Why does 28.08 show up as 28.080000000000002 in DataGridView

[Jon Skeet [C# MVP]]

<snip>

Having failed to sleep properly due to nuances about this kind of thing
floating (no pun intended) round my head, I think it's probably best
for everyone if I sign off from the thread at this point. That's not in
any way meant to try to tell people not to reply to the posts I made a
short while ago - I'm not trying to get the last word.

I've enjoyed the debate, and I hope you all have too. Back to bed for a
couple of hours, hopefully to get some rest...

 

[Cor Ligthert [MVP]]

Hi guys,

It is very interesting, but I see in this long thread not any question from
a VB.Net guy/girl, they know this probably already, so will you next time
disconnect the not relevant newsgroups as you recognise this.

Thanks in advance,

Cor

 

[Mike C#]

1/3 is in the range, but can't be stored exactly. Which part of that
statement do you disagree with?

Just as an example: Consider the SQL NUMERIC(2, 1) data type. 1/3 is
*absolutely not* a member of the set of valid values for this type. The
complete set of valid values for this type is:

{ -9.9, -9.8, -9.7, -9.6, -9.5, -9.4, -9.3, -9.2, -9.1, -9.0, -8.9, -8.8, -8.7,
-8.6, -8.5, -8.4, -8.3, -8.2, -8.1, -8.0, -7.9, -7.8, -7.7, -7.6, -7.5, -7.4,
-7.3, -7.2, -7.1, -7.0, -6.9, -6.8, -6.7, -6.6, -6.5, -6.4, -6.3, -6.2, -6.1,
-6.0, -5.9, -5.8, -5.7, -5.6, -5.5, -5.4, -5.3, -5.2, -5.1, -5.0, -4.9, -4.8,
-4.7, -4.6, -4.5, -4.4, -4.3, -4.2, -4.1, -4.0, -3.9, -3.8, -3.7, -3.6, -3.5,
-3.4, -3.3, -3.2, -3.1, -3.0, -2.9, -2.8, -2.7, -2.6, -2.5, -2.4, -2.3, -2.2,
-2.1, -2.0, -1.9, -1.8, -1.7, -1.6, -1.5, -1.4, -1.3, -1.2, -1.1, -1.0, -0.9,
-0.8, -0.7, -0.6, -0.5, -0.4, -0.3, -0.2, -0.1, 0.0, 0.1, 0.2, 0.3, 0.4,
0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9,
2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9, 3.0, 3.1, 3.2, 3.3, 3.4,
3.5, 3.6, 3.7, 3.8, 3.9, 4.0, 4.1, 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9,
5.0, 5.1, 5.2, 5.3, 5.4, 5.5, 5.6, 5.7, 5.8, 5.9, 6.0, 6.1, 6.2, 6.3, 6.4,
6.5, 6.6, 6.7, 6.8, 6.9, 7.0, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6, 7.7, 7.8, 7.9,
8.0, 8.1, 8.2, 8.3, 8.4, 8.5, 8.6, 8.7, 8.8, 8.9, 9.0, 9.1, 9.2, 9.3, 9.4,
9.5, 9.6, 9.7, 9.8, 9.9 }

Likewise with NUMERIC (10, 2), DECIMAL (24, 18), or any other valid
precision and scale settings for the NUMERIC and DECIMAL types.

The fact that you cannot accurately store a value that is not a member of
the set of valid values for a type is of no significance. Since by
definition 1/3 is not a member of the set of valid values for the DECIMAL or
NUMERIC types, why do you find it surprising that 1/3 cannot be stored
exactly using these types?

They're the same thing. Converting 0.1 base 3 (i.e. 1/3) into a decimal
*is* a conversion from one base to another, and the Decimal and Numeric
types *do not* store that number exactly.

No, they are two different arguments. As explained, 0.1 is a non-repeating
decimal, whereas 1/3 is an infinitely repeating decimal which cannot be
represented exactly in decimal form, period. The fact that an infinitely
repeating decimal cannot be represented *exactly* using a DECIMAL or NUMERIC
type should come as no surprise to you or anyone else for that matter. The
fact that a non-repeating decimal with a scale of 1 or 2 (just one or two
digits after the decimal point) cannot be stored *exactly* using FLOAT and
REAL data types comes as a surprise to many people.

