![]() If character sets are too different, there will be two traslations: first from column charset to Unicode and then from Unicode to the connection charset.Īlso, you can request transliteration manually by CASTing the column to another charset, example:ĬAST(column_name AS varchar(100) character set WIN1251). There is an automatic one:Įvery piece of data you retrieve from database (via SELECT or otherwise) is transliterated from character set of database table's column to connection character set. There are various points where character set transliteration occurs. This happens when you have data in database stored in one character set, but the transliteration to required character set fails. Similar goes for intermediary values stored in stored procedure or trigger variables. Or maybe you really need to increase the column size. If the result goes into a table column, perhaps it's a valid error. It happens when the concatenated string doesn't fit the underlying CHAR or VARCHAR datatype size. Which would yield numeric(18,6) result, but it is quite possible that you would get more accurate result by casting to double:Ĭast(cast(value1 as double precision) * cast(value2 as double precision) as numeric(18,6))Īlso, if you have mixed multiplications and divisions it helps to change the order of operations, so that the overflow doesn't happen. If (for example) you wish to keep only 6 digits of precision, you could use something like:Ĭast(value1 as numeric(18,3)) * cast(value2 as numeric(18,3)) For example, numeric(18,6) times numeric(18,6) yields numeric(18,12) result, meaning that maximal value it can store is 9223372.036854775807. Problems are rarely seen with such low precision as 2. Therefore, the result is stored in numeric(18,4). Doesn't look much, but when you have complex calculations, you can easily loose thousands (dollars or euros). If Firebird would store that into numeric(18,2) datatype, we would lose 0.0032. Here's an example: if you multiply 9.12 with 8.11 (both numeric(18,2)) you would get 73.9632. Otherwise you need to check every operation and calculate the result. ![]() If it works and you don't care about being too precise, you can leave it at that. Try casting the values in complex expressions as double precision and see whether the error goes away. If you use fixed precision datatypes (smallint, integer, bigint, decimal and numeric), it is possible that the result of calculation doesn't fit the datatype. SELECT x, CASE when x = 0 then 0 else 10/x END Here's an example showing how to use CASE: ![]() Depending on your requirements, it might be better to use CASE (which is available since Firebird 1.5). Please note that DZERO would return zero even for NULL input. Or you can use DZERO function from rFunc UDF library. Is it possible that some of the values can be zero? If this is the case, you can use the CASE statement to replace the zero with something else. Take a look and see if you have any divisions in your equasions. ![]() This error message is actually the same for different kind of errors. The best method is to remove parts of the query one-by-one until you discover the problematic one. This error message can be caused by various things, and it's isn't always easy to determine the cause. Arithmetic exception, numeric overflow, or string truncation ![]()
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