I need to compare a huge list of data which has two rows of dates as part of the whole data set. When I use the 'exact' formula, one looks like a date in the formula and the other a number and so they come up as not exact.
The response was helpful, though the data had been stored as dates. So I tried the suggestion , which did not help, but then tried =IFERROR(DATEVALUE,F2=E2) which worked for some of the data but not all,so still somewhat confused. It may be that the source data was provided to me in a strange way, so I'll need to follow up with the 'provder' of the initial data dump.
The date in F2 is stored as text. You can do a comparison like this:
=DATEVALUE(F2)=E2
Related
I am trying to insert data from a staging table into the master table. The table has nearly 300 columns, and is a mix of data-typed Varchars, Integers, Decimals, Dates, etc.
Snowflake gives the unhelpful error message of "Numeric value '' is not recognized"
I have gone through and cut out various parts of the query to try and isolate where it is coming from. After several hours and cutting every column, it is still happening.
Does anyone know of a Snowflake diagnostic query (like Redshift has) which can tell me a specific column where the issue is occurring?
Unfortunately not at the point you're at. If you went back to the COPY INTO that loaded the data, you'd be able to use VALIDATE() function to get better information to the record and byte-offset level.
I would query your staging table for just the numeric fields and look for blanks, or you can wrap all of your fields destined for numeric fields with try_to_number() functions. A bit tedious, but might not be too bad if you don't have a lot of numbers.
https://docs.snowflake.com/en/sql-reference/functions/try_to_decimal.html
As a note, when you stage, you should try and use the NULL_IF options to get rid of bad characters and/or try to load them into stage using the actual datatypes in your stage table, so you can leverage the VALIDATE() function to make sure the data types are correct before loading into Snowflake.
Query your staging using try_to_number() and/or try_to_decimal() for number and decimal fields of the table and the use the minus to get the difference
Select $1,$2,...$300 from #stage
minus
Select $1,try_to_number($2)...$300 from#stage
If any number field has a string that cannot be converted then it will be null and then minus should return those rows which have a problem..Once you get the rows then try to analyze the columns in the result set for errors.
I have a two tables in a database in AWS Athena that I want to join.
I want to join them by several columns, one of them being date.
However in one data set the date string is encoded for single value months is encoded as
"08/31/2018"
While the other would have it encoded as
"8/31/2018"
Is there a way to make them the same format?
I am unsure if it is easier to add the extra 0 to strings which have lack the extra 0 or to concatenate strings which have the extra 0.
Based on what I have researched I think I will have to use the CASE and CONCAT functions.
Both of the tables were loaded into the database from a CSV file, and the variables are in the string format.
I have tried changing the values manually in the CSV file, tried running an R script on one of the tables to format the date in the same way, and have also tried re-loading the tables into the database as the same date format.
However no matter what I do whenever it is loaded into the database, even when they have the same date type, it always loads them with different formats.
One with the the extra 0 and the other without it.
The last avenue I haven't tried is through a SQL query.
However I am not well versed in Athena and am having a hard time formatting this query.
I know this is rather vague, so please ask me for more information if you need.
If someone could help me start this query I would be grateful.
Thank you for the help.
Here is the query for changing dates in Athena.
date_parse(table.date_variable,'%m/%d/%Y')
Though Athena tables are immutable once created.
You can convert the value to date using date_parse(). So, this should work:
date_parse(t1.datecol, '%m/%d/%Y') = str_to_date(t2.datecol, '%m/%d/%Y')
Having said that, you should fix the data model. Store dates as dates not as strings! Then you can use an equality join and that is just better all around.
I would like to change the manner in which the mileage is represented in the database. For example, right now the mileage is represented as 080+0.348; this would mean that this particular feature is at mileage point 80.348 along the roadway corridor. I would like to have the data represented in the database in the latter form, 80.348 and so on. This would save me from having to export the dataset to excel for the conversion. Is this even possible? The name of the column is NRLG_MILEPOINT.
Much appreciated.
One thing you could try is to pick the string value apart into its component pieces and then recombine them as a number. If your data is in a table called TEST you might do something like the following:
select miles, fraction,
nvl(to_number(miles), 0) + nvl(to_number(fraction), 0) as milepoint
from (select regexp_substr(nrlg_milepoint, '[0-9]*') as miles,
regexp_substr(nrlg_milepoint, '[+-][0-9.]*') as fraction
from test);
SQLFiddle here.
Share and enjoy.