I'm happy to disagree with any number of people, if what they're saying
doesn't make sense.

That's perfectly fine; however, as long as the standard calls INT, DECIMAL,
NUMERIC, etc., "exact" numeric data types and it calls FLOAT and REAL
"approximate" numeric data types, you may as well prepare yourself to
disagree with every SQL database programmer and designer in the world.

In that case the same can be said for Float. Every Float value is an
exact value - you can write down the *exact* binary string it
represents, and even convert it into an exact decimal representation.

Sounds great! Prove it: Store 28.08 as a FLOAT value and write doen the
*exact* binary string it represents. Then convert it into an exact decimal
representation. Here's something to get you started:

DECLARE @r FLOAT
SELECT @r = 28.08
SELECT @r

--Result in QA: 28.079999999999998

I don't think that makes any difference - when maximum precision is
used, the range of valid values is given in terms of a minimum and a
maximum, and not every value in that range is exactly representable. If
you're going to treat it as "every value that can be exactly
represented can be exactly represented" then as I say, the same is true
for Float.

Of course you don't think it makes any difference, or you would have quoted
the full quote instead of just the last part that appears to substantiate
your argument. The point is that when the precision and scale are turned
down the set of valid values changes for a given NUMERIC or DECIMAL data
type declaration. See the example above for NUMERIC (2, 1) for an example.
The range of valid values for NUMERIC (2, 1) is nowhere near -10^38 + 1 nor
+10^38 - 1. The minimum and maximum values in this case are -9.9 and +9.9,
and the set of valid values includes only decimal numbers that consist of
two digits where one of those digits is after the decimal point. Your
example of 1/3 does not fit in that set since it has more than 1 digit after
the decimal point (in fact, it has infinitely more digits after the decimal
point).

No, I'm recognising the mathematical reality that the existence (and
value) of a number doesn't depend on what base you represent it in.

The mathematical reality is that very few people through the course of human
history have needed to use Base 3. The reality is that NUMERIC and DECIMAL
types do not represent every single number "in existence", nor every single
number between the minimum and maximum values specified by the precision and
scale set by the user. The real reality is that the user defines the set of
*valid values* as well as implicit upper and lower bounds when they declare
a DECIMAL or NUMERIC type. The reality is that every single *valid value*
in this set can be stored *exactly*; that is, you get out of it exactly what
you put into it.

I wasn't talking about usefulness, I was talking about existence.
Suggesting that 1/3 isn't a number is silly, IMO.

1/3 is a number, and I for one did not say otherwise. Now let me tell you
what is really silly (IMO):

- Suggesting that because you can't store an exact representation of the
infinitely repeating fraction 1/3 in a NUMERIC or DECIMAL (or any data type
for that matter) makes it inexact is ludicrous. As mentioned above, 1/3 is
not defined as a *valid value* for any NUMERIC or DECIMAL precision and
scale.

- Saying that trying to store the infinitely repeating decimal number 1/3
and getting less than an infinitely repeating decimal number is *the same
thing* as storing the non-repeating decimal number 1/10 and getting
something other than 0.1 back as a result is absolutely ridiculous.

Maybe a couple of examples will help, but let's get away from numbers and
bases for a minute and talk about character data (so we don't have to worry
about my decimal bias, or your love for "Base 3"):

0. Consider the VARCHAR data type on SQL 2000. It maxes out at 8,000
characters. You absolutely cannot store an infinite number of 'A'
characters in a VARCHAR(8000); therefore it is not exact by your reasoning.
By your reasoning, the only "exact" representation would be one in which you
could store every possible combination of characters ever, in an infinitely
long character string; even though a character string with 9,000 characters
is not a member of the set of valid values for a VARCHAR(8000). Likewise
you cannot store an infinitely repeating decimal (like 1/3) which, by your
reasoning, makes the NUMERIC and DECIMAL data types "inexact" as well;
despite the fact that 1/3 is not a member of the set of *valid values*
defined for the type.

