Hello,
I have noticed that today, my datastore writes were unusually very high.
It is about 15-20 time higher.
How can I resolve this and figure out what happened.
Thanks,
-Aswath
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This has been an issue since last November.
The only consistency I see on GAE/J is 15s+ user facing requests. This is a
absolute killer on interactive webpages.
It is trivial to identify which requests are "using facing" cold instance
starts in the logs. Google however, was un-inpressed.
So I por
I believe most who suffer from this problem understand the intended operation
of the scheduler as stated in the docs.
Some just imply that the design is sub-optimal.
A much louder complaint is the lack of consistent behavior, whether it is
understood or not.
David
On Thursday, March 7, 201
Thanks for all the replies. Yes, the reason I ask is because all the data I
aniticapte will be about 500mb big and was wondering to store it all in
memcache and non in the datastore to avoid front end instance hours, but it
seems as this is not a good approach since it is unreliable.
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You r
You should probably create a pipeline that first runs your mapreduce, then
runs a pipeline stage that only prepends a header to the header-less csv created
from the mapreduce.
On Tue, Mar 5, 2013 at 3:45 PM, Jonas Heyden wrote:
> I am fiddling around with GAE mapreduce and have one question:
>
>
Can you also please fix issue 5236 since files.blobstore.create() still
fails 1-2% of the time due to timeout (it hangs in google coded as
explained in issue)?
https://code.google.com/p/googleappengine/issues/detail?id=5236
On Tuesday, March 5, 2013 9:38:10 PM UTC-5, Ryan Huebsch wrote:
>
> We'v
On Thursday, March 7, 2013 2:38:39 PM UTC-6, Chad Vincent wrote:
>
> Or if you like, you could use Objectify which manages the Memcache as a
> Datastore cache automatically...
There are many data storage abstraction layers and frameworks available for
Java and Python on GAE, of which Objectify
A lot of this can be done in Python and since you need this in
functionality in Java you can set up a Python version or backend wrapped in
a RESTful API that uses xhtml2pdf and then consume that API from your Java
code.
For HTML/image to PDF I use the Python library http://www.xhtml2pdf.com/ wh
On Thursday, March 7, 2013 1:13:35 PM UTC-6, Vinny P wrote:
> The smartest thing to do is to simply put everything into memcache, and
> when you need the object, pull it out. If the pull fails, then query the
> datastore/cloud sql.
>
Or if you like, you could use Objectify which manages the Mem
Nobody knows except maybe Google, and I doubt even they know.
The reason is that memcache exists in the memory of the particular machine
your application is on (as opposed to the datastore/cloud sql, which can
exist on another machine or span multiple machines). This means that
memcache is blaz
Hello, I noticed in Google App engine the quota is 1mb value for menarche,
but does not specify a TOTAL limit of how much I can use. How much memcache
space do I have?
Thanks
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Be careful how you analyze the situation with your instances. When I was
playing with resident instances, I assumed that they represented a "floor"
you wouldn't drop below in times of low traffic, but that's not quite what
they do. Rather, they represent a pool of *deliberately idle* resources
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