL true false
false false false false
or op NULL true false
NULL NULL true NULL
true true true true
false NULL true false
The concept of NULL is questionable, to say the least. Codd introduced the relational model and the concept of NULL in context (and went on to propose more than one kind of NULL!) However, relational theory has evolved since Codd's original writings: some of his proposals have since been dropped (e.g. primary key) and others never caught on (e.g. theta operators). In modern relational theory (truly relational theory, I should stress) NULL simply does not exist. See The Third Manifesto. http://www.thethirdmanifesto.com/
The SQL language suffers the problem of backwards compatibility. NULL found its way into SQL and we are stuck with it. Arguably, the implementation of NULL in SQL is flawed (SQL Server's implementation makes things even more complicated due to its ANSI_NULLS option).
I recommend avoiding the use of NULLable columns in base tables.
Although perhaps I shouldn't be tempted, I just wanted to assert a corrections of my own about how NULL works in SQL:
NULL = NULL evaluates to UNKNOWN.
UNKNOWN is a logical value.
NULL is a data value.
This is easy to prove e.g.
SELECT NULL = NULL
correctly generates an error in SQL Server. If the result was a data value then we would expect to see NULL, as some answers here (wrongly) suggest we would.
The logical value UNKNOWN is treated differently in SQL DML and SQL DDL respectively.
In SQL DML, UNKNOWN causes rows to be removed from the resultset.
For example:
CREATE TABLE MyTable
(
key_col INTEGER NOT NULL UNIQUE,
data_col INTEGER
CHECK (data_col = 55)
);
INSERT INTO MyTable (key_col, data_col)
VALUES (1, NULL);
The INSERT succeeds for this row, even though the CHECK condition resolves to NULL = NULL. This is due defined in the SQL-92 ("ANSI") Standard:
11.6 table constraint definition
3)
If the table constraint is a check
constraint definition, then let SC be
the search condition immediately
contained in the check constraint
definition and let T be the table name
included in the corresponding table
constraint descriptor; the table
constraint is not satisfied if and
only if
EXISTS ( SELECT * FROM T WHERE NOT
( SC ) )
is true.
Read that again carefully, following the logic.
In plain English, our new row above is given the 'benefit of the doubt' about being UNKNOWN and allowed to pass.
In SQL DML, the rule for the WHERE clause is much easier to follow:
The search condition is applied to
each row of T. The result of the where
clause is a table of those rows of T
for which the result of the search
condition is true.
In plain English, rows that evaluate to UNKNOWN are removed from the resultset.
Because NULL means 'unknown value' and two unknown values cannot be equal.
So, if to our logic NULL N°1 is equal to NULL N°2, then we have to tell that somehow:
SELECT 1
WHERE ISNULL(nullParam1, -1) = ISNULL(nullParam2, -1)
where known value -1 N°1 is equal to -1 N°2
NULL isn't equal to anything, not even itself. My personal solution to understanding the behavior of NULL is to avoid using it as much as possible :).
The question:
Does one unknown equal another unknown?
(NULL = NULL)
That question is something no one can answer so it defaults to true or false depending on your ansi_nulls setting.
However the question:
Is this unknown variable unknown?
This question is quite different and can be answered with true.
nullVariable = null is comparing the values
nullVariable is null is comparing the state of the variable
The confusion arises from the level of indirection (abstraction) that comes about from using NULL.
Going back to the "what's under the Christmas tree" analogy, "Unknown" describes the state of knowledge about what is in Box A.
So if you don't know what's in Box A, you say it's "Unknown", but that doesn't mean that "Unknown" is inside the box. Something other than unknown is in the box, possibly some kind of object, or possibly nothing is in the box.
Similarly, if you don't know what's in Box B, you can label your state of knowledge about the contents as being "Unknown".
So here's the kicker: Your state of knowledge about Box A is equal to your state of knowledge about Box B. (Your state of knowledge in both cases is "Unknown" or "I don't know what's in the Box".) But the contents of the boxes may or may not be equal.
Going back to SQL, ideally you should only be able to compare values when you know what they are. Unfortunately, the label that describes a lack of knowledge is stored in the cell itself, so we're tempted to use it as a value. But we should not use that as a value, because it would lead to "the content of Box A equals the content of Box B when we don't know what's in Box A and/or we don't know what's in Box B.
