Bringing a new piece of internet-connected technology into your life requires an adequate amount of thought. And after a couple years of thinking about it, I finally decided to get an Apple Watch. Usually there’s no need to blog about a consumer electronics purchase, but I feel like the Apple Watch is different. Its capabilities and role are more intimate.

That is, I’m not getting one to tell me the time – I have a couple of great watches that work fine. No, I’m getting the Watch because it can help remind me to focus on me and the long term. If properly utilized it can aid me in my quest for a simpler, healthier life.

The promised sleep tracking, combined with the package of simpler features is what finally pushed me over the edge to purchase one. I’d love to be able to use the EKG feature, but it’s not enabled (yet) on Watches sold in Japan.

Sleep Tracking

Reading about the sleep tracking feature online, it seems that it’s less focused on the minutia and more focused on the big picture. Get you to bed on time as planned, so you get your hours in, and the rest will work itself out. Focus on the basics.

While I usually get 7-8 hours of sleep a night, with a toddler, my sleep or wake time are not entirely in my control. Having a record of my hours slept and then being able to corollate it over time to how I feel will be powerful.

Haptic Alarms

One part of my sleep schedule that is partially in control is my wake time. Ideally I’d like to get up around 4am consistently. This is easier in the summer in Japan because the sun rises around 4:30am and it’s hot by 6am. However, I’m unable to use an alarm that makes noise, as it’ll wake everyone else in the house up. Using an alarm that doesn’t make noise, but just vibrates so I can feel it is a game changer for controlling when I rise each day.

AirPods + GPS

Having a built in GPS and being able to sync music / podcasts to my watch will make it easier to go for a run in the morning. No more fiddling with my phone, wearing a bag to hold it or anything. Just grab my keys/id and go.

ApplePay

Full support for ApplePay on the Watch means when I’m out for a run, I can stop by 7-11 to get an iced coffee without fiddling with cash or my phone. It also means that even if I forget my phone (or choose to leave it at home), I still have my credit cards / transit cards, everything with me.

Even if I manage to wake consistently at 4am, I’ve still got to use that time efficiently. Waking up by my iPhone next to me is a quick way to lose my faith in humanity before getting out of bed each day.

I’ll end with a few things that I hope the Watch will encourage.

• Consistent wake and sleep times. I’m shooting for an 8pm ~ 4am schedule. When my son is gone, the 8pm bedtime goes out the window.


• Increase Water Consumption: I tend to slip on coffee for too long and not drink enough water. Simple reminders to nag me to drink my water.


• Increase movement during the day: I’m a programmer and when I get in the zone I don’t get up unless I have two. Fortunately my workplace lets me get in the zone pretty good almost everyday. Unfortunately, I’ll realize sat down after lunch and haven’t gotten up once until quitting time.


• Running: My eternal goal - become a regular runner. I was a regular runner for 3 months just after Leo was born. It may have been because I was sleep deprived and my brain couldn’t talk it’s way out of running. But it also may have been that I was always up at 4am anyways.

It arrives tomorrow and I am giddy.

I bought a new (shorter) domain for my new email address. One advantage of migrating away from gmail that I hadn’t anticipated is how much calmer I feel.

You see, Gmail technically supports IMAP, but it’s more of a shim. You’re not really supposed to use IMAP with Gmail. And as such I never felt comfortable using a regular email client, instead opting to check mail via the web-app.

Checking mail via a browser is fine but being in a browser switches your mind to a different context. Browsers are meant for consuming. The entire internet is just a simple cmd-T away. So “checking email” became a mental excuse to open my web browser. And then Twitter. And then Hacker News. And then Reddit. Oh, I wonder if I got any new email? And repeat.

Now with a provider where IMAP is a first class citizen, I can use Mail.app again. Mail is set to be pulled in once an hour. No more temptation from a web browser. And an unexpected sense of calm.

I’m back in control.

I’ve been using my gmail account since a few months after the beta started. I’ve moved a dozen times since then, but my email stayed the same.

However, over the years Google has lost my confidence that they’ll do the right thing and do no evil. It’s for this reason I don’t use their apps, don’t invest in tweaking gmail, or even (especially) sync my contacts.

As a Mac user for almost 20 years, I’d like to use iCloud for my email, but I can’t use custom domains with Apple. While I don’t foresee Apple losing my trust and confidence, I can’t be sure.

