I have a table with more than 5000 pieces of data, and it takes more than 1.2 seconds to query the full amount or limit each time. May I ask how to locate the problem?
CREATE STABLE daily_kline (time timestamp, open double, close double, high double, low double, vary double, vary_rate double, pre_open double, pre_close double, pre_high double, pre_low double, pre_vary double, pre_vary_rate double, suf_open double, suf_close double, suf_high double, suf_low double, suf_vary double, suf_vary_rate double, volume double, turnover double, after_volume double, after_turnover double) TAGS (market nchar(4), type nchar(8));
OS: [CentOS 7.0]
Memory, CPU, current Disk Spaceļ¼4c, 16g, 46g
TDengine Version [3.0.1.4]
Related
I'm expanding on a similar question titled Is SQL Server 'MONEY' data type a decimal floating point or binary floating point?
The accepted answer told us that the "money" datatype is fixed-point rather than floating-point type, but didn't say whether it was binary-fixed-point or decimal-fixed-point.
I'm assuming it's decimal-fixed-point but I can't find confirmation of this anywhere.
The documentation tells us the range and size, but not the underlying implementation.
Not sure why you care about the underlying implementation but you can CAST a money data type value to binary(8) to see the value's bits:
DECLARE #money money;;
--same as min 64-bit signed integer (2's compliment) with 4 decimal places assumed
SET #money = -922337203685477.5808;
SELECT CAST(#money AS binary(8)); --0x8000000000000000
--same as max 64-bit signed integer with 4 decimal places assumed
SET #money = 922337203685477.5807
SELECT CAST(#money AS binary(8)); --0x7FFFFFFFFFFFFFFF
So money looks to be a 64 bit signed integer with 4 decimal places assumed. The precision/scale is not included with the value with money (and it's smallmoney cousin).
I'm trying to multiply two values. One is a decimal and one is numeric.
Example - Total is what I want:
Number Decimal Total
900 1.111 999.9
800 1.25 1000
460 4.25 1955
In my Sql query, I've tried the following:
(ISNUMERIC(UpgradeEmptyNodesPercentageLimitForAllocation) * RawTotalNodes) as ExpectedEmptyNodeCountForUpgrade
However, it always returns Number. How do the above?
Thanks...
Check the scale on your data types, if they are zero then SQL will remove the decimals
DECLARE #d1 DECIMAL(18,0) = 1.111
DECLARE #d2 DECIMAL(18,10) = 1.111
SELECT #d1,#d2
ISNUMERIC is really for evaluating strings, not number types -- and it only returns 0 or 1. I think what you want to do is a CAST of your numeric and/or decimal values to one with more precision, and then multiply. What is affecting the operation is the precision and scale of both factors to your operation, as-stored.
These types, by name, are actually interchangeable - but precision and scale are not.
Try casting both to a NUMERIC of some acceptabe precision and scale, and then multiplying. Or, if you don't have a precision and scale that will always work, then cast to REAL, if that's an option.
Read more on MSDN.
I am looking for a division result that is extremely accurate.
This SQL returns the following results:
SELECT (CAST(297282.26 AS DECIMAL(38, 30)) / CAST(495470.44 AS DECIMAL(38, 30))) AS ResultDecimal
SELECT (CAST(297282.26 AS FLOAT) / CAST(495470.44 AS FLOAT)) AS ResultFloat
Here is the accurate result from WolframAlpha:
http://www.wolframalpha.com/input/?i=297282.26%2F495470.44
I was under the impression that DECIMAL would be more accurate than FLOAT:
"Because of the approximate nature of the float and real data types, do not use these data types when exact numeric behavior is required, such as in financial applications, in operations involving rounding, or in equality checks. Instead, use the integer, decimal, money, or smallmoney data types."
https://technet.microsoft.com/en-us/library/ms187912(v=sql.105).aspx
Why does the FLOAT calculation give me a result more accurate than when using DECIMAL?
