# Worked-Out Example 1¶

## Processing text¶

Problem: Take a list of the British members of Parliament (MPs) in the last decade and characterise each by a couple of meaningful word from their wikipedia pages. Meaningful words are those that appear in the article for the the particular MP but not everywhere else.

(The complete code for this example and a list of MPs [valid in 2010] with the jug source)

The algorithm looks like this:

allcounts = []
for mp in MPs:
article = get_data(mp)
words = count_words(mp, article)
allcounts.append(words)

global_counts = add_counts(allcounts) # Here all processes must sync

for mp, mp_count in zip(MPs, counts):
meaningful = []
for w, c in mp_count:
if c > global_counts[w] // 100:
meaningful.append(w)
meaningful.sort(key=mp_count.get)
meaningful.reverse()
print(mp, meaningful[:8])


Very simple. It’s also embarassingly parallel, except for the line which computes global_counts, because it uses the results from everyone.

To use jug, we write the above, including the functions, to a file (in this case, the file is jugfile.py). Now, I can call jug status jugfile.py to see the state of the computation:

Task name                                    Waiting       Ready    Finished     Running
----------------------------------------------------------------------------------------
jugfile.get_data                                   0         657           0           0
jugfile.count_words                              657           0           0           0
jugfile.divergence                               657           0           0           0
........................................................................................
Total:                                          1315         657           0           0


Unsurprisingly, no task is finished and only the get_data task is ready to run. No nodes are running. So, let’s start a couple of processes [1]:

$jug execute jugfile.py &$ jug execute jugfile.py &
$jug execute jugfile.py &$ jug execute jugfile.py &
$sleep 4$ jug status jugfile.py
$sleep 48$ jug status jugfile.py


This prints out first:

Task name                                    Waiting       Ready    Finished     Running
----------------------------------------------------------------------------------------
jugfile.get_data                                   0         653           0           4
jugfile.count_words                              657           0           0           0
jugfile.divergence                               657           0           0           0
........................................................................................
Total:                                          1315         653           0           4

$sleep 48$ jug status jugfile.py

----------------------------------------------------------------------------------------
jugfile.get_data                                   0         635          20           2
jugfile.count_words                              637           2          16           2
jugfile.divergence                               657           0           0           0
........................................................................................
Total:                                          1295         637          36           4


So, we can see that almost immediately after the four background processes were started, 4 of them were working on the get_data task [2].

Forty-eight seconds later, some of the get_data calls are finished, which makes some count_words tasks be callable and some have been executed. The order in which tasks are executed is decided by jug itself.

At this point, we can add a couple more nodes to the process if we want for no other reason than to demonstrate this capability (maybe you have a dynamic clustering system and a whole lot more nodes have become available). The nodes will happily chug along until we get to the following situation:

Task name                                    Waiting       Ready    Finished     Running
----------------------------------------------------------------------------------------
jugfile.get_data                                   0           0         657           0
jugfile.count_words                                0           0         657           0
jugfile.divergence                               657           0           0           0
........................................................................................
Total:                                           657           0        1314           1


This is the bottleneck in the programme: Notice how there is only one node running, it is computing add_counts(). Everyone else is waiting (there are no ready tasks) [3]. Fortunately, once that node finishes, everyone else can get to work computing divergence:

Task name                                    Waiting       Ready    Finished     Running
----------------------------------------------------------------------------------------
jugfile.get_data                                   0           0         657           0
jugfile.count_words                                0           0         657           0
jugfile.divergence                                 0         653           0           4
........................................................................................
Total:                                             0         653        1315           4


Eventually, all the nodes finish and we are done. All the results are now left inside jugdata. To access it, we can write a little script:

import jug

jug.init('jugfile.py', 'jugdata')
import jugfile

for mp, r in zip(file('MPs.txt'), results):
mp = mp.strip()
print(mp, ":    ", " ".join(r[:8]))


The jug.init() call takes the jugfile (which does not need to be called jugfile.py) and the storage backend (at the simplest, just a directory path like here). Internally, jug.init imports the module, but we need to import it here too to make the names available (it is important that you use this interface. For example, running the jugfile directly on the interpreter might result in different task names and weirdness all around). jug.task.value looks up the value computed and then we can process the results into a nicer output format.

Besides serving to demonstrate, jug’s abilities, this is actually a very convenient format for organising computations:

1. Have a master jugfile.py that does all the computations that take a long time.
2. Have a secondary outputresult.py that loads the results and does the pretty printing. This should run fast and not do much computation.

The reason why it’s good to have the second step as a separate process is that you often want fast iteration on the output or even interactive use (if you are outputting a graph, for example; you want to be able to fiddle with the colours and axes and have immediate feedback). Otherwise, you could have had everything in the main jugfile.py, with a final function writing to an output file.

 [1] For this tutorial, all nodes are on the same machine. In real life, they could be on different computers as long as they can communicate with each other.
 [2] In order to make this a more realistic example, tasks all call the sleep() function to simulate long running processes. This example, without the sleep() calls, takes four seconds to run, so it wouldn’t be worth the effort to run multiple processors. Check jugfile.py for details.
 [3] There is a limit to how long the nodes will wait before giving up to avoid having one bad task keep every node in active-wait mode, which is very unfriendly if you are sharing a cluster. By default, the maximum wait time is set to roughly half an hour. You can set this with the --nr-wait-cycles (how many times jug will check for tasks) and --wait-cycle-time (the number of seconds to wait between each check).