Migrating a small application to a highly replicated data warehouse

Question

The current recommendation from Google is that all applications begin to migrate to the highly replicated data warehouse. My application is small and still under development. My understanding is that the more data my application loads, the larger the transition process will be. Therefore, I decided to migrate ASAP (so far I have only 56 thousand user data).

I followed the documentation for loading all data from the master-slave application. I have uploaded data. I have deployed my high replication application. When I try to use the upload command (from the same section in the documentation), I get an error. Here is what I am trying to run:

C:\Users\Hank\Documents\Aptana Studio 3 Workspace\hanksandbox>appcfg.py upload_data --application=essayhost --kind=User --filename=sandboxed . 

and the error I get:

  File "C:\Program Files (x86)\Google\google_appengine\google\appengine\datastore\datastore_rpc.py", line 1048, in check_rpc_success raise _ToDatastoreError(err) google.appengine.api.datastore_errors.BadRequestError: app s~essayhost cannot access app essayhost data 

This is the last line of a very long trace. If you need more, let me know. If you know a good, thorough passage through this process, please combine it. Most of what I find is a bit out of depth.

PS Does anyone know how to download all kinds at once?

Answer 1

I believe the fix is ​​to specify the full application identifier for your highly replicated application in the -application flag. In your case, the full application identifier is: s ~ essayhost. App Engine tries to hide the prefix s ~, but it does not always work.

Answer 2

I encountered the same problem as you. Here is how I fixed it:

 appcfg.py upload_data --config-file=bulkloader.yaml --filename=links.csv --kind=Link --application=s~myapp --url=http://myapp.appspot.com/_ah/remote_api 

Currently, I am transferring my data from my VPS to Google App Engine, 60 thousand rows of data.

Edit: you cannot transfer all species at once; you can try to make a batch script package or do something for you, but it's not that hard to print everything.