I found the best precision to be when you use:
SELECT (CAST(297282.26 AS DECIMAL(15, 9)) / CAST(495470.44 AS DECIMAL(24, 2))) AS ResultDecimal
This gives a result of
0.599999991926864496699338915153
I think the actual value (to 100 digits) is:
0.5999999919268644966993389151530412187657451370862810705720405842980259326873264124495499670979362562...
Please bear in mind SQL Server defines the maximum precision and scale for division as:
max precision = (p1 - s1 + s2) + MAX(6, s1 + p2 + 1) -- up to 38
max scale = MAX(6, s1 + p2 + 1)
Where p1 & p2 are the precision of the two numbers and s1 & s2 are the scale of the numbers.
In this case the maximum precision is (15-9+2) + MAX(6, 9+24+1) = 8 + 34 = 42.
However SQL Server only allows a maximum precision of 38.
The maximum scale = MAX(6, 9+24+1) = 34
Hopefully you already understand that just because the FLOAT version presents more numbers after the decimal point, doesn't necessarily mean that those are the true numbers. This is about precision, not accuracy.
It is the CAST function itself that causes this loss of precision, not the difference between the FLOAT and DECIMAL data types.
To demonstrate this, compare your previous results to the result of this:
SELECT 297282.26 / 495470.44 AS ResultNoCast
In my version of the query, the presence of a decimal point in the literal numbers tells SQL Server to treat the values as DECIMAL datatype, with appropriate length and precision as determined by the server. The result is more precise than when you CAST explicitly to DECIMAL.
A clue to the reason for this can be found hidden in the official documentation of the CAST function, under Truncating and Rounding Results:
When you convert data types that differ in decimal places, sometimes the result value is truncated and at other times it is rounded. The following table shows the behavior.
From | To | Behavior
numeric | numeric | Round
So the fact that each separate literal value is treated as a NUMERIC (same thing as DECIMAL) on the way in, and is being casted to NUMERIC, causes rounding.
Anticipating your next question a little, if you want a more precise result from the NUMERIC/DECIMAL datatype, you just need to tell SQL Server that each component of the calculation is more precise:
SELECT 297282.26000000 / 495470.44000000 AS ResultSuperPrecise
This appears (from experimentation) to be the most precise I can get: either adding or removing a 0 from either the numerator or denominator makes the result less precise. I'm at a loss to explain why that is, because the result is only 23 digits to the right of the decimal point.
It doesn't give you a more accurate result. I say that because the value is an approximate and not all values will be available to stored in a float. On the other side of that coin though is that float has the possibility of a lot more precision. The maximum precision of a decimal/numeric is 38. https://msdn.microsoft.com/en-us/library/ms187746.aspx
When you look at float though the maximum precision is 53. https://msdn.microsoft.com/en-us/library/ms173773.aspx
Okay, here is what I think is going on.
#philosophicles - I think you are right in that the CAST is causing the problem, but not because I am trying to "convert data types that differ in decimal places".
When I execute the following statement
SELECT CAST((297282.26 / 495470.44) AS DECIMAL(38, 30)) AS ResultDecimal
The accurate result for the calculation is
This has way more than 30 digits after the decimal point, and my data type has scale set to 30. So the CAST rounds the value, then just adds zeros to the end until there are 30 digits. We end up with this:
So the interesting thing is how does the CAST determine up to how many decimals to round or truncate the output? I am not sure, but as #philosophicles pointed out, the scale of the input effects the rounding applied on the output.
SELECT CAST(((297282.26/10000) / (495470.44/10000)) AS DECIMAL(38, 30)) AS ResultDecimal
Thoughts?
Also interesting:
However, in simple terms, precision is lost when the input scales are
high because the result scales need to be dropped to 38 with a
matching precision drop.
https://dba.stackexchange.com/questions/41743/automatic-decimal-rounding-issue
The precision and scale of the numeric data types besides decimal are fixed.
https://dba.stackexchange.com/questions/41743/automatic-decimal-rounding-issue
It is really strange how auto convert between numeric data behaves in T-Sql
Declare #fdays as float(12)
Declare #mAmount As Money
Declare #fDaysi as float(12)
Set #fdays =3
Set #fdaysi =1
Set #mAmount=527228.52
Set #mAmount = #fdaysi * #mAmount/#fDays
Select #mAmount, 527228.52/3
The result of this computation is
175742.8281 175742.840000
Does this occur because money and float are not actually the same kind of numeric data? Float is Approximate Numeric and Money is Exact Numeric
Money and Decimal are fixed numeric datatypes while Float is an
approximate numeric datatype. Results of mathematical operations on
floating point numbers can seem unpredictable, especially when
rounding is involved. Be sure you understand the significance of the
difference before you use Float!
