I am attempting to run a simple UPDATE script on an integer field, whereby the trailing 2 numbers are "kept", and the leading numbers are removed. For example, "0440" would be updated as "40." I can get the desired data in a SELECT statement, such as
SELECT RIGHT(field_name::varchar, 2)
FROM table_name;
However, I run into an error when I try to use this same functionality in an UPDATE script, such as:
UPDATE schema_name.table_name
SET field_name = RIGHT(field_name::varchar, 2);
The error I receive reads:
column . . . is of type integer but expression is of type text . . .
HINT: You will need to rewrite or cast the expression
You're casting the integer to varchar but you're not casting the result back to integer.
UPDATE schema_name.table_name
SET field_name = RIGHT(field_name::TEXT, 2)::INTEGER;
The error is quite straight forward - right returns textual data, which you cannot assign to an integer column. You could, however, explicitly cast it back:
UPDATE schema_name.table_name
SET field_name = RIGHT(field_name::varchar, 2)::int;
1 is a digit (or a number - or a string), '123' is a number (or a string).
Your example 0440 does not make sense for an integer value, since leading (insignificant) 0 are not stored.
Strictly speaking data type integer is no good to store the "trailing 2 numbers" - meaning digits - since 00 and 0 both result in the same integer value 0. But I don't think that's what you meant.
For operating on the numeric value, don't use string functions (which requires casting back and forth. The modulo operator % does what you need, exactly: field_name%100. So:
UPDATE schema_name.table_name
SET field_name = field_name%100
WHERE field_name > 99; -- to avoid empty updates
Related
In my sqlalchemy ( sqlalchemy = "^1.4.36" ) query I have a clause:
.filter( some_model.some_field[2].in_(['item1', 'item2']) )
where some_field is jsonb and the value in some_field value in the db formatted like this:
["something","something","123"]
or
["something","something","0123"]
note: some_field[2] is always digits-only double-quoted string, sometimes with leading zeroes and sometimes without them.
The query works fine for cases like this:
.filter( some_model.some_field[2].in_(['123', '345']) )
and fails when the values in the in_ clause have leading zeroes:
e.g. .filter( some_model.some_field[2].in_(['0123', '0345']) ) fails.
The error it gives:
cursor.execute(statement, parameters)\\npsycopg2.errors.InvalidTextRepresentation: invalid input syntax for type json\\nLINE 3: ...d_on) = 2 AND (app_cache.value_metadata -> 2) IN (\\'0123\\'\\n ^\\nDETAIL: Token \"0123\" is invalid.
Again, in the case of '123' (or any string of digits without leading zero) instead of '0123' the error is not thrown.
What is wrong with having leading zeroes for the strings in the list of in_ clause? Thanks.
UPDATE: basically, sqlachemy's IN_ assumes int input and fails accordingly. There must be some reasoning behind this behavior, can't tell what it is. I removed that filter fromm the query and did the filtering of the ouput in python code afterwards.
The problem here is that the values in the IN clause are being interpreted by PostgreSQL as JSON representations of integers, and an integer with a leading zero is not valid JSON.
The IN clause has a value of type jsonb on the left hand side. The values on the right hand side are not explicitly typed, so Postgres tries to find the best match that will allow them to be compared with a jsonb value. This type is jsonb, so Postgres attempts to cast the values to jsonb. This works for values without a leading zero, because digits in single quotes without leading zeroes are valid representations of integers in JSON:
test# select '123'::jsonb;
jsonb
═══════
123
(1 row)
but it doesn't work for values with leading zeroes, because they are not valid JSON:
test# select '0123'::jsonb;
ERROR: invalid input syntax for type json
LINE 1: select '0123'::jsonb;
^
DETAIL: Token "0123" is invalid.
CONTEXT: JSON data, line 1: 0123
Assuming that you expect some_field[2].in_(['123', '345']) and some_field[2].in_(['0123', '345']) to match ["something","something","123"] and ["something","something","123"] respectively, you can either serialise the values to JSON yourself:
some_field[2].in_([json.dumps(x) for x in ['0123', '345']])
or use the contained_by operator (<# in PostgreSQL), to test whether some_field[2] is present in the list of values:
some_field[2].contained_by(['0123', '345'])
or cast some_field[2] to text (that is, use the ->> operator) so that the values are compared as text, not JSON.
some_field[2].astext.in_(['0123', '345'])
I hava data in my database and i need to select all data where 1 column number is between 1-100.
Im having problems, because i cant use - between 1 and 100; Because that column is character varying, not integer. But all data are numbers (i cant change it to integer).
Code;
dst_db1.eachRow("Select length_to_fault from diags where length_to_fault between 1 AND 100")
Error - operator does not exist: character varying >= integer
Since your column supposed to contain numeric values but is defined as text (or version of text) there will be times when it does not i.e. You need 2 validations: that the column actually contains numeric data and that it falls into your value restriction. So add the following predicates to your query.
and length_to_fault ~ '^\+?\d+(\.\d*)?$'
and length_to_fault::numeric <# ('[1.0,100.0]')::numrange;
The first builds a regexp that insures the column is a valid floating point value. The second insures the numeric value fall within the specified numeric range. See fiddle.