Using the answer provided above, I was able to expand it to get exactly the answer i needed. Thanks a ton to everyone who helped! Here is the query i ended up with:
select distinct nrlg_dept_route,corridor_code_rb,nrlg_county,next_county,
nvl(to_number(miles), 0) + nvl(to_number(fraction), 0) as milepoint
from (select regexp_substr(nrlg_milepoint, '[0-9]*') as miles,
nrlg_milepoint as nrlg_mile_point
nrlg_dept_route as nrlg_dept_route,
nrlg_county as nrlg_county,
next_county as next_county,
corridor_code_rb as corridor_code_rb,
corridor_code as corridor_code,
regexp_substr(nrlg_milepoint, '[+-][0-9.]*') as fraction
from corridor_county_intersect,south_van_data_view)
where nrlg_dept_route = corridor_code
order by 1,5;
There are a variety of ways to do this. Which one depends on your situation, how the data needs to be stored, and how it is being interacted with. Some of these options include:
Changing the datatype.
This option would potentially require you to change how the data is being stored currently. The conversion of the data would have to be done by whatever is writing the data to the schema currently.
Creating another column that stores the data in the correct format.
If you have an existing means of storing the data that would be broken by changing the datatype of NRLG_MILEPOINT and/or you have a requirement to store the data in that format; you could optionally add another column... say NRLG_MILEAGE_DISPLAY that is of a datatype number perhaps, and store the data there. You could make a trigger that updates/inserts NRLG_MILEAGE_DISPLAY appropriately, based on the data in NRLG_MILEPOINT.
If you are just wanting the data to be displayed differently in your select statement, you can convert the datatype in your SQL statement. Specifically how you would do this depends on the current datatype of NRLG_MILEPOINT.
Assuming that varchar2 is the type, based on the comments, here is an SQLFIDDLE link displaying a crude example of option 3. Your usage of this may vary depending on the actual datatype of NRLG_MILEPOINT. Regardless of its datatype... I am sure there is a means of converting how it is displayed in your query. You could take this further and create a view if you needed to. As an inline view or as a stored view, you can then use the converted value for doing your join later.
I am from non database background. I have created a table with one of the field data type TEXT.
dataF TEXT
INSERTION:
Have inserted three records with the values :
'842-2-4'
'842-2-5'
'842-2-6'
SELECTION:
Tring to get the records based on date now.
... where dateF between '842-2-4' and '842-2-10'
It fails.
Whereas,
... where dateF between '842-2-4' and '842-2-8'
retrieves all the 3 records.
What am i doing wrong ? Why the first statment fails ?
Kindly suggest.
Because you are comparing strings not dates. The computer has no idea these are dates. You have to either store as date and do a date comparison or implement your own logic to analyze strings.
Simply put, it is looking at the 1 in 10 as being less than your values rather than 10 being more. It's string comparison, not date.
Although sqlite doesn't support date types, it does have functions for dealing with them. See here:
http://www.sqlite.org/lang_datefunc.html
When comparing strings, the values are compared left to right...
As one string is shorter that the other, you are kind of comparing this...
'842-2-4'
'842-2-1'
Well, nothing is >= '842-2-4' AND <= '842-2-1'.
Because '842-2-1' comes before '842-2-4'.
And, so, '842-2-10' comes before '842-2-4' too.
Just as 'Squiggled' comes before 'Squiggly'
And as 'xxx-y-az' comes before 'xxx-y-z'
To compare as you desire, make sure all your dates are padded with 0's.
BETWEEN '0842-02-04' AND '0842-02-10'
But that will only work after you have padded out the values in your table too.
EDIT:
Also, note that this assumes that your format is YYYY-MM-DD. As a string is compared left to right, you must have the highest magnitude values to the left.
(This means that you can't use YYYY-DD-MM and still have native string comparisons behave as you would want them.)
There is a problem in VBA text box while filling input mask property:
I am trying to make the combination of date and time:
Hence i put it like below:
00/00/00;0;_00:00;0;_
But while running the application, i am only getting 00/00/00 (Date).
But i remember, i got the result as like 00/00/00 00.00 as expected when i first put the expression as like above;
but now i am not getting it :-(
The InputMask property can contain up to three sections separated by semicolons (;)
Your mask should be like this:
"00/00/00 00:00;0;0"
or
"00/00/00 00:00;0;_" // to display it like __/__/__ __:__
Why not just use the built in "General Date" format? I've found over the years that input masks are very restricting and basically a pain. Although it's been so long since I've used them that I don't recall the details of why I despise them.
This also has the benefit of respecting the users choices of regional date format. For example I always use yyyy-mm-dd format.
Also a client had a situation where the date format was decreed to be Medium Date on all fields. Which is dd-mmm-yy. It later turned out that in a table of 100K records there were twelve dates before 1900. They had simple had something extra keyed in in the year so Windows/Access interpreted those dates as being in the 3rd or 5th century or whatever. Now these dates weren't used in any kind of calculation so it wasn't a big deal. SQL Server upsizing to small date/time fields didn't appreciate those though.