Also, Money doesn't provide any advantages over Decimal. If fractional
units up to 5 decimal places are not valid in your currency or
database schema, just use Decimal with the appropriate precision and
scale.
ref link : http://www.sqlservercentral.com/Forums/Topic1408159-391-1.aspx
Should you choose the MONEY or DECIMAL(x,y) datatypes in SQL Server?
https://dba.stackexchange.com/questions/12916/datatypes-in-sql-server-difference-between-similar-dataypes-numeric-money
float [ (n) ]
Where n is the number of bits that are used to store the mantissa of the float number in scientific notation and, therefore, dictates the precision and storage size. If n is specified, it must be a value between 1 and 53. The default value of n is 53.
When n in 1-24 then precision is 7 digits.
When n in 25-53 then precision is 15 digits.
So in your example precision is 7 digits, thus first part #fdaysi * #mAmount
rounds result to 7 digits 527228.5. The second part returns 527228.5/3=175742.828 and casting 175742.828 to Money results in 175742.8281. So FLOAT and REAL are approximate data types and sometimes you get such surprises.
DECLARE #f AS FLOAT = '29545428.022495';
SELECT CAST(#f AS NUMERIC(28, 14)) AS value;
The result of this is 29545428.02249500200000 with just a casting.
I couldn't find anything that rejects or confirms whether SQL Server 'MONEY' data type is a decimal floating point or binary floating point.
In the description it says that MONEY type range is from -2^63 to 2^63 - 1 so this kind of implies that it should be a binary floating point.
But on this page it lists MONEY as "exact" numeric. Which kind of suggests that MONEY might be a decimal floating point (otherwise how is it exact? or what is the definition of exact?)
Then if MONEY is a decimal floating point, then what is the difference between MONEY and DECIMAL(19,4) ?
Neither. If it were an implementation of floating point it would be subject to the same inaccuracies as FLOAT and REAL types. See Floating Point on wikipedia.
MONEY is a fixed point type.
It's one byte smaller than a DECIMAL(19,4), because it has a smaller range (922,337,203,685,477.5808 to 922,337,203,685,477.5807) as opposed to (-10^15+1 to 10^15-1).
To see the differences we can look at the documentation:
Documentation for money:
Data type Range Storage
money -922,337,203,685,477.5808 to 922,337,203,685,477.5807 8 bytes
smallmoney -214,748.3648 to 214,748.3647 4 bytes
The money and smallmoney data types are accurate to a ten-thousandth of the monetary units that they represent.
Compare to decimal:
When maximum precision is used, valid values are from -10^38 + 1 through 10^38 - 1.
Precision Storage
1 - 9 5 bytes
10 - 19 9 bytes
20 - 28 13 bytes
29 - 38 17 bytes
So they're not exactly equivalent, just similar. A DECIMAL(19,4) has a slightly greater range than MONEY (it can store from -10^15 + 0.0001 to 10^15 - 0.0001), but also needs one more byte of storage.
In other words, this works:
CREATE TABLE Table1 (test DECIMAL(19,4) NOT NULL);
INSERT INTO Table1 (test) VALUES
(999999999999999.9999);
SELECT * FROM Table1
999999999999999.9999
But this doesn't:
CREATE TABLE Table1 (test MONEY NOT NULL);
INSERT INTO Table1 (test) VALUES
(999999999999999.9999);
SELECT * FROM Table1
Arithmetic overflow error converting numeric to data type money.
There's also a semantic difference. If you want to store monetary values, it makes sense to use the type money.
I think the primary difference will be the storage space required.
DECIMAL(19,4) will require 9 storage bytes
MONEY will require 8 storage bytes