I understand you cannot change the database, but this looks like a good place for a check constraint esp. if n/a is the only non-numeric are allowed. You may want to talk with your DBA ans see about the following constraint.
alter table diags
add constraint length_to_fault_check
check ( lower(length_to_fault) = 'n/a'
or ( length_to_fault ~ '^\+?\d+(\.\d*)?$'
and length_to_fault::numeric <# ('[1.0,100.0]')::numrange
)
);
Then your query need only check that:
lower(lenth_to_fault) != 'n/a'
The below PostgreSQL query will work
SELECT length_to_fault FROM diags WHERE regexp_replace(length_to_fault, '[\s+]', '', 'g')::numeric BETWEEN 1 AND 100;
I have field that has up to 9 comma separated values each of which have a string value and a numeric value separated by colon. After parsing them all some of the values between 0 and 1 are being set to an integer rather than a numeric as cast. The problem is obviously related to data type but I am unsure what is causing it or how to fix it. The problem only exists in the case statement, the split_part function seems to be working perfect.
Things I have tried:
nvl(split_part(one,':',2),0) = COALESCE types text and integer cannot be matched
nvl(split_part(one,':',2)::numeric,0) => Invalid input syntax for type numeric
numerous other cast/convert variations
(CASE WHEN split_part(one,':',2) = '' THEN 0::numeric ELSE split_part(one,':',2)::numeric END)::numeric => runs but get int value of 0
When using the split_part function outside of case statement it does work correctly. However, I need the result to be zero for null values.
split_part(one,':',2) => 0.02068278096187390979 (expected result)
When running the code above I get zero but expect 0.02068278096187390979
Field "one" has the following value 'xyz: 0.02068278096187390979' before the split_part function.
EXAMPLE:
create table test(one varchar);
insert into test values('XYZ: 0.50000000000000000000')
select
one ,split_part(one,':',2) as correct_value_for_those_that_are_not_null ,
case
when split_part(one,':',2) = '' then null
else split_part(one,':',2)::numeric
end::numeric as this_one_is_the_problem
from test
However, I need the result to be zero for null values.
Your example does not deal with NULL values at all, though. Only addressing the empty string ('').
To replace either with 0 reliably, efficiently and without casting issues:
SELECT part1, CASE WHEN part2 <> '' THEN part2::numeric ELSE numeric '0' END AS part2
FROM (
SELECT split_part(one, ':', 1) AS part1
, split_part(one, ':', 2) AS part2
FROM test
) sub;
See:
Best way to check for "empty or null value"
Also note that all SQL CASE branches must agree on a common data type. There have been minor adjustments in the logic that determines the resulting type in the past, so the version of Postgres may play a role in corner cases. Don't recall the details now.
nvl()is not a Postgres function. You probably meant COALESCE. The manual:
This SQL-standard function provides capabilities similar to NVL and IFNULL, which are used in some other database systems.
original query looks like this :
UPDATE reponse_question_finale t1, reponse_question_finale t2 SET
t1.nb_question_repondu = (9-(ISNULL(t1.valeur_question_4)+ISNULL(t1.valeur_question_6)+ISNULL(t1.valeur_question_7)+ISNULL(t1.valeur_question_9))) WHERE t1.APPLICATION = t2.APPLICATION;
I know you cannot update 2 tables in a single query so i tried this :
UPDATE reponse_question_finale t1
SET nb_question_repondu = (9-(COALESCE(t1.valeur_question_4,'')::int+COALESCE(t1.valeur_question_6,'')::int+COALESCE(t1.valeur_question_7)::int+COALESCE(t1.valeur_question_9,'')::int))
WHERE t1.APPLICATION = t1.APPLICATION;
But this query gaves me an error : invalid input syntax for integer: ""
I saw that the Postgres equivalent to MySQL is COALESCE() so i think i'm on the good way here.
I also know you cannot add varchar to varchar so i tried to cast it to integer to do that. I'm not sure if i casted it correctly with parenthesis at the good place and regarding to error maybe i cannot cast to int with coalesce.
Last thing, i can certainly do a co-related sub-select to update my two tables but i'm a little lost at this point.
The output must be an integer matching the number of questions answered to a backup survey.
Any thoughts?
Thanks.
coalesce() returns the first non-null value from the list supplied. So, if the column value is null the expression COALESCE(t1.valeur_question_4,'') returns an empty string and that's why you get the error.
But it seems you want something completely different: you want check if the column is null (or empty) and then subtract a value if it is to count the number of non-null columns.