Tying my email to a third party domain will lock me in to their ecosystem, for better or worse. Moreover, I could lose it all in an instant by the whim of an algorithm with little to no recourse.

With Gmail, I’m not the customer, the advertisers are. And because our interests are not aligned, I have no idea how my data will actually be used.

What to do?

The obvious answer is to move my email to a domain I own. Then find a provider that supports open protocols and that I pay at a regular interval.

I’m leaning towards Fastmail. They’ve got a nice detailed migration guide, I’ve been a customer on the business side for a number of years, it’s time to renew, and most importantly their systems behave in ways that I expect.

The main blocker isn’t even money, it’s updating each account that uses my gmail as a login to my new address. Lock-in, albeit defacto and of my own doing, is a bitch.

The mantra in bootstrapping circles for the past while has been “charge more”. And the best way to charge more, over time, is a SaaS. So it’s natural that most bootstrapers default to a SaaS pricing model when starting their new projects and companies.

I’m no different. I build web-apps professionally and have for the past 10 years. Web apps are my bread and butter.

But when I compare my successful SaaS projects to my successful desktop app projects, no matter the metric, I’ve always made more when I charge less and charge it once.

And since I’ve been so focused on SaaS and this charge more mentality, I’ve automatically dismissed ideas that I had that weren’t SaaS.

After attempting to build a number of web apps independently I’ve mostly stopped midway through. The slog of getting the basics perfect, managing servers, dealing with recurring payments, it’s too much like my day-job.

And so I find myself considering going back to my old bread and butter for side-projects: native apps for the Macintosh.

So far I’ve got a few ideas for small utility apps. The ones I’m most interested in are the ones that fit in the open web and apps that can help increase privacy for its users.

It’s been a breath of fresh air and I’m excited to be having fun making things again.

Django has a nice security feature that verifies the request HOST header against the ALLOWED_HOSTS whitelist and will return errors if the requesting host is not in the list. Often you’ll see this when first setting up an app where you only expect requests to app.example.com but some bot makes a request to <server ip address>.

While it’s not strictly harmful to add your server ip to your ALLOWED_HOSTS, in theory, it does allow bots to easily reach and fire requests to your Django app, which will needlessly consume resources on your app server. It’s better to filter out the requests before they get to your app server.

For HTTP requests, you can block requests by adding default_server that acts as a catchall. Your app server proxy then set its server_name to the a domain in your ALLOWED_HOSTS. This simple configuration will prevent http://<server ip address> requests from ever reaching your app server.

// default.conf
server {
  listen 80 default_server;
  return 444;
}

// app.conf
upstream app_server {
server 127.0.0.1:8000 fail_timeout=0;
}

server {

listen 80; server_name {{ WEB_SERVER_NAME }};
access_log /var/log/nginx/access.log access_json;
error_log /var/log/nginx/error.log warn;

location /static/ {
alias /var/app/static/;
}

location / {
proxy_set_header Host $host;
proxy_set_header X-Real-IP $remote_addr;
proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
proxy_set_header X-Request-Id $request_id;
proxy_redirect off; proxy_pass http://app_server;
}
}

However, once you enable SSL with Let’s Encrypt, despite the fact that they matching by host, as there is only one SSL server configuration by default, it routes all https traffic to the same host. What this means is that while requests made to http://<server ip address> will continue to be blocked, requests to https://<server ip address> will begin to be forwarded to your django app server, resulting in errors. Yikes!

The solution is to add a default SSL enabled server, much like your http configuration. Thee only tricky bit is that all ssl configurations must have a valid ssl certificate configuration as well.  Rather than making a self-signed certificate I reused my let’s encrypt ssl configuration.

// default.conf
server {
  listen 80 default_server; return 444;
}

server {
listen 443 ssl default_server;
ssl_certificate /etc/letsencrypt/live/{{ WEB_SERVER_NAME }}/fullchain.pem;
ssl_certificate_key /etc/letsencrypt/live/{{ WEB_SERVER_NAME }}/privkey.pem;
include /etc/letsencrypt/options-ssl-nginx.conf;
ssl_dhparam /etc/letsencrypt/ssl-dhparams.pem;

if ($host != {{ WEB_SERVER_NAME }}) {
return 444;
}
}

By adding a default SSL server to your nginx config your server_name settings will be respected and requests that do not match your host name will no longer be forwarded to your app server.