1. Consider an imaginary data type similar to VARCHAR. We'll call it
APPROXIMATE VARCHAR. The APPROXIMATE VARCHAR will store basically the
strings we pass it; however, it may lose some accuracy in the least
significant characters (right-hand side of the string) due to the way it is
stored. If you were to store 8000 'A' characters in an APPROXIMATE
VARCHAR(8000), but then select the value back you might get 7,999 'A'
characters and one 'B' character. This might be no big deal if you really
only ever need to use the first 10 characters anyway, which appears to be
IEEE's rational behind their representation of floating point numbers. The
analogy is, of course, storing 28.08 in a FLOAT and retrieving 28.07999...
or 28.080000002 or some such number that *approximates* the exact value you
stored originally.

By your reasoning, both of these situations are the same: i.e., trying to
store an infinite number of characters in a data type that has a
well-defined limitation on the number of characters that can be stored *is
the same thing* as storing an approximation of the character string you pass
in. This is pretty silly, IMO.

Regardless of the reason, 1/10 is not a repeating decimal in decimal
(yes,
I'm showing my "bias" for decimal again). It has a finite representation
in
decimal. 1/3 is a repeating decimal in decimal. Since it's decimal
representation repeats infinitely, it's impossible to store it exactly
anywhere. However, with regards to Decimal and Numeric data types in
SQL,
"0.33333" is not a member of the set of valid values for a DECIMAL (10,
2)
or a NUMERIC (10, 3) [for instance].

Indeed.

Glad we agree on something.

There's no reason why they shouldn't - why would it not be able to
guarantees tha the number is essentially reproducible, which is all I
can understand that we're talking about? The binary string "0.010101"
represents an exact number and no others.

SQL REAL and FLOAT types are based on the IEEE standard which guarantees
that it will store an approximation of a value you feed to it. Since we're
dealing with binary computers (as opposed to analog computers) we can expect
that once our exact value is converted to an approximation the internal
representation *of that approximation* will be exact (1's and 0's).
However, the exact value you feed it is still converted to an approximation,
as shown by the OP. NUMERIC and DECIMAL types do not suffer from this
approximation. They store only values from the set of *valid values*
defined for their precision and scale, and they store them exactly. In your
example of 1/3, storing it as a NUMERIC (2, 1) results in "0.3" being
stored, and that rounding occurs during casting, not during the storage
operation. Basically it is attempting to cast 1/3, which is not a member of
the valid set of numbers for NUMERIC(2, 1), to a valid member of that set.
It has nothing to do with the exactness with which "0.3" is actually stored;
whereas that is the entire issue with FLOAT and REAL.

Do you regard 28.08 as in the "range" for FLOAT? It seems unfair to
demand that it is while saying that 1/3 isn't in the "RANGE" for
NUMERIC, but that's what you've basically got to do in order to claim
that FLOAT is "approximate" but DECIMAL is "exact".

Life is not fair; however, your argument here is lacking.

FLOAT is a floating point type and 28.08 is a non-repeating decimal. The
fact that FLOAT cannot accurately store 28.08 exactly but DECIMAL and
NUMERIC can proves the point very well.

DECIMAL and NUMERIC are fixed-point types with a guarantee of storage
"exactness". As mentioned, you define the precision (total number of
digits) and scale (number of digits after the decimal point) for DECIMAL and
NUMERIC, and in the process you implicitly define the upper and lower
bounds, as well as the set of *valid values* for the DECIMAL and NUMERIC
types. The guarantee is that any value that is a member of the set of
*valid values* for those types will be stored exactly. If you want to store
1/3 as DECIMAL or NUMERIC you have to cast it (implicitly or explicitly) to
a member of the set of *valid values* defined for the precision and scale
you have chosen. 1/3 is not a *valid value* from the set of *valid values*
for any NUMERIC or DECIMAL type (since they all have a finite scale).
Consider a DECIMAL (5, 4). You can store only 4 digits after the decimal
point; therefore you cannot store an infinite number of 3's after the
decimal point. Therefore 1/3 is not in the set of valid values for that
type (see previous for example).