To return 1 if a value is not null or 0 if it isn't you can use:
(nullif(valeur_question_4, '') is null)::int
nullif returns null if the first value equals the second. The IS NULL condition returns a boolean (something that MySQL doesn't have) and that can be cast to an integer (where false will be cast to 0 and true to 1)
So the whole expression should be:
nb_question_repondu = 9 - (
(nullif(t1.valeur_question_4,'') is null)::int
+ (nullif(t1.valeur_question_6,'') is null)::int
+ (nullif(t1.valeur_question_7,'') is null)::int
+ (nullif(t1.valeur_question_9,'') is null)::int
)
Another option is to unpivot the columns and do a select on them in a sub-select:
update reponse_question_finale
set nb_question_repondu = (select count(*)
from (
values
(valeur_question_4),
(valeur_question_6),
(valeur_question_7),
(valeur_question_9)
) as t(q)
where nullif(trim(q),'') is not null);
Adding more columns to be considered is quite easy then, as you just need to add a single line to the values() clause
I have a table with a varchar column, and I want to find values that match a certain number. So lets say that column contains the following entries (except with millions of rows in real life):
123456789012
2345678
3456
23 45
713?2
00123456789012
So I decide I want all the rows which are numerically 123456789012 write a statement that looks something like this:
SELECT * FROM MyTable WHERE CAST(MyColumn as bigint) = 123456789012
It should return the first and last row, but instead the whole query blows up because it can't convert the "23 45" and "713?2" to bigint.
Is there another way to do the conversion that will return NULL for values that can't convert?
SQL Server does NOT guarantee boolean operator short-circuit, see On SQL Server boolean operator short-circuit. So all solution using ISNUMERIC(...) AND CAST(...) are fundamentally flawed (they may work, but hey can arbitrarily fail later dependiong on the generated plan). A better solution is using CASE, as Thomas suggests: CASE ISNUMERIC(...) WHEN 1 THEN CAST(...) ELSE NULL END. But, as gbn pointed out, ISNUMERIC is notoriously finicky in identifying what 'numeric' means and many cases where one would expect it to return 0 it returns 1. So mixing the CASE with the LIKE:
CASE WHEN MyRow NOT LIKE '%[^0-9]%' THEN CAST(MyRow as bigint) ELSE NULL END
But the real problem is that if you have millions of rows and you have to search them like this, you'll always end up scanning end-to-end since the expression is not SARG-able (no matter how we rewrite it). The real issue here is data purity, and should be addressed at the appropriate level, where the data is populated. Another thing to consider is if is possible to create a persisted computed column with this expression and create a filtered index on it which eliminates NULL (ie. non-numeric). That would speed up things a little.
If you are using SQL Server 2012 you can use the 2 new methods:
TRY_CAST()
TRY_CONVERT()
Both methods are equivalent. They return a value cast to the specified data type if the cast succeeds; otherwise, returns null. The only difference is that CONVERT is SQL Server specific, CAST is ANSI. using CAST will make your code more portable (although not sure if any other database provider implements TRY_CAST)
ISNUMERIC will accept empty string and values like 1.23 or 5E-04 so could be unreliable.
And you don't know what order things will be evaluated in so it could still fail (SQL is declarative, not procedural, so the WHERE clause probably won't be evaluated left to right)
So:
you want to accept value that consist only of the characters 0-9
you need to materialise the "number" filter so it's applied before CAST
Something like:
SELECT
*
FROM
(
SELECT TOP 2000000000 *
FROM MyTable
WHERE MyColumn NOT LIKE '%[^0-9]%' --double negative rejects anything except 0-9
ORDER BY MyColumn
) foo
WHERE
CAST(MyColumn as bigint) = 123456789012 --applied after number check
Edit: quick example that fails.
CREATE TABLE #foo (bigintstring varchar(100))
INSERT #foo (bigintstring )VALUES ('1.23')
INSERT #foo (bigintstring )VALUES ('1 23')
INSERT #foo (bigintstring )VALUES ('123')
SELECT * FROM #foo
WHERE
ISNUMERIC(bigintstring) = 1
AND
CAST(bigintstring AS bigint) = 123
SELECT *
FROM MyTable
WHERE ISNUMERIC(MyRow) = 1
AND CAST(MyRow as float) = 123456789012
The ISNUMERIC() function should give you what you need.
SELECT * FROM MyTable
WHERE ISNUMERIC(MyRow) = 1
AND CAST(MyRow as bigint) = 123456789012
And to add a case statement like Thomas suggested:
SELECT * FROM MyTable
WHERE CASE(ISNUMERIC(MyRow)
WHEN 1 THEN CAST(MyRow as bigint)
ELSE NULL
END = 123456789012
http://msdn.microsoft.com/en-us/library/ms186272.aspx
SELECT *
FROM MyTable
WHERE (ISNUMERIC(MyColumn) = 1) AND (CAST(MyColumn as bigint) = 123456789012)
Additionally you can use a CASE statement in order to get null values.
SELECT
CASE
WHEN (ISNUMERIC(MyColumn) = 1) THEN CAST(MyColumn as bigint)
ELSE NULL
END AS 'MyColumnAsBigInt'
FROM tableName
If you require additional filtering, for numerics which are not valid to be cast to bigint, you can use the following instead of ISNUMERIC:
PATINDEX('%[^0-9]%',MyColumn)) = 0
If you need decimal values instead of integers, cast to float instead and change the regex to '%[^0-9.]%'