Broken Levels Challenge – Egham Raspberry Pi Jam July 2017

Today at the Egham Raspberry Pi Jam we did two things:

1. The Broken Levels Challenge

Some nasty person came and broke our levels for our game Rabbit Escape and we need you to fix them!

To play this game you will need a PC version of Rabbit Escape, our Broken Levels, and the instruction sheets. Let us know how you get on!

2. Python Traffic Lights Programming Workshop

I ran a workshop to learn a bit of Python programming using this resource sheet Pi Stop Traffic Lights.

We had a lot of fun, and hopefully some people even learnt a little bit of coding.

Women Who Code workshop on “Write your own programming language”

On Wednesday 28th June 2017 a group of people from OpenMarket went to the Fora office space in Clerkenwell, London to run a workshop with the Women Who Code group, who work to help women achieve their career goals.

OpenMarket provided the workshop “Write your own programming language” and funded the food, and the venue was provided gratis by Fora.

We started the evening with some networking and food:

networking

food

but most of the time was spent coding:

coding

with lots of help from our OpenMarket helpers:

helpers

The feedback we got was very positive:

Everyone seemed to be having fun, so we hope we might get invited back to do more in future.

Why do this?

At OpenMarket we want to improve our diversity, and we have started by looking at gender diversity specifically. By being involved with events like this we hope to learn how we can make our company better at welcoming and supporting employees, encourage people from under-represented groups to apply to work here, and improve the general climate in our industry.

Thank you

A huge thank you to the OpenMarket people (from London and Guadalajara!) who helped out – I think people felt welcome and there was plenty of help available for the attendees – you did a great job.

Thank you also for the great response from everyone in our London office – several people in the office wanted to come but couldn’t make it on the night – I am hoping we will get more opportunities in future.

We’re also really grateful to OpenMarket for funding the food, to Fora for providing the space, and to Women Who Code for doing such great work to improve our industry.

Links

[Photos by David Lawson.]

Running a virtualenv with a custom-built Python

For my attempt to improve the asyncio.as_completed Python standard library function I needed to build a local copy of cpython (the Python interpreter).

To test it, I needed the aiohttp module, which is not part of the standard library, so the easiest way to get it was using virtualenv.

Here is the recipe I used to get a virtualenv and install packages using pip with a custom-built Python:

$ ~/code/public/cpython/python -m venv env
$ . env/bin/activate
(env) $ pip install aiohttp
(env) $ python mycode.py

Making 100 million requests with Python aiohttp

Series: asyncio basics, large numbers in parallel, parallel HTTP requests, adding to stdlib

I’ve been working on how to make a very large number of HTTP requests using Python’s asyncio and aiohttp.

Paweł Miech’s post Making 1 million requests with python-aiohttp taught me how to think about this, and got us a long way, with 1 million requests running in a reasonable time, but I need to go further.

Paweł’s approach limits the number of requests that are in progress, but it uses an unbounded amount of memory to hold the futures that it wants to execute.

We can avoid using unbounded memory by using the limited_as_completed function I outined in my previous post.

Setup

Server

We have a server program “server”:

(Note it differs from Paweł’s version because I am using an older version of aiohttp which has fewer convenient features.)

#!/usr/bin/env python3.5

from aiohttp import web
import asyncio
import random

async def handle(request):
    await asyncio.sleep(random.randint(0, 3))
    return web.Response(text="Hello, World!")

async def init():
    app = web.Application()
    app.router.add_route('GET', '/{name}', handle)
    return await loop.create_server(
        app.make_handler(), '127.0.0.1', 8080)

loop = asyncio.get_event_loop()
loop.run_until_complete(init())
loop.run_forever()

This just responds “Hello, World!” to every request it receives, but after an artificial delay of 0-3 seconds.

Synchronous client

As a baseline, we have a synchronous client “client-sync”:

#!/usr/bin/env python3.5

import requests
import sys

url = "http://localhost:8080/{}"
for i in range(int(sys.argv[1])):
    requests.get(url.format(i)).text

This waits for each request to complete before making the next one. Like the other clients below, it takes the number of requests to make as a command-line argument.

Async client using semaphores

Copied mostly verbatim from Making 1 million requests with python-aiohttp we have an async client “client-async-sem” that uses a semaphore to restrict the number of requests that are in progress at any time to 1000:

#!/usr/bin/env python3.5

from aiohttp import ClientSession
import asyncio
import sys

limit = 1000

async def fetch(url, session):
    async with session.get(url) as response:
        return await response.read()

async def bound_fetch(sem, url, session):
    # Getter function with semaphore.
    async with sem:
        await fetch(url, session)

async def run(session, r):
    url = "http://localhost:8080/{}"
    tasks = []
    # create instance of Semaphore
    sem = asyncio.Semaphore(limit)
    for i in range(r):
        # pass Semaphore and session to every GET request
        task = asyncio.ensure_future(bound_fetch(sem, url.format(i), session))
        tasks.append(task)
    responses = asyncio.gather(*tasks)
    await responses

loop = asyncio.get_event_loop()
with ClientSession() as session:
    loop.run_until_complete(asyncio.ensure_future(run(session, int(sys.argv[1]))))

Async client using limited_as_completed

The new client I am presenting here uses limited_as_completed from the previous post. This means it can make a generator that provides the futures to wait for as they are needed, instead of making them all at the beginning.