Personally I don't find your argument about not being able to store an exact
representation of an infinitely repeating decimal on machines with a finite
amount of memory to be all that compelling a reason to call DECIMAL and
NUMERIC data types "inexact".

 

[Mike C#]

I see nothing hair-splitting about recognising that 1/3 is a number.

Listen to some of your arguments:

Argument A
========
0. "All numeric data types are *approximate* and none are *exact* because
you can't represent every single combination, including infinitely repeating
combinations, using them."

By extension (and we may need to enter the realm of philosophy here), your
argument says there is no such thing as an exact data type of *any kind*
since you can't represent every combination of whatever their base type is
using them; including infinitely repeating series. VARCHAR(8000) is now
*approximate* because I can't store 8,001 'Z' characters in it. INT is
*approximate* because I can't store 999999999999999999999999999999999999999
in it. Personally I find that silly, IMO. An exact data type is not
*exact* based on being able to store every infinite combination; it is exact
because the valid values it does store are stored using *exact*
representations instead of approximations.

Argument B
========
0. "I type in the FLOAT value 10.0,"
1. "The computer stores it as an approximate value of 9.999999,"
2. "The binary representation of 9.999999 is *exact*,"
3. "Therefore FLOAT is an *exact* data types."

Really...

You may notice that there are multiple newsgroups involved here...

Yes I did notice, and I'm responding from the sqlserver newsgroup, just FYI.
SQL folks call floating point numbers "approximate" because that's what the
standard says.

Just because ANSI says something doesn't mean it's right, IMO.

I agree with that. However, they did just fine with this one. Unless you
can find a better argument than "You can't store an infinitely repeating
decimal exactly in a fixed-decimal point type, on a machine with a finite
amount of memory, therefore the fixed-decimal point type is not an *exact*
type", you're not going to convince ANSI or just about anyone else of the
justness of your cause.

 

[Razvan Socol]

[...] The fact that an infinitely
repeating decimal cannot be represented *exactly* using a DECIMAL or NUMERIC
type should come as no surprise to you or anyone else for that matter. The
fact that a non-repeating decimal with a scale of 1 or 2 (just one or two
digits after the decimal point) cannot be stored *exactly* using FLOAT and
REAL data types comes as a surprise to many people. [...]

I agree with both statements above. But you have to consider that the
number 0.2, for example (as well as 28.08), is an "infinitely repeating
binary number" (although is not an "infinitely repeating decimal").

The representation of 0.2 in base 2 is:
0.0011001100110011...
For easier reading, the same thing in base-16 (hexadecimal), would be:
0.3333...

The representation of 0.08 in base 2 is:
0.00010100011110101110 00010100011110101110 00010100011110101110 ...
For easier reading, in hex that would be:
0.147AE147AE147AE...

This may come as surprise to many people, but if you really think about
it, it is normal that some numbers which can be represented in base-10
with a finit number of digits after the decimal point, will be
represented in base-2 with an infinite number of digits. Obviously,
these numbers cannot be accurately stored in SQL using a float or real
data type (which uses base 2).

To avoid problems when displaying such a number, in the conversion from
base-2 back to base-10, the last bits are sometimes ignored (because
they cannot form an entire decimal digit anyway) and the result
sometimes happens to be the expected result (e.g. 0.2), but not always.


Razvan

 

[Mike C#]

Mike C# wrote:
I agree with both statements above. But you have to consider that the
number 0.2, for example (as well as 28.08), is an "infinitely repeating
binary number" (although is not an "infinitely repeating decimal").
Absolutely.

This may come as surprise to many people, but if you really think about
it, it is normal that some numbers which can be represented in base-10
with a finit number of digits after the decimal point, will be
represented in base-2 with an infinite number of digits. Obviously,
these numbers cannot be accurately stored in SQL using a float or real
data type (which uses base 2).