(Logically, the implication "if I don't know what's in Box A and if I don't know what's in Box B, then what's in Box A = What's in Box B" is false.)
Yay, Dead Horse.
There are two sensible ways to handle NULL = NULL comparisons in a WHERE clause, and they boil down to "What do you mean by NULL?" One way assumes NULL means "unknown," and the other assumes NULL means "data does not exist." SQL has chosen a third way which is wrong all around.
The "NULL means unknown" solution: Throw an error.
Unknown = unknown should evaluate to 3VL null. But the output of a WHERE clause is 2VL: You either return the row or you don't. It's like being asked to divide by zero and return a number: There is no correct response. So you throw an error instead, and force the programmer to explicitly handle this situation.
The "NULL means no data" solution: Return the row.
No data = no data should evaluate to true. If I'm comparing two people, and they have the same first name, and the same last name, and neither has a middle name, then it is correct to say "These people have the same name."
The SQL solution: Don't return the row.
This is always wrong. If NULL means "unknown," then you don't know if the row should be returned or not, and you should not try to guess. If NULL means "no data," then you should return the row. Either way, silently removing the row is incorrect and will cause problems. It's the worst of both worlds.
Setting aside theory and speaking in practical terms, I'm with AlexDev: I have almost never encountered a case where "return the row" was not the desired result. However, "almost never" is not "never," and SQL databases often serve as the backbones of big important systems, so I can see a fair case for being rigorous and throwing an error.
What I cannot see is a case for silently coercing 3VL null into 2VL false. Like most silent type coercions, it's a rabid weasel waiting to be set loose in your system, and when the weasel finally jumps out and bites someone, you'll have the merry devil of a time tracking it back to its nest.
null is unknown in sql so we cant expect two unknowns to be same.
However you can get that behavior by setting ANSI_NULLS to Off(its On by Default)
You will be able to use = operator for nulls
SET ANSI_NULLS off
if null=null
print 1
else
print 2
set ansi_nulls on
if null=null
print 1
else
print 2
You work for the government registering information about citizens. This includes the national ID for every person in the country. A child was left at the door of a church some 40 years ago, nobody knows who their parents are. This person's father ID is NULL. Two such people exist. Count people who share the same father ID with at least one other person (people who are siblings). Do you count those two too?
The answer is no, you don’t, because we don’t know if they are siblings or not.
Suppose you don’t have a NULL option, and instead use some pre-determined value to represent “the unknown”, perhaps an empty string or the number 0 or a * character, etc. Then you would have in your queries that * = *, 0 = 0, and “” = “”, etc. This is not what you want (as per the example above), and as you might often forget about these cases (the example above is a clear fringe case outside ordinary everyday thinking), then you need the language to remember for you that NULL = NULL is not true.
Necessity is the mother of invention.
Just an addition to other wonderful answers:
AND: The result of true and unknown is unknown, false and unknown is false,
while unknown and unknown is unknown.
OR: The result of true or unknown is true, false or unknown is unknown, while unknown or unknown is unknown.
NOT: The result of not unknown is unknown
If you are looking for an expression returning true for two NULLs you can use:
SELECT 1
WHERE EXISTS (
SELECT NULL
INTERSECT
SELECT NULL
)
It is helpful if you want to replicate data from one table to another.
The equality test, for example, in a case statement when clause, can be changed from
XYZ = NULL
to
XYZ IS NULL
If I want to treat blanks and empty string as equal to NULL I often also use an equality test like:
(NULLIF(ltrim( XYZ ),'') IS NULL)
To quote the Christmas analogy again:
In SQL, NULL basically means "closed box" (unknown). So, the result of comparing two closed boxes will also be unknown (null).
I understand, for a developer, this is counter-intuitive, because in programming languages, often NULL rather means "empty box" (known). And comparing two empty boxes will naturally yield true / equal.
This is why JavaScript for example distinguishes between null and undefined.
