8K monthly Worlds Top 100 ID Giants at #11 by Interior Design Magazine Attractive remuneration package Excellent career advancement WELL SMART INTERNATIONAL INVESTMENT PTE LTD Downtown Core A WELLNESS HOLDINGS PTE. LTD. Bukit Batok SGD 3K - 4. 5K mo...
Secretary Female
Extremely good in communication
Pleasant person with good interpersonal skills
Should have 3 to 7 experience in Admin/Secretary
Any graduate
Only female candidates.
Interested Candidates can call Sumathi @ 8056036143 or mail to sumathi@skillmine. Administrative Officer,
Secretary
counting fundamentals
working knowledge
3.
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Hi, I have two tables that have the exact same configuration outside of a few different columns (although all data types and lengths are shared). I have some data I am merging, upon user upload and one of the tables is taking EXTREMELY LONG while the other is very fast. I cannot for the life of me see what the difference here could possibly be, so I'm hoping a different set of eyes will be able to point it out. Note that both data sets are the same length, I've even tried making the fast one 10x longer, and it still is somehow faster. It possibly has something to do with the number of columns, but there is only one additional column.. I didn't see anything in the pyodbc source that suggests a different method of attack for 10 columns vs 9 or 20 variables vs 18. query args is a list of lists, I have made both roughly the same size for the purposes of testing. # this one is extremely fast. ~ 11 seconds for 10k rows with nnect(nnection_string) as conn: cursor = () statement = """ UPDATE [GLAPPS].