That's exactly the point I was discussing with Mr. Skeet. The
"approximation" comes into play precisely because of the conversion from
base-10 to base-2 in the FLOAT and REAL types, whereas with DECIMAL and
NUMERIC types presumably use some form of Binary Coded Decimal (possibly
packed BCD or some similar algorithm) to represent the number, which
prevents data loss during the storage process.

 

[Jon Skeet [C# MVP]]

I agree with that. However, they did just fine with this one. Unless you
can find a better argument than "You can't store an infinitely repeating
decimal exactly in a fixed-decimal point type, on a machine with a finite
amount of memory, therefore the fixed-decimal point type is not an *exact*
type", you're not going to convince ANSI or just about anyone else of the
justness of your cause.

Just *one* point I can't resist here: I've never claimed that decimal
isn't exact. I've claimed that float is exact too.

Once you've got a float value, you can always convert it to an exact
decimal string too, and indeed I've got C# code to do it, referenced
from
http://www.pobox.com/~skeet/csharp/floatingpoint.html
There's also an online version where you can type in the decimal value
you want to find the closest double to, and then show the exact value
of the double. (These are C# floats and doubles, but the principle is
the same). For example, the closest double to 28.08 is *exactly*
28.0799999999999982946974341757595539093017578125

My point (which I thought I'd made again and again, but clearly not
enough) is that it's the conversion from a decimal literal to a
floating point value which is an "approximate" conversion, *not* the
float value itself. The point about base 3 is that the same would be
true - convert a base 3 number to a decimal and you'd end up with an
exact value which is the closest decimal to that number, but that may
not be exactly the same as the original value. (eg 1/3 becoming 0.333).
In both cases the conversion is "lossy" but the resultant value is
exact.

 

[Aaron Bertrand [SQL Server MVP]]

Just *one* point I can't resist here: I've never claimed that decimal

isn't exact. I've claimed that float is exact too.

The problem is, if I hard-code a value like 28.08, and store it in a FLOAT,
when I ask for that value back, I may not get that EXACT value. It doesn't
matter to me if that approximation occurred during conversion, or
translation, or storage, or divine intervention. The value I thought I had
sent to the database has been changed on me.

A

 

[Jon Skeet [C# MVP]]

The problem is, if I hard-code a value like 28.08, and store it in a FLOAT,
when I ask for that value back, I may not get that EXACT value.

Indeed - and that's why you want to use DECIMAL in such a situation.
I've never denied the usefulness of DECIMAL in the slightest.

It doesn't matter to me if that approximation occurred during
conversion, or translation, or storage, or divine intervention. The
value I thought I had sent to the database has been changed on me.

It may not matter to you, but accuracy in description of types matters
to me. It's a bit like people claiming that "objects are passed by
reference by default" in C#, or that "value types live on the stack and
reference types live on the heap" - both statements sound okay on first
hearing, but dig a little deeper and they're inaccurate. I like to be
accurate from the start (where I can, at least). Why claim that it's
the type which is approximate rather than the conversion? Where's the
benefit?

 

[Mike C#]

Just *one* point I can't resist here: I've never claimed that decimal
isn't exact. I've claimed that float is exact too.

I pointed this out in Argument B above: You enter 10.0, the computer
converts it to an approximate FLOAT base-2 representation of "9.999999" and
stores that value. Since "9.999999" can be stored "exactly" using the IEEE
floating point standard, it follows that the FLOAT data type must be exact
too.

Hogwash. The reason it is "approximate" is because it is converted to an
Base-2 approximate representation since the exact number can't be
represented in Base-2. The reason the other types are exact is because they
don't convert to Base-2 but instead store the decimal digits in a packed
format so no conversion to a different base needs to take place.

