App Engine：检查数据存储中数据更新的最佳方式，同时避免数据存储写入

Question

I have a large amount of entities (products) in my datastore which come from a external data source. I want to check them for updates daily.

Some items are already updated because the application fetched them directly. Some are newly inserted and don´t need updates.

For ones which have not been fetched I have cron jobs running. I use the Python API.

At the moment I do the following.

I have a field

dateupdated = db.DateTimeProperty(auto_now_add=True)

I can then use

query = dbmodel.product.all()
query.filter('dateupdated <', newdate)
query.order('dateupdated')        
results = query.fetch(limit=mylimit, offset=myoffset)

to pick the oldest entries and schedule them for update. I used the Task Queue with custom task names to make sure each product update is only run once a day.

The problem is, that I need to update the field dateupdated, which means a datastore write, even if a product´s data was not changed, just to keep track of the update process.

This consumes lots of ressources (CPU hours, Datastore API calls, etc.).

Is there a better way to perform such a task and avoid the unnecessary datastore writes?

Answer 1

Yes, use cursors

By ordering a query by dateupdated and then storing a cursor after you have processed your entities, you can re-run the same query later to get only the items updated after your last query.

So, given a class like

class MyEntity(db.model):
    dateupdated = db.DateTimeProperty(auto_now_add=True)

You could setup a handler to be run as a task like:

class ProcessNewEntities(webapp.RequestHandler):
    def get(self):
        """Run via a task to process batches of 'batch_size' 
        recently updated entities"""
        # number of eneities to process per task execution
        batch_size = 100
        # build the basic query
        q = MyEntity.all().order("dateupdated")
        # use a cursor?
        cursor = self.request.get("cursor")
        if cursor:
            q.with_cursor(cursor)
        # fetch the batch
        entities = q.fetch(batch_size)
        for entity in entities:
            # process the entity
            do_your_processing(entity)
        # queue up the next task to process the next 100
        # if we have no more to process then delay this task 
        # for a while so that it doesn't hog the application
        delay = 600 if len(entities)<batch_size else 0
        taskqueue.add(
            url='/tasks/process_new_entities', 
            params={'cursor': q.cursor()},
            countdown=delay)

and then you just need to trigger the start of the task execution like:

def start_processing_entities():
    taskqueue.add(url='/tasks/process_new_entities')