more faithfully represent aws lambda python runtime context

[softprops]

What did you implement:

Closes #5283

Brought the python fake local invoke context up to greater parity with aws python runtime context based on the investigation in missing attributes and attribute value type mismatches mentioned in #5283 (comment)

How did you implement it:

I added two new property methods to invoke.py for the two attributes I was missing and corrected the value type of memory_limit_in_mb to match that of the aws lambda python runtime

How can we verify it:

You can essentially follow the same experiment here #5283 (comment)

you can take the off the shelf aws lambda python3.6 runtime template and change it to output the attrs and value types defined on the context object

def hello(event, context):
    return [(attr, str(type(getattr(context, attr)))) for attr in dir(context) if not attr.startswith('__')]

the compare the output of serverless invoke local -f hello vs serverless invoke -f hello ( after deploying it of course )

you should see that the value type of memory_limit_in_mb is now consistent and the attributes log_group_name and log_stream_name now exist in both contexts

Todos:

• Write tests
• Write documentation
• Fix linting errors
• Make sure code coverage hasn't dropped
• Provide verification config / commands / resources
• Enable "Allow edits from maintainers" for this PR
• Update the messages below

Is this ready for review?: YES
Is it a breaking change?: NO

[softprops]

@eahefnawy thoughts on this?

[softprops]

@horike37 / @RafalWilinski reaching out as you folks have ushered in the last few merges to this file. What are your thoughts on this change?

[RafalWilinski]

How about actual date? 😄

[RafalWilinski]

Left one small comment, other than that, LGTM 💃

[softprops]

@RafalWilinski, I pushed a change to address the comment. I used time.strftime since invoke.py was already using the time module

>>> from time import strftime
>>> strftime('%Y/%m/%d')
'2018/09/18'

Let me know what you think.

[horike37]

@softprops
LGTM 👍

[softprops]

thank you so much @horike37! what does the release cycle look like for batching up changes?

[horike37]

@softprops
You are welcome 😄 Thank you for this work well done 💯 Awesome!

what does the release cycle look like for batching up changes?

We don't decide a definite release cycle. But recent releases looks like every month.

[softprops]

awesome. I noticed you folks have been releasing a lot lately. I love it!