Once you've got a float value, you can always convert it to an exact
decimal string too, and indeed I've got C# code to do it, referenced
from
http://www.pobox.com/~skeet/csharp/floatingpoint.html
There's also an online version where you can type in the decimal value
you want to find the closest double to, and then show the exact value
of the double. (These are C# floats and doubles, but the principle is
the same). For example, the closest double to 28.08 is *exactly*
28.0799999999999982946974341757595539093017578125

But 28.0799999999999982946974341757595539093017578125 is not *EXACTLY*
28.08. It is an approximation. Hence the word "approximate".

Perhaps not surprisingly, the closest NUMERIC(4, 2) to 28.08 is *EXACTLY*
28.08! And the *EXACT* string representation of the NUMERIC(4, 2) value
28.08 is *EXACTLY* 28.08.

My point (which I thought I'd made again and again, but clearly not
enough) is that it's the conversion from a decimal literal to a
floating point value which is an "approximate" conversion, *not* the
float value itself. The point about base 3 is that the same would be
true - convert a base 3 number to a decimal and you'd end up with an
exact value which is the closest decimal to that number, but that may
not be exactly the same as the original value. (eg 1/3 becoming 0.333).
In both cases the conversion is "lossy" but the resultant value is
exact.

"You'd end up with an exact value which is the closest decimal to that
number". Again you're talking about base conversions here, which is good as
it happens to be the root of the problem with FLOAT. However, DECIMAL and
NUMERIC types were introduced because sometimes - that is to say every once
in a while - you actually want the EXACT value back that you originally fed
into the computer.

Every once in a while when you deposit $100 in the bank, you might want to
later take $100 out. You obviously can't do that if the bank "approximates"
the value of your deposit down to $92 every time you deposit. It would be
interesting to watch a bank manager explain to his customer that $92 is the
EXACT value of their deposit since it is an EXACT number, even though it's
not EXACTLY the amount they deposited. After all 92 is an EXACT number, it
also happens to be EXACTLY *not* 100...

The loss of information from conversion to a different base is what ANSI had
in mind when they applied the terms "approximate" and "exact". The loss of
information from truncating an infinitely repeating decimal is neither here
nor there no matter what media you are storing data on. You can expect
truncation under any circumstances in which you have an inifinitely long
repeating decimal and a finite amount of storage - whether it's computer
memory or a college-ruled notebook. The fact that FLOAT can't represent
many simple non-repeating decimals without corruption is what makes it
"approximate". I can't say it any more plainly than that.

 

[Aaron Bertrand [SQL Server MVP]]

Why claim that it's

the type which is approximate rather than the conversion? Where's the
benefit?

Because in SQL Server there is no plausible distinction. I pass in 28.08, I
get back a lot more decimals, it really is irrelevant whether it's the type
of the conversion or something else.

 

[Mike C#]

Why claim that it's
the type which is approximate rather than the conversion? Where's the
benefit?

Because some MVP will jump in and claim that the conversion cannot
technically be called "approximate". After all, the conversion is a
hard-coded set of instructions that perform the same function every time and
produce the same value given the same input every time. Sounds fairly exact
to me.

 

[Jon Skeet [C# MVP]]

I pointed this out in Argument B above: You enter 10.0, the computer
converts it to an approximate FLOAT base-2 representation of "9.999999" and
stores that value. Since "9.999999" can be stored "exactly" using the IEEE
floating point standard, it follows that the FLOAT data type must be exact
too.

Indeed. The value is 9.999999, and that value is stored exactly.

Hogwash. The reason it is "approximate" is because it is converted to an
Base-2 approximate representation since the exact number can't be
represented in Base-2. The reason the other types are exact is because they
don't convert to Base-2 but instead store the decimal digits in a packed
format so no conversion to a different base needs to take place.

And that's great, so long as the numbers are originally specified in
base 10 - you don't need a conversion.

I distinguish between an approximate *conversion* and whether or not a
*type* is exact.

But 28.0799999999999982946974341757595539093017578125 is not *EXACTLY*
28.08. It is an approximation. Hence the word "approximate".

The conversion is an approximation, the value is exact.

Perhaps not surprisingly, the closest NUMERIC(4, 2) to 28.08 is *EXACTLY*
28.08! And the *EXACT* string representation of the NUMERIC(4, 2) value
28.08 is *EXACTLY* 28.08.

