To remove integer part of float numbers i use:
update ACTIVITIES set TIME = TIME - FLOOR(TIME) --TIME is float
this works anyway in the calculation there is some errors due to floating point calculation.
EDIT: I cannot modify the schema, TIME must stay float.
The reason i need to do this is that because of a bug the float numbers become > 1 even if the decimal part is still ok. So i need to remove the integer part.
I cannot reproduce it now, but i remember i had something like:
1.6666666667 becomes 0.6666542534, while it should be 0.6666666667.
Please note that this is legacy code so TIME is a float number, while if i'd write this from scratch i would use a TIME datatype.
So my question is: is this correct or can it be improved?
update ACTIVITIES set TIME = TIME - FLOOR(TIME)
Related
I am currently making a small MariaDB database and ran into the following problem:
I want to save a floatingpoint number with only 2 poistions after the decimal point but everything before the decimal point should be unaffected.
For example: 1.11; 56789.12; 9999.00; 999999999999.01 etc.
I have done some research and this is what I am using right now:
CREATE TABLE mytable (
mynumber DOUBLE(10, 2)
)
The problem with this solution is that I also have to limit the number of positions before the decimal point, what I don't want to do.
So is there a possibility to limit the number of positions after the decimal point without affecting the positions before the decimal point or is there a "default number" I can use for the positions before the decimal point?
Don't use (m,n) with FLOAT or DOUBLE. It does nothing useful; it does cause an extra round.
DECIMAL(10,2) is possible; that will store numbers precisely (to 2 decimal places).
See also ROUND() and FORMAT() for controlling the rounding for specific values.
You had a mistake -- 999999999999.01 won't fit in DOUBLE(10,2), nor DECIMAL(10,2). It can handle only 8 (=10-2) digits to the left of the decimal point.
You can create a trigger that intercepts INSERT and UPDATE statements and truncates their value to 2 decimal places. Note, however, that due to how floating point numbers work at machine level, the actual number may be different.
Double precision numbers are accurate up to 14 significant figures, not a certain number of decimal points. Realistically, you need to detemine what is the biggest value you might ever want to store. Once you have done that, the DECIMAL type may be more appropriate for what you are trying to do.
See here for more details:
https://dev.mysql.com/doc/refman/8.0/en/precision-math-decimal-characteristics.html
I have a weird problem if you can call it a problem that is.
Sorry in advance, the database is in french.
I have a table which hold the time a user passed on a specific task
I want to sum the time passed for every task
I'm able to get a sum from the database but the data is kind of wierd
The field is a real number to start with
Example, if I sum 0,35 + 0,63 + 1 I should get 1,98 Data without a sum:
But instead Access give me 1,97999998927116 Data with sum:
If I was to sum only integer the number would be correct
I know I could simply use a round function to get rid of it.
But I would like to know why it does this.
This is because Sum uses floating-point arithmetic if you execute it on a column that is defined as a Single or a Double
Floating-point arithmetic is often inaccurate.
You can avoid these kinds of errors by defining your column as a Decimal or as Currency
I had a table with two columns for coordinates stored in. These columns were REAL datatype, and I noticed that from my application it was only showing 5 decimals for coordinates, and positions were not accurate enough.
I decided to change datatype to FLOAT, so I could use more decimals. It was for my pleasant surprise that when I changed the column data type, the decimals suddenly appeared without me having to store all the coordinates again.
Anyone can tell me why this happens? What happens with the decimal precision on REAL datatype?. Isn´t the data rounded and truncated when inserted? Why when I changed the datatype the precision came up with no loss of data?..
You want to use a Decimal data-type.
Floating point values are caluclated by a value and an exponenent. This allows you have store huge number representations in small amounts of memory. This also means that you don't always get exactly the number you're looking for, just very very close. This is why when you compare floating point values, you compare them within a certain tolerance.
It was for my pleasant surprise that when I changed the column data type, the decimals suddenly appeared without me having to store all the coordinates again.
Be careful, this doesn't mean that the value that was filled in is the accurate value of what you're looking for. If you truncated your original calculation, you need to get those numbers again without cutting off any precision. The values that it autofills when you convert from Real to Float aren't the rest of what you truncated, they are entirely new values which result from adding more precision to the calculation used to populate your Real value.
Here is a good thread that explains the difference in data-types in SQL:
Difference between numeric, float and decimal in SQL Server
Another helpful link:
Bad habits to kick : choosing the wrong data type
We are stuck with a database that (unfortunately) uses floats instead of decimal values. This makes rounding a bit difficult. Consider the following example (SQL Server T-SQL):
SELECT ROUND(6.925e0, 2) --> returns 6.92
ROUND does round half up, but since floating point numbers cannot accurately represent decimal numbers, the "wrong" result (from the point of view of the end-user) is displayed. I understand why this happens.
I already came up with two possible solutions (both returning a float, which is, unfortunately, also a requirement):
Convert to a decimal data type before rounding: SELECT CONVERT(float, ROUND(CONVERT(decimal(29,14), 6.925e0), 2))
Multiply until the third digit is on the left-hand side of the decimal point (i.e. accurately represented), and then do the rounding: SELECT ROUND(6.925e0 * 1000, -1) / 1000
Which one should I choose? Is there some better solution? (Unfortunately, we cannot change the field types in the database due to some legacy applications accessing the same DB.)