Recently at work I’ve been working quite a bit with Django and GraphQL. There doesn’t seem to be much written about best practices for organizing your Graphene-Django projects, so I’ve decided to document what’s working for me. In this example I have 3 django apps: common, foo, and hoge.

There’s two main goals for this architecture:

1. Minimize importing from “outside” apps.


2. Keep testing simple.

Queries and Mutations Package

Anything beyond simple queries (i.e. a query that just returns all records of a given model) are implemented in their own file in the queries or mutations sub-package. Each file is as self-contained as possible and contains any type definitions specific to that query, forms for validation, and an object that can be imported by the app's schema.py.

Input Validation

All input validation is performed by a classic Django form instance. For ease of use django form input does not necessarily match the GraphQL input. Consider a mutation that sends a list of dictionaries with an object id.

{
  "foos": [
    {
        "id": 1,
        "name": "Bumble"
    },
    {
        "id": 2,
        "name": "Bee"
  ]
}

Before processing the request, you want to validate that the ids passed actually exist and or reference-able by the user making the request. Writing a django form field to handle input would be time consuming and potentially error prone. Instead each form has a class method called convert_graphql_input_to_form_input which takes the mutation input object and returns a dictionary that can be passed the form to clean and validate it.

from django import forms
from foo import models

class UpdateFooForm(forms.Form):
foos = forms.ModelMultipleChoiceField(queryset=models.Foo.objects)

@classmethod
def convert_graphql_input_to_form_input(cls, graphql_input: UpdateFooInput):
return { "foos": [foo["id"] for foo in graphql_input.foos]] }

Extra Processing

Extra processing before save is handled by the form in a prepare_data method. The role this method plays is to prepare any data prior to / without saving. Usually I'd prepare model instances, set values on existing instances and so forth. This allows the save() method to use bulk_create() and bulk_update() easily to keeps save doing just that - saving.

Objects/List of objects that are going to be saved / bulk_created / updated in save are stored on the form. The list is defined / set in init with full typehints. Example:

from typing import List, Optional

class UpdateFooForm(forms.Form):
foos = forms.ModelMultipleChoiceField(queryset=models.Foo.objects)

def __init__(*args, **kwargs)
super().__init__(*args, **kwargs)
self.foo_bars: List[FooBar] = []
self.bar: Optional[Bar] = None

Type Definition Graduation

Types are defined in each query / mutation where possible. As schema grows and multiple queries/mutations or other app's queries/mutations reference the same type, the location where the type is defined changes. This is partially for a cleaner architecture, but also to avoid import errors.

└── apps
├── common
│   ├── schema.py
│   └── types.py  # global types used by multiple apps are defined here
└── hoge
├── mutations
│   ├── create_hoge.py  # types only used by create_hoge are in here
│   └── update_hoge.py
├── queries
│   └── complex_query.py
├── schema.py
└── types.py  # types used by either create/update_hoge and or complex_query are defined here

Example Mutation

The logic kept inside a query/mutation is as minimal as possible. This is as it's difficult to test logic inside the mutation without writing a full-blown end-to-end test.

from graphene_django.types import ErrorType

class UpdateHogeReturnType(graphene.Union):
class Meta:
types = (HogeType, ErrorType)

class UpdateHogeMutationType(graphene.Mutation):

class Meta:
output = graphene.NonNull(UpdateHogeReturnType)

class Arguments:
update_hoge_input = UpdateHogeInputType()

@staticmethod
def mutate(root, info, update_hoge_input: UpdateHogeInputType) -> str:
data = UpdateHogeForm.convert_mutation_input_to_form_input(update_hoge_)
form = MutationValidationForm(data=data)
if form.is_valid():
form.prepare_data()
return form.save()
errors = ErrorType.from_errors(form)
return ErrorType(errors=errors)

Adding Queries/Mutations to your Schema

This architecture tries to consistently follow the graphene standard for defining schema. i.e. when defining your schema you create a class Query and class Mutation, then pass those to your schema schema = Schema(query=Query, mutation=Mutation)

Each app should build its Query and Mutation objects. These will then be imported in the schema.py, combined into a new Query class, and passed to schema.