Yup, and if we didn't use base 10, we'd have (potentially) exactly the
same loss with DECIMAL. DECIMAL itself isn't inherently more "exact"
than FLOAT, it's only the fact that there's no lossy conversion
required when an initial value is specified in base 10 that leads to
this (IMO) invalid distinction.

"You'd end up with an exact value which is the closest decimal to that
number". Again you're talking about base conversions here, which is good as
it happens to be the root of the problem with FLOAT. However, DECIMAL and
NUMERIC types were introduced because sometimes - that is to say every once
in a while - you actually want the EXACT value back that you originally fed
into the computer.

How many times do I have to say that I have nothing against DECIMAL,
that I can quite easily see why it exists, and that it is indeed the
most appropriate answer to the OP's dilemma?

The only issue I have is the claim that FLOAT is approximate but
DECIMAL is exact. The reality as I see it is that both FLOAT and
DECIMAL are exact, but conversions from base 10 to FLOAT are
approximate.

The fact that FLOAT can't represent
many simple non-repeating decimals without corruption is what makes it
"approximate". I can't say it any more plainly than that.

Again, that's a base-10-biased view of the world, rather than a
maths/computer science view, IMO. I like to look at the inherent
properties of a type, viewing conversions which may be used as separate
from the types themselves. Maybe it's because I'm a mathematician and
computer scientist by training rather than a banker, but it certainly
feels like a more deeply accurate view of the types involved than one
which is effectively based on how many fingers we have.

 

[Jon Skeet [C# MVP]]

Because in SQL Server there is no plausible distinction. I pass in 28.08, I
get back a lot more decimals, it really is irrelevant whether it's the type
of the conversion or something else.

You don't like to understand what's going on beneath the surface? Why
there's a loss, and why there *wouldn't* be a loss if you pass in a
value which is already in binary, and why (if you were able to do so)
you'd still lose information if you passed a base 3 value into a
DECIMAL?

<shrug> I guess I'm just inquisitive.

 

[Jon Skeet [C# MVP]]

Because some MVP will jump in and claim that the conversion cannot
technically be called "approximate". After all, the conversion is a
hard-coded set of instructions that perform the same function every time and
produce the same value given the same input every time. Sounds fairly exact
to me.

That means it's deterministic, not that it's exact. It certainly *is*
an approximation, and it's losing information. I'd certainly be
perfectly happy to argue the "approximate" side of that debate


Do either you or Aaron actually disagree that when you separate the
idea of "type" from "conversion" it's the *conversion* which is
approximate rather than the FLOAT type itself? If you do, it's probably
worth continuing to debate (despite my 5am post!) - if not, it's
presumably just a matter of how much we all do/don't care about making
the distinction between types and conversions. That's a matter of
opinion and probably not worth arguing about.

 

[Shuurai]

It may not matter to you, but accuracy in description of types matters
to me. It's a bit like people claiming that "objects are passed by
reference by default" in C#, or that "value types live on the stack and
reference types live on the heap" - both statements sound okay on first
hearing, but dig a little deeper and they're inaccurate. I like to be
accurate from the start (where I can, at least). Why claim that it's
the type which is approximate rather than the conversion? Where's the
benefit?

The benefit is that you're left with a useful definition. By your
reasoning, no datatype can ever be "exact" so the term is rendered
essentially useless. We can't ever store the "exact" value of 1/3 in
any meaningful way.

In this case the terminology is more or less describing the way the
datatype can be expected to perform. When storing a valid value
(28.08) in a DECIMAL column you can expect to get exactly the same
value back again. Store the same value in FLOAT and you get an
approximation.

 

[Adrian Gallero]

We can't ever store the "exact" value of 1/3 in
any meaningful way.

Hate to add more noise to this thread, but I dissagree.

You can store 1/3 exactly, just not on base 10.
If you use for example to base 24 (who does not use base 24 on a day by
day basis...), you can store 1/3 of a day exactly.
8 hours.

