on web development.
Notes taken during a presentation given by Rach Belaid at PyConUK
1620 on Friday
Look up immutable server concept
Docker for devs and administration
Packer https://www.packer.io/
Vagrant docker
Ansible http://www.ansible.com/
He shows various ways to deploy the docker app
Supervisord used in docker to run many processes
Baby steps approach to moving to docker. Normally one container per process
Isolation / sandbox
James
Google input
Even finance companies are considering it
Only a young project
What
Packaging
1 app = 1 container
What’s the diff
Uses less space, only store what is necessary
Start instantaneously
Use cases :
Development
Test prod architecture
Can combine with Jenkins CI CD
Can use base images
And then keep the docker configuration file clean
Is command line tool, but can use other tools to make simpler
Fig is a helpful tool for creating an infrastructure
Possible mention to http://deis.io/overview/ and Heroku
Almost no down time, if upgrading a component
Look at mounting volumes for running local dev code
Can use another app to orchestrate the dockers apps
Handy util when writing Python code that includes multiline strings: textwrap.dedent
Notes taken during a talk at PyConUK
#1 one code repo many deploys
#2 what about declaring your dependencies, e.g. gcc compiled items, deply containers
#3 read config from environment
debug flag, db connections etc
avoids secret leakage,
add defaults
make it language independent
populating env – foreman, supervisor (d?), ansible, bash -whatever etc
doismellburning – django app
www.doismellburning. co.uk .com?
NB: can be in another repo, just def outside of your app
#4 Loose coupling
#5 build, release (when config gets blended), run
build tip
…
fpm – builds an os package (ruby, don’t snigger too much – it’s awesome)
run stage needs to be a simple as possibly, do as much work in advance. e.g. fetch eggs in build, collect static etc
#6 stateless
assume nothing about RAM, persist data to external serices
#7 Port binding:
clean interface, should be able to tell your app a protocol and port and go
#8 Scale out not up, start more processes, don’t daemonise – let the env do it
#9 Stop quick;y, stop nicely
#10 Keep env similar: time, people, tool gap
Deploy more often
people
-> get people together, devops, …
tools
-> it works on my machine
#11 Treat logs as event streams
Run a log agreegator
one event, one line
=> q. how do you handle exceptions – let the agreegotr handle this, some differently code new lines.
#12 Run admin tasks in your app, eg clean up old sessions, as a django management command not a one off script on some dev machine
Language and framework agnostics
Notes taken during a Performance talk given at PyConUK.
function calls
c method instead
cython instead
Use operators
Loops
Avoid
list comprehension is quicker than for loop over an emnumerator
What is op?
xrange vs range
– no wasted memory
NB: In python3 renamed range
itertools
Or use a c extension
mstexttools
Sorting
l.sort vs sorted() – any iterator
Key based:
look up operator -> operator.itemgetter
Can use on object attribute
Strings:
“”.join([a, b, c]) faster but less readable
% method not that fast an ugly – don’t use
Dictionary
Int keys are faster than string keys
interned string keys – no garbage collection
sys.intern – P3
Variable look ups
Mentioned global variable trick
local var faster than global in general
Look up: Old style/new style classes??
Slot attributes ?? look up more just seemed to be attributes???
Don’t use exceptions as an often use case. => much faster say to use getattr than catch a valueexception
Try to rely on c methods
Profiling tools:
c profiler built into Python
cpython vs number – can make pythonrun as fast as C – suggestion