Moving large SQL query to NumPy

I have a very large MySQL query in my web app that looks like this:

query = 
SELECT video_tag.video_id, (sum(user_rating.rating) * video.rating_norm) as score 

FROM video_tag 
JOIN user_rating ON user_rating.item_id = video_tag.tag_id
JOIN video ON video.id = video_tag.video_id 

WHERE item_type = 3 AND user_id = 1 AND rating != 0 AND video.website_id = 2 
AND rating_norm > 0 AND video_id NOT IN (1,2,3) GROUP BY video_id 
ORDER BY score DESC LIMIT 20"

This query joins three tables (video, video_tag, and user_rating), groups the results, and does some basic math to compute a score for each video. This takes about 2s to run as the tables are large.

Instead of making SQL do all this work, I suspect it would be faster to do this computation using NumPy arrays. The data in 'video' and 'video_tag' is constant - so I could just load those table into memory once and not have to ping SQL each time.

However, while I can load these three tables into three separate arrays, I'm having a heck of a time replicating the above query (sp开发者_JAVA技巧ecifically the JOIN and GROUP BY parts). Has anyone any experience with replicating SQL queries using NumPy arrays?

Thanks!

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What makes this exercise awkward is the single-data-type constraint for NumPy arrays. For instance, the GROUP BY operation implicitly requires (at least) one field/column of continuous values (to aggregate/sum) and one field/column to partition or group by.

Of course, NumPy recarrays can represent a 2D array (or SQL Table) using a different data type for each column (aka 'Field'), but I find these composite arrays cumbersome to work with. So in the code snippets below, i just used the conventional ndarray class to replicate the two SQL operations highlighted in the OP's Question.

to mimic SQL JOIN in NumPy:

first, create two NumPy arrays (A & B) each to represent an SQL Table. The primary keys for A are in 1st column; foreign key for B also in 1st column.

import numpy as NP
A = NP.random.randint(10, 100, 40).reshape(8, 5)
a = NP.random.randint(1, 3, 8).reshape(8, -1)    # add column of primary keys      
A = NP.column_stack((a, A))

B = NP.random.randint(0, 10, 4).reshape(2, 2)
b = NP.array([1, 2])
B = NP.column_stack((b, B))

Now (attempt to) replicate JOIN using NumPy array objects:

# prepare the array that will hold the 'result set':
AB = NP.column_stack((A, NP.zeros((A.shape[0], B.shape[1]-1))))

def join(A, B) :
    '''
    returns None, side effect is population of 'results set' NumPy array, 'AB';
    pass in A, B, two NumPy 2D arrays, representing the two SQL Tables to join
    '''
    k, v = B[:,0], B[:,1:]
    dx = dict(zip(k, v))
    for i in range(A.shape[0]) :
        AB[i:,-2:] = dx[A[i,0]]

to mimic SQL GROUP BY in NumPy:

def group_by(AB, col_id) :
    '''
    returns 2D NumPy array aggregated on the unique values in column specified by col_id;
    pass in a 2D NumPy array and the col_id (integer) which holds the unique values to group by
    '''
    uv = NP.unique(AB[:,col_id]) 
    temp = []
    for v in uv :
        ndx = AB[:,0] == v          
        temp.append(NP.sum(AB[:,1:][ndx,], axis=0))
    temp = NP. row_stack(temp)
    uv = uv.reshape(-1, 1)
    return NP.column_stack((uv, temp))

for a test case, they return the correct result:

>>> A
  array([[ 1, 92, 50, 67, 51, 75],
         [ 2, 64, 35, 38, 69, 11],
         [ 1, 83, 62, 73, 24, 55],
         [ 2, 54, 71, 38, 15, 73],
         [ 2, 39, 28, 49, 47, 28],
         [ 1, 68, 52, 28, 46, 69],
         [ 2, 82, 98, 24, 97, 98],
         [ 1, 98, 37, 32, 53, 29]])

>>> B
  array([[1, 5, 4],
         [2, 3, 7]])

>>> join(A, B)
  array([[  1.,  92.,  50.,  67.,  51.,  75.,   5.,   4.],
         [  2.,  64.,  35.,  38.,  69.,  11.,   3.,   7.],
         [  1.,  83.,  62.,  73.,  24.,  55.,   5.,   4.],
         [  2.,  54.,  71.,  38.,  15.,  73.,   3.,   7.],
         [  2.,  39.,  28.,  49.,  47.,  28.,   3.,   7.],
         [  1.,  68.,  52.,  28.,  46.,  69.,   5.,   4.],
         [  2.,  82.,  98.,  24.,  97.,  98.,   3.,   7.],
         [  1.,  98.,  37.,  32.,  53.,  29.,   5.,   4.]])

>>> group_by(AB, 0)
  array([[   1.,  341.,  201.,  200.,  174.,  228.,   20.,   16.],
         [   2.,  239.,  232.,  149.,  228.,  210.,   12.,   28.]])