 

[Jon Skeet [C# MVP]]

The benefit is that you're left with a useful definition. By your
reasoning, no datatype can ever be "exact" so the term is rendered
essentially useless. We can't ever store the "exact" value of 1/3 in
any meaningful way.

No, by my definition pretty much all datatypes are exact. (I can't
think of any that aren't, offhand.) Conversions between datatypes (at
least numeric ones) tend to be lossy.

Let's look at another example - INTEGER. Is INTEGER approximate? I
wouldn't say so - but if you tried to convert 28.08 into an INTEGER,
you'd certainly lose information, because that conversion is lossy.
Is INTEGER described as an "approximate" type by ANSI? I doubt it
somehow...

In this case the terminology is more or less describing the way the
datatype can be expected to perform. When storing a valid value
(28.08) in a DECIMAL column you can expect to get exactly the same
value back again. Store the same value in FLOAT and you get an
approximation.

So when there's no lossy conversion involved, there's no loss of
information, but when there *is* a lossy conversion involved,
information is lost. That's not exactly a surprise, and has nothing to
do with the types themselves, as far as I can see.

I don't see why people believe it's so difficult to explain/understand
that it's the conversion which is lossy, not the type.

Note that if you pass in floating binary point data rather than decimal
data, you *won't* lose information - because hey, again, there's no
conversion involved. That isn't clear if you regard the type itself as
being lossy, but it's absolutely obvious when you separate conversions
from types.

 

[Aaron Bertrand [SQL Server MVP]]

Because in SQL Server there is no plausible distinction. I pass in

You don't like to understand what's going on beneath the surface?

It's interesting, sure. But I don't have a problem with the standards
calling it an "approximate data type" if it's meant to describe how data
will potentially be stored differently from how it is provided to the
database. As I've said multiple times, it really isn't relevant in this
case whether the approximation occured during conversion or during storage
or during retrieval. And again, base 3 is about as useful as fool's gold
here, because base 3 does not come into play.

Unlike INT or DECIMAL, where "approximation" can occur because you used a
value that doesn't fit into the constraints you've defined, with FLOAT there
is much less control over how this happens, and it is much more surprising
to most users I have come across who chose FLOAT for reasons other than the
fact that they knew the nature of its conversion and storage. Do you know
how many people I have seen choose FLOAT for storing monetary values like
prices, or even quantities? And do you know how surprised they are to find
out that SQL Server changed 28.08 to 28.0800000000002? SQL Server
approximated the data that they passed in, and they don't really care how it
happened beneath the surface.

 

[Jon Skeet [C# MVP]]

It's interesting, sure. But I don't have a problem with the standards
calling it an "approximate data type" if it's meant to describe how data
will potentially be stored differently from how it is provided to the
database. As I've said multiple times, it really isn't relevant in this
case whether the approximation occured during conversion or during storage
or during retrieval. And again, base 3 is about as useful as fool's gold
here, because base 3 does not come into play.

Well, I think it's useful for demonstrating that it's not the type
that's exact, merely the conversion from data which is already in
decimal form. I never claimed real world usefulness.

Unlike INT or DECIMAL, where "approximation" can occur because you used a
value that doesn't fit into the constraints you've defined, with FLOAT there
is much less control over how this happens, and it is much more surprising
to most users I have come across who chose FLOAT for reasons other than the
fact that they knew the nature of its conversion and storage. Do you know
how many people I have seen choose FLOAT for storing monetary values like
prices, or even quantities? And do you know how surprised they are to find
out that SQL Server changed 28.08 to 28.0800000000002? SQL Server
approximated the data that they passed in, and they don't really care how it
happened beneath the surface.

Do you think it would be hard to explain that it's the *conversion*
which loses the data, rather than giving the impression that FLOAT is
inherently inexact?

I don't doubt that people use FLOAT inappropriately, and that they are
indeed surprised by the lossy conversion, but I don't see how
describing the type as inaccurate is any clearer than saying
(accurately) that it's the conversion which loses the information.