Is there a well-established best practice solution for this (common?) problem?
(Obviously, the common technique "rounding twice" will not help here since 6.925 is already rounded to three decimal places -- as far as this is possible in a float.)
Your first solution seems safer, and also seems like a conceptually closer fit to the problem: convert as soon as possible from float to decimal, do all relevant calculations within the decimal type, and then do a last minute conversion back to float before writing to the DB.
Edit: You'll likely still need to do an extra round (e.g. to 3 decimal places, or whatever's appropriate for your application) immediately after retrieving the float value and converting to decimal, to make sure that you end up with the decimal value that was actually intended. 6.925e0 converted to decimal would again be likely (assuming that the decimal format has > 16 digits of precision) to give something that's very close to, but not exactly equal to, 6.925; an extra round would take care of this.
The second solution doesn't look reliable to me: what if the stored value for 6.925e0 happens to be, due to the usual binary floating-point issues, a tiny amount too small? Then after multiplication by 1000, the result may still be a touch under 6925, so that the rounding step rounds down instead of up. If you know your value always has at most 3 digits after the point, you could fix this by doing an extra round after multiplying by 1000, something like ROUND(ROUND(x * 1000, 0), -1).
(Disclaimer: while I have plenty of experience dealing with float and decimal issues in other contexts, I know next to nothing about SQL.)
Old question, but I am surprised that the normal practice is not mentioned here, so I just add it.
Normally, you would add a small amount that you know is much smaller than the accuracy of the numbers you are working with, e.g. like this:
SELECT ROUND(6.925e0 + 1e-7, 2)
Of course the added amount must be larger than the precision of the floating point type that is used.
Use an arbitrary-precision format such as DECIMAL. That way you can leave it to the language to get it right (or wrong as the case may be).
I managed to round the float column correctly using the following command:
SELECT CONVERT(float, ROUND(ROUND(CONVERT(decimal(38,14),float_column_name),3),2))
I inherited a project that uses SQL Server 200x, wherein a column that stores a value that is always considered as a percentage in the problem domain is stored as its greater than 1 decimal equivalent. For example, 70% (0.7, literally) is stored as 70, 100% as 100, etc. Aside from the need to remember to * 0.01 on retrieved values and * 100 before persisting values, it doesn't seem to be a problem in and of itself. It does make my head explode though... so is there a good reason for it that I'm missing? Are there compelling reasons to fix it, given that there is a fair amount of code written to work with the pseudo-percentages?
There are a few cases where greater than 100% occurs, but I don't see why the value wouldn't just be stored as 1.05, for example, in those cases.
EDIT: Head feeling better, and slightly smarter. Thanks for all the insights.
There are actually four good reasons I can think of that you might want to store—and calculate with—whole-number percentage values rather than floating-point equivalents:
Depending on the data types chosen, the integer value may take up less space.
Depending on the data type, the floating-point value may lose precision (remember that not all languages have a data type equivalent to SQL Server's decimal type).
If the value will be input from or output to the user very frequently, it may be more convenient to keep it in a more user-friendly format (decision between convert when you display and convert when you calculate ... but see the next point).
If the principle values are also integers, then
principle * integerPercentage / 100
which uses all integer arithmetic is usually faster than its floating-point equivalent (likely significantly faster in the case of a floating-point type equivalent to T-SQL's decimal type).
If its a byte field then it takes up less room in the db than floating point numbers, but unless you have millions and millions of records, you'll hardly see a difference.
Since floating-point values can't be compared for equality, an integer may have been used to make the SQL simpler.
For example
(0.3==3*.1)
is usually False.
However
abs( 0.3 - 3*.1 )
Is a tiny number (5.55e-17). But it's pain to have to do everything with (column-SomeValue) BETWEEN -0.0001 AND 0.0001 or ABS(column-SomeValue) < 0.0001. You'd rather do column = SomeValue in your WHERE clause.
Floating point numbers are prone to rounding errors and, therefore, can act "funny" in comparisons. If you always want to deal with it as fixed decimal, you could either choose a decimal type, say decimal(5,2), or do the convert and store as int thing that your db does. I'd probably go the decimal route, even though the int would take up less space.
A good guess is because anything you do with integers (storing, calculating, stuffing into an edit for for a user, etc.) is marginally easier and more efficient than doing the same with floating point numbers. And the rounding issues aren't so obvious when you look at the data.
If these are numbers that end users are likely to see and interact with, percentages are easier to understand than decimals.
This is one of those situations where a notation aid can help; in the program, be consistent in using a prefix (Hungarian) or postfix to specify values that are percentages vs. those that are decimal. If you can extend a naming convention to the database fields themselves, so much the better.
And to add to the data storage issue, if you can use integer arithmetic for whatever processing you are doing, the performance is much better than when doing floating point arithmetic... So storing ther percetages as integer values may allow the processing logic to itilize integer arithmetic
If you're actually using them as a coefficient (or expect users of the database to do this sort of thing in reports), there's a case for storing them as a coefficient - particularly if there's a reason to do calculations involving more than one.
However, if you do this you should be consistent - either all percentages or all coefficients.