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    <p>Here is one quick thought, assuming it is crapping out fetching from the datastore. You could use <a href="http://code.google.com/appengine/docs/python/taskqueue/overview.html" rel="nofollow">tasks</a> and <a href="http://code.google.com/appengine/docs/python/datastore/queryclass.html#Query_cursor" rel="nofollow">cursors</a> to fetch the data in smaller chunks, then do the generation at the end.</p> <p>Start a task which does the initial query and fetches 300 (arbitrary number) records, then enqueues a named(!important) task that you pass the cursor to. That one in turn queries <em>[your arbitrary number]</em> records, and then passes the cursor to a new named task as well. Continue that until you have enough records.</p> <p>Within each task process the entities, then store the serialized result in a text or blob property on a 'processing' model. I would make the model's key_name the same as the task that created it. Keep in mind the serialized data will need to be under the API call size limit.</p> <p>To serialize your table pretty fast you could use:</p> <pre><code>serialized_data = "\x1e".join("\x1f".join(voter) for voter in data) </code></pre> <p>Have the last task (when you get enough records) kick of the PDf or CSV generation. If you use key_names for you models you, should be able to grab all of the entities with encoded data by key. Fetches by key are pretty fast, you'll know the model's keys since you know the last task name. Again, you'll want to be mindful size of your fetches from the datastore!</p> <p>To deserialize:</p> <pre><code>list(voter.split('\x1f') for voter in serialized_data.split('\x1e')) </code></pre> <p>Now run your PDF / CSV generation on the data. If splitting up the datastore fetches alone does not help you'll have to look into doing more of the processing in each task.</p> <p>Don't forget in the 'build' task you'll want to raise an exception if any of the interim models are not yet present. Your final task will automatically retry.</p>
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    1. POHow to generate large files (PDF and CSV) using AppEngine and Datastore?
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    1. USRobert Kluin
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    1. POHow to generate large files (PDF and CSV) using AppEngine and Datastore?
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    1. COWouldn't cycling through the results with a limit essentially be the same thing? I don't believe it's the data pull that's generating the timeout - although you could be correct. If I remember correctly, there is a 30 second timeout on the process, but a 10 second timeout on requests. 30 seconds should be more than enough time to actually process 1500-2000 records and output them in a PDF. This is timing out a lot quicker than that.
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    2. COMy suggestion here was meant to partially separate the data retrieval from the processing. On App Engine fetching 1,500 entities could *easily* consume several *seconds* of processing time. So yes, if you have not already tried I would definitely try running this with a limit of 10. Also, if you have not already, you should use Appstats (http://code.google.com/appengine/docs/python/tools/appstats.html) and try to determine exactly what is taking so much time.
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    3. COGreat idea - that just got released with the new version didn't it? Haven't tried it out yet, but I'm going to do it now.
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