google-app-engine-python

I'm experiencing occasional Exceeded soft private memory limit error in a wide variety of request handlers in app engine. I understand that this error means that the RAM used by the instance has exceeded the amount allocated, and how that causes the instance to shut down. I'd like to understand what might be the possible causes of the error, and to start, I'd like to understand how app engine python instances are expected to manage memory. My rudimentary assumptions were: An F2 instance starts with 256 MB When it starts up, it loads my application code - lets say 30 MB When it handles a

PROBLEM DESCRIPTION I am trying to create a custom managed VM for Google App Engine that behaves identically to the standard python27 managed VM provided by Google. (I'm doing this as a first step to adding a C++ library to the runtime). From google documentation , the following Dockerfile specifies the standard python27 runtime: FROM gcr.io/google_appengine/python-compat ADD . /app I have verified that this is the right Dockerfile by examining the one generated by gcloud preview app run when using the standard python27 runtime. It is identical to this. But when I run my application with this

I want to move an app from the Python 3.7 standard environment to the Python 3.6 flex environment. Python 3.7 comes with pip 18, Python 3.6 comes with pip 9. Pip 10 introduced a very useful feature, build dependencies. Many analytics packages need this to work, otherwise you have to install build dependencies like Cython manually in a separate step. That is obviously a non-starter in GAE. Outside of GAE the solution is pip install --upgrade pip . Is it possible to have the Python 3.6 environment update its pip before it runs pip install -r requirements.txt ? Adding pip to requirements.txt has

When I run a query on a large set of small objects (15k objects with only a few short string and boolean properties), without doing anything with these objects, I see my instance's memory usage continuously increasing (70Mb increase). The memory increase doesn't look proportional to the amount of data it ever needs to keep in memory for just the query. The loop I use is the following: cursor = None while True: query = MyModel.all() if cursor: query.with_cursor(cursor) fetched = 0 for result in query.run(batch_size = 500): fetched += 1 # Do something with 'result' here. Actually leaving it