It is called “client-async-as-completed”:

#!/usr/bin/env python3.5

from aiohttp import ClientSession
import asyncio
from itertools import islice
import sys

def limited_as_completed(coros, limit):
    futures = [
        asyncio.ensure_future(c)
        for c in islice(coros, 0, limit)
    ]
    async def first_to_finish():
        while True:
            await asyncio.sleep(0)
            for f in futures:
                if f.done():
                    futures.remove(f)
                    try:
                        newf = next(coros)
                        futures.append(
                            asyncio.ensure_future(newf))
                    except StopIteration as e:
                        pass
                    return f.result()
    while len(futures) > 0:
        yield first_to_finish()

async def fetch(url, session):
    async with session.get(url) as response:
        return await response.read()

limit = 1000

async def print_when_done(tasks):
    for res in limited_as_completed(tasks, limit):
        await res

r = int(sys.argv[1])
url = "http://localhost:8080/{}"
loop = asyncio.get_event_loop()
with ClientSession() as session:
    coros = (fetch(url.format(i), session) for i in range(r))
    loop.run_until_complete(print_when_done(coros))
loop.close()

Again, this limits the number of requests to 1000.

Test setup

Finally, we have a test runner script called “timed”:

#!/usr/bin/env bash

./server &
sleep 1 # Wait for server to start

/usr/bin/time --format "Memory usage: %MKB\tTime: %e seconds" "$@"

# %e Elapsed real (wall clock) time used by the process, in seconds.
# %M Maximum resident set size of the process in Kilobytes.

kill %1

This runs each process, ensuring the server is restarted each time it runs, and prints out how long it took to run, and how much memory it used.

Results

When making only 10 requests, the async clients worked faster because they launched all the requests simultaneously and only had to wait for the longest one (3 seconds). The memory usage of all three clients was fine:

$ ./timed ./client-sync 10
Memory usage: 20548KB	Time: 15.16 seconds
$ ./timed ./client-async-sem 10
Memory usage: 24996KB	Time: 3.13 seconds
$ ./timed ./client-async-as-completed 10
Memory usage: 23176KB	Time: 3.13 seconds

When making 100 requests, the synchronous client was very slow, but all three clients worked eventually:

$ ./timed ./client-sync 100
Memory usage: 20528KB	Time: 156.63 seconds
$ ./timed ./client-async-sem 100
Memory usage: 24980KB	Time: 3.21 seconds
$ ./timed ./client-async-as-completed 100
Memory usage: 24904KB	Time: 3.21 seconds

At this point let’s agree that life is too short to wait for the synchronous client.

When making 10000 requests, both async clients worked quite quickly, and both had increased memory usage, but the semaphore-based one used almost twice as much memory as the limited_as_completed version:

$ ./timed ./client-async-sem 10000
Memory usage: 77912KB	Time: 18.10 seconds
$ ./timed ./client-async-as-completed 10000
Memory usage: 46780KB	Time: 17.86 seconds

For 1 million requests, the semaphore-based client took 25 minutes on my (32GB RAM) machine. It only used about 10% of my CPU, and it used a lot of memory (over 3GB):

$ ./timed ./client-async-sem 1000000
Memory usage: 3815076KB	Time: 1544.04 seconds

Note: Paweł’s version only took 9 minutes on his laptop and used all his CPU, so I wonder whether I have made a mistake somewhere, or whether my version of Python (3.5.2) is not as good as a later one.

The limited_as_completed version ran in a similar amount of time but used 100% of my CPU, and used a much smaller amount of memory (162MB):

$ ./timed ./client-async-as-completed 1000000
Memory usage: 162168KB	Time: 1505.75 seconds

Now let’s try 100 million requests. The semaphore-based version lasted 10 hours before it was killed by Linux’s OOM Killer, but it didn’t manage to make any requests in this time, because it creates all its futures before it starts making requests:

$ ./timed ./client-async-sem 100000000
Command terminated by signal 9

I left the limited_as_completed version over the weekend and it managed to succeed eventually:

$ ./timed ./client-async-as-completed 100000000
Memory usage: 294304KB	Time: 150213.15 seconds

So its memory usage was still very bounded, and it managed to do about 665 requests/second over an extended period, which is almost identical to the throughput of the previous cases.

Conclusion

Making a million requests is usually enough, but when we really need to do a lot of work while keeping our memory usage bounded, it looks like an approach like limited_as_completed is a good way to go. I also think it’s slightly easier to understand.

In the next post I describe my attempt to get something like this added to the Python standard library.

Python – printing UTC dates in ISO8601 format with time zone

By default, when you make a UTC date from a Unix timestamp in Python and print it in ISO format, it has no time zone:

$ python3
>>> from datetime import datetime
>>> datetime.utcfromtimestamp(1496998804).isoformat()
'2017-06-09T09:00:04'

Whenever you talk about a datetime, I think you should always include a time zone, so I find this problematic.

The solution is to mention the timezone explicitly when you create the datetime:

$ python3
>>> from datetime import datetime, timezone
>>> datetime.fromtimestamp(1496998804, tz=timezone.utc).isoformat()
'2017-06-09T09:00:04+00:00'

Note, including the timezone explicitly works the same way when creating a datetime in other ways:

$ python3
>>> from datetime import datetime, timezone
>>> datetime(2017, 6, 9).isoformat()
'2017-06-09T00:00:00'
>>> datetime(2017, 6, 9, tzinfo=timezone.utc).isoformat()
'2017-06-09T00:00:00+00:00'