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How to select rows that have current day's timestamp?
...table` WHERE DATE(`timestamp`) = CURDATE()
I guess using DATE still uses INDEX.
see the execution plan on the DEMO
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Apache and Node.js on the Same Server
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Great question!
There are many websites and free web apps implemented in PHP that run on Apache, lots of people use it so you can mash up something pretty easy and besides, its a no-brainer way of serving static content. Node is fast, powerful, elegant, and a sexy tool with the raw power of V8 and...
How to set iPhone UIView z index?
I want to move one view on top of another, how can I know the z index of the view, and how to move on to top?
9 Answers
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How can you determine how much disk space a particular MySQL table is taking up?
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For a table mydb.mytable run this for:
BYTES
SELECT (data_length+index_length) tablesize
FROM information_schema.tables
WHERE table_schema='mydb' and table_name='mytable';
KILOBYTES
SELECT (data_length+index_length)/power(1024,1) tablesize_kb
FROM information_schema.tables
WHERE table_s...
WSGI vs uWSGi with Nginx [closed]
...sees the two terms and has no prior info on how things work outside of mod_PHP world (for e.g. nothing against php or folks)
The site does well to explain in practical terms what is needed and what is the difference as well as a good deployment example for nginx.
For the convenience, the explanatio...
B-Tree vs Hash Table
In MySQL, an index type is a b-tree, and access an element in a b-tree is in logarithmic amortized time O(log(n)) .
5 Answ...
Python + Django page redirect
...direct (e.g. cflocation in ColdFusion, or header(location:http://) for PHP) in Django?
10 Answers
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Creating a Pandas DataFrame from a Numpy array: How do I specify the index column and column headers
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You need to specify data, index and columns to DataFrame constructor, as in:
>>> pd.DataFrame(data=data[1:,1:], # values
... index=data[1:,0], # 1st column as index
... columns=data[0,1:]) # 1st row as the co...
When and why are database joins expensive?
... working set. Joins involving properly selected keys with correctly set up indexes are cheap, not expensive, because they allow significant pruning of the result before the rows are materialised.
Materialising the result involves bulk disk reads which are the most expensive aspect of the exercise ...
Random row selection in Pandas dataframe
...like this?
import random
def some(x, n):
return x.ix[random.sample(x.index, n)]
Note: As of Pandas v0.20.0, ix has been deprecated in favour of loc for label based indexing.
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