rank

I have the following table structure for a table Player Table Player { Long playerID; Long points; Long rank; } Assuming that the playerID and the points have valid values, can I update the rank for all the players based on the number of points in a single query? If two people have the same number of points, they should tie for the rank. UPDATE: I'm using hibernate using the query suggested as a native query. Hibernate does not like using variables, especially the ':'. Does anyone know of any workarounds? Either by not using variables or working around hibernate's limitation in this case by

I have following table in hive user-id, user-name, user-address,clicks,impressions,page-id,page-name I need to find out top 5 users[user-id,user-name,user-address] by clicks for each page [page-id,page-name] I understand that we need to first group by [page-id,page-name] and within each group I want to orderby [clicks,impressions] desc and then emit only top 5 users[user-id, user-name, user-address] for each page but I am finding it difficult to construct the query. How can we do this using HIve UDF ? You can do it with a rank() UDF described here: http://ragrawal.wordpress.com/2011/11/18

How does one handle ties when ranking results in a mysql query? I've simplified the table names and columns in this example, but it should illustrate my problem: SET @rank=0; SELECT student_names.students, @rank := @rank +1 AS rank, scores.grades FROM student_names LEFT JOIN scores ON student_names.students = scores.students ORDER BY scores.grades DESC So imagine the the above query produces: Students Rank Grades ======================= Al 1 90 Amy 2 90 George 3 78 Bob 4 73 Mary 5 NULL William 6 NULL Even though Al and Amy have the same grade, one is ranked higher than the other. Amy got

Given the following data frame: import pandas as pd import numpy as np df=pd.DataFrame({'A':['A','A','A','B','B','B'], 'B':['a','a','b','a','a','a'], }) df A B 0 A a 1 A a 2 A b 3 B a 4 B a 5 B a I'd like to create column 'C', which numbers the rows within each group in columns A and B like this: A B C 0 A a 1 1 A a 2 2 A b 1 3 B a 1 4 B a 2 5 B a 3 I've tried this so far: df['C']=df.groupby(['A','B'])['B'].transform('rank') ...but no dice! Thanks in advance! Use groupby/cumcount : In [25]: df['C'] = df.groupby(['A','B']).cumcount()+1; df Out[25]: A B C 0 A a 1 1 A a 2 2 A b 1 3 B a 1 4 B a 2

I have detected that a range of IP addresses may be used in a malicious way and I don't know how to block it. I would like to block the range 66.249.74.* from accessing my website by using the .htaccess file. You could use: Order Allow,Deny Deny from 66.249.74.0/24 Allow from all Or you could use this: RewriteEngine on RewriteCond %{REMOTE_ADDR} ^66\.249\.74\. RewriteRule ^ - [F] Use just the first 3 octets Order Allow,Deny Deny from 66.249.74. Allow from all Vladyn I’ve just used Order Allow,Deny Deny from 188.143.*.* Allow from all as spam attack comes from xxx.xxx.0-80.0-80 .