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POAre mapreduce yield datastore mutation operations compatible with NDB models
 

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  1. POAre mapreduce yield datastore mutation operations compatible with NDB models
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    <p>In my Python AppEngine application, I have an ndb class which I am running a MapReduce job over in order to remove some old objects that are no longer needed. </p> <p>The class is as follows: </p> <pre><code>class UserModel(ndb.Model): is_backup_object = ndb.BooleanProperty(default=False) etc. </code></pre> <p>And the MapReduce job is as follows:</p> <pre><code>from mapreduce import operation as op # Note: userobject is an instantiation of UserModel def mapreduce_update_userobject(userobject): # This will remove "backup" userobjects from the database, while leaving # "normal" userobjects alone if userobject.is_backup_object: yield op.db.Delete(userobject) </code></pre> <p>When I run the MapReduce job, I have many <code>userobject</code>s that I want to delete (<code>is_backup_object = True</code>), and some of these objects are not being deleted even though the <code>is_backup_object value</code> is <code>True</code>. </p> <p>Questions:</p> <ol> <li>Are the mapreduce yield datastore mutation operations functions <code>operation.db.Put</code> and <code>operation.db.Delete</code> designed to work with NDB objects?</li> <li>Would the NDB automatic caching interfere with the removal of the yielded objects (or perhaps show outdated objects in the datastore viewer)?</li> <li>Is there a specific way that we should be yielding NDB objects that is different than standard database objects?</li> <li>Is there any other possible explanation for this strange behaviour that I have witnessed?</li> <li>If I am doing something incorrectly, then what is the best way to efficiently batch process NDB database entities with mapreduce? </li> </ol>
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    1. USAlexander Marquardt
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