# hoge/mutations/update_hoge.py

class UpdateHogeMutation:

update_hoge = UpdateHogeMutationType.Field()

# hoge/mutations/schema.py

from .mutations import update_hoge, create_hoge

class Mutation(update_hoge.Mutation,
create_hoge.Mutation):
pass

# common/schema.py

import graphene

import foo.schema
import hoge.schema

class Query(hoge.schema.Query, foo.schema.Query, graphene.GrapheneObjectType):
pass

class Mutation(hoge.schema.Mutation, foo.schema.Mutation, graphene.GrapheneObjectType):
pass

schema = graphene.Schema(query=Query, mutation=Mutation)

Directory Tree Overview

└── apps
├── common
│   ├── schema.py
│   └── types.py
├── foo
│   ├── mutations
│   │   └── create_or_update_foo.py
│   ├── queries
│   │   └── complex_foo_query.py
│   └── schema.py
└── hoge
├── mutations
│   ├── common.py
│   ├── create_hoge.py
│   └── update_hoge.py
├── queries
│   └── complex_query.py
├── schema.py
└── types.py

Watching videos like this one about UNIX system from 1982 is a great reminder that no matter what you're building today, we all stand on the shoulders of giants. Highly worth 20 minutes of your time.

https://youtu.be/XvDZLjaCJuw

A side project of mine is to archive the air pollution data for the state of Texas from the Texas Commission on Environmental Quality (TCEQ). My archiver then tweets out via the @Kuukihouston when thresholds of certain compounds go above certain thresholds that have been deemed by the EPA to be a health risk.

Recently I added support to automatically update the list of locations that it collects data from, rather than having a fixed list. Doing so is very straight forward: download the webpage, look for the <select> box that contains the sites, and scrape the value and text for each <option>.

There was only only a single hiccup during development of this feature: the developers don’t close their option tags and instead rely on web browsers “to do the right thing”.

That is their code looks like this:

        Oyster Creek [29]        Channelview [R]

When it should look like this:

        Oyster Creek [29]        Channelview [R]

Lucky web browsers excel in guessing and fixing incorrect html. But as I do not rely on a web browser to parse the html, I’m using BeautifulSoup. The BeaitfulSoup  html.parser closes the tags at the end of all of the options i.e. just before the </select> tag. What this does is when I try to get the text for the first option in the list, I get the text for the first option + every following option.

The simple fix is to switch from the html.parser parser to the lxml parser, which will close the open <option> tags at the beginning of the next <option> tag, allowing me to get the text for each individual item.

# Bad
soup = BeautifulSoup(response.text, ‘html.parser')
# Good 
soup = BeautifulSoup(response.text, 'lxml')

Getting started with GraphQL and Python most of the documentation is focused on the basics: basic queries, filtering using pre-built libraries and so forth. This is great for quick “Hello World” APIs, but there isn’t much discussion for best practices that discuss how to build out larger APIs, testing, or maintenance. Perhaps it’s just too early in the Python-GraphQL story for the best practices to have been fully established and documented.

Introductory GraphQL examples online don’t really require much testing of the resolver specifically. This is because these examples  just return the results of a Django Queryset directly. For those types of fields executing a full query is usually enough. But how to you handle more complex resolvers, ones that have some processing?

Accessing your resolver directly from a unit test is difficult and cumbersome. To properly test a resolver, you're going to need to  split the parts that warrant independent testing into their own functions / classes. Then once it's split, you can pass the required input for processing, and return the results.

However passing or returning Graphene objects to your functions will make testing them difficult in much of the same way that calling your resolver outside of a GraphQL request is difficult: you can't access the attribute values directly - they must be resolved.

blog = Blog(title="my title")
assert blog.title === "my title" # fail

Where Blog is a Graphene object, the above test will fail. As blog.title will not be a String as you'd think, but rather a graphene wrapper that will eventually return "my title" when passed through the GraphQL machine.

There's two ways to work around this:

1. Pass in `namedtuples`  that match attribute for attribute your Graphene objects in their place. This is will become a maintenance headache as each time your object changes, you'll need to also update your named tuples to match.


2. Pass/return primitive values into your functions and box them into GraphQL objects in your resolver directly before return.

I've done both in my code and think that the second method is a best practice when writing GraphQL apis.

By passing the values from graphene as primitives to your processing functions your code is no longer tied directly to graphene directly. You can change frameworks and re-use the same code.

It's also easier to write tests as you pass in common objects like dict and int which are quite easy to assert equality and simpler to reason about.

Takeaways:

1. Break your logic out of your resolver and into specialized functions / classes.


2. Pass/return primitive or other such values from these functions to maximize reuse.