Null isn't equal to anything including itself
Best way to test if an object is null is to check whether the object equals itself since null is the only object not equal to itself
const obj = null
console.log(obj==obj) //false, then it's null
Check this article
I never had to do this before and never even thought about this before. How can i or what is the best way of storing RGB values in the database.
I thought of couple of options. The most obvious one being 3 byte columns to store the R,G and the B.(I dont want to go this route)
Another option is to store it in a 32 bit int column. ( I am leaning towards this one)
or may be i am just missing something trivial.
The "wasted" space of 32-bit integer column would allow you to store an alpha channel as well, should the need ever arise for it.
First and foremost: what are your requirements?
Do you need to retrieve the color and only the color? Do you ever need to query be components? do you need to search by colorspace distance? Do you need to store colorspace information (Adobe RGB or sRGB)? See also Best Way to represent a color in SQL.
If you're doing storing these numbers for web design, I would suggest simply using a char(6) and storing a string of hex triplets.
Sure, that's two bytes "wasted" over a 32-bit integer, but if you're not comparing them mathematically in some way and just regurgitating them to a CSS file, for instance, storing as a string will remove the need to translate back and forth.
Not that hex triplets to integers is a tough translation, but doing the easiest thing possible rather than optimizing for a few bytes may be worth considering.
If you're doing something other than web-related work, you may want to consider building in room for more than 8 bits per channel.
RGB values are usually described on the web in the format 0xRRGGBB where RR, GG, and BB are the hex values of R, G, and B. While you may be wasting a bit of space with a 32 bit int, I can't imagine it's much compared to the benefit you'll potentially gain from storing the values in a well-known format.
In case you'd like quick primer on how to go about the conversion, wikipedia's got you covered!
Just store it as a 32 bit value. There is no point in breaking down into 3 fields since you will most likely want all 3 components together all the time.
My guess is to store a 32 bit integer.
However if your SQL operations require each component to be of individual columns (meaning to say you need to compare values of R vs G of another column for example) you will have to separate out the values into individual columns. R, G, B, each 0-255 integer.
From first glance, it would appear I have two basic choices for storing ZIP codes in a database table:
Text (probably most common), i.e. char(5) or varchar(9) to support +4 extension
Numeric, i.e. 32-bit integer
Both would satisfy the requirements of the data, if we assume that there are no international concerns. In the past we've generally just gone the text route, but I was wondering if anyone does the opposite? Just from brief comparison it looks like the integer method has two clear advantages:
It is, by means of its nature, automatically limited to numerics only (whereas without validation the text style could store letters and such which are not, to my knowledge, ever valid in a ZIP code). This doesn't mean we could/would/should forgo validating user input as normal, though!
It takes less space, being 4 bytes (which should be plenty even for 9-digit ZIP codes) instead of 5 or 9 bytes.
Also, it seems like it wouldn't hurt display output much. It is trivial to slap a ToString() on a numeric value, use simple string manipulation to insert a hyphen or space or whatever for the +4 extension, and use string formatting to restore leading zeroes.
Is there anything that would discourage using int as a datatype for US-only ZIP codes?
A numeric ZIP code is -- in a small way -- misleading.
Numbers should mean something numeric. ZIP codes don't add or subtract or participate in any numeric operations. 12309 - 12345 does not compute the distance from downtown Schenectady to my neighborhood.
Granted, for ZIP codes, no one is confused. However, for other number-like fields, it can be confusing.
Since ZIP codes aren't numbers -- they just happen to be coded with a restricted alphabet -- I suggest avoiding a numeric field. The 1-byte saving isn't worth much. And I think that that meaning is more important than the byte.
Edit.
"As for leading zeroes..." is my point. Numbers don't have leading zeros. The presence of meaningful leading zeros on ZIP codes is yet another proof that they're not numeric.
Are you going to ever store non-US postal codes? Canada is 6 characters with some letters. I usually just use a 10 character field. Disk space is cheap, having to rework your data model is not.
Use a string with validation. Zip codes can begin with 0, so numeric is not a suitable type. Also, this applies neatly to international postal codes (e.g. UK, which is up to 8 characters). In the unlikely case that postal codes are a bottleneck, you could limit it to 10 characters, but check out your target formats first.
Here are validation regexes for UK, US and Canada.
Yes, you can pad to get the leading zeroes back. However, you're theoretically throwing away information that might help in case of errors. If someone finds 1235 in the database, is that originally 01235, or has another digit been missed?
Best practice says you should say what you mean. A zip code is a code, not a number. Are you going to add/subtract/multiply/divide zip codes? And from a practical perspective, it's far more important that you're excluding extended zips.
Normally you would use a non-numerical datatype such as a varchar which would allow for more zip code types. If you are dead set on only allowing 5 digit [XXXXX] or 9 digit [XXXXX-XXXX] zip codes, you could then use a char(5) or char(10), but I would not recommend it. Varchar is the safest and most sane choice.
Edit: It should also be noted that if you don't plan on doing numerical calculations on the field, you should not use a numerical data type. ZIP Code is a not a number in the sense that you add or subtract against it. It is just a string that happens to be made up typically of numbers, so you should refrain from using numerical data types for it.
From a technical standpoint, some points raised here are fairly trivial. I work with address data cleansing on a daily basis - in particular cleansing address data from all over the world. It's not a trivial task by any stretch of the imagination. When it comes to zip codes, you could store them as an integer although it may not be "semantically" correct. The fact is, the data is of a numeric form whether or not, strictly speaking it is considered numeric in value.
However, the very real drawback of storing them as numeric types is that you'll lose the ability to easily see if the data was entered incorrectly (i.e. has missing values) or if the system removed leading zeros leading to costly operations to validate potentially invalid zip codes that were otherwise correct.
It's also very hard to force the user to input correct data if one of the repercussions is a delay of business. Users often don't have the patience to enter correct data if it's not immediately obvious. Using a regex is one way of guaranteeing correct data, however if the user enters a value that doesn't conform and they're displayed an error, they may just omit this value altogether or enter something that conforms but is otherwise incorrect. One example [using Canadian postal codes] is that you often see A0A 0A0 entered which isn't valid but conforms to the regex for Canadian postal codes. More often than not, this is entered by users who are forced to provide a postal code, but they either don't know what it is or don't have all of it correct.
One suggestion is to validate the whole of the entry as a unit validating that the zip code is correct when compared with the rest of the address. If it is incorrect, then offering alternate valid zip codes for the address will make it easier for them to input valid data. Likewise, if the zip code is correct for the street address, but the street number falls outside the domain of that zip code, then offer alternate street numbers for that zip code/street combination.
No, because
You never do math functions on zip code
Could contain dashes
Could start with 0
NULL values sometimes interpreted as zero in case of scalar types
like integer (e.g. when you export the data somehow)
Zip code, even if it's a number, is a designation of an area,
meaning this is a name instead of a numeric quantity of anything
Unless you have a business requirement to perform mathematical calculations on ZIP code data, there's no point in using an INT. You're over engineering.
Hope this helps,
Bill
ZIP Codes are traditionally digits, as well as a hyphen for Zip+4, but there is at least one Zip+4 with a hyphen and capital letters:
10022-SHOE
https://www.prnewswire.com/news-releases/saks-fifth-avenue-celebrates-the-10th-birthday-of-its-famed-10022-shoe-salon-300504519.html
Realistically, a lot of business applications will not need to support this edge case, even if it is valid.
Integer is nice, but it only works in the US, which is why most people don't do it. Usually I just use a varchar(20) or so. Probably overkill for any locale.
If you were to use an integer for US Zips, you would want to multiply the leading part by 10,000 and add the +4. The encoding in the database has nothing to do with input validation. You can always require the input to be valid or not, but the storage is matter of how much you think your requirements or the USPS will change. (Hint: your requirements will change.)
I learned recently that in Ruby one reason you would want to avoid this is because there are some zip codes that begin with leading zeroes, which–if stored as in integer–will automatically be converted to octal.
From the docs:
You can use a special prefix to write numbers in decimal, hexadecimal, octal or binary formats. For decimal numbers use a prefix of 0d, for hexadecimal numbers use a prefix of 0x, for octal numbers use a prefix of 0 or 0o…
I think the ZIP code in the int datatype can affect the ML-model. Probably, the higher the code can create outlier in the data for the calculation