Some thoughts on modules, how to structure them, and adding your own/extending HPI

This is slightly more advanced, and would be useful if you’re trying to extend HPI by developing your own modules, or contributing back to HPI

all.py

Some modules have lots of different sources for data. For example, my.location (location data) has lots of possible sources – from my.google.takeout.parser, using the gpslogger android app, or through geo locating my.ip addresses. For a module with multiple possible sources, its common to split it into files like:

my/location
├── all.py -- specifies all possible sources/combines/merges data
├── common.py  -- defines shared code, e.g. to merge data from across entries, a shared model (namedtuple/dataclass) or protocol
├── google_takeout.py  -- source for data using my.google.takeout.parser
├── gpslogger.py -- source for data using gpslogger
├── home.py -- fallback source
└── via_ip.py -- source using my.ip

Its common for each of those sources to have their own file, like my.location.google_takeout, my.location.gpslogger and my.location.via_ip, and then they all get merged into a single function in my.location.all, like:

from .common import Location

def locations() -> Iterator[Location]:
    # can add/comment out sources here to enable/disable them
    yield from _takeout_locations()
    yield from _gpslogger_locations()


@import_source(module_name="my.location.google_takeout")
def _takeout_locations() -> Iterator[Location]:
    from . import google_takeout
    yield from google_takeout.locations()


@import_source(module_name="my.location.gpslogger")
def _gpslogger_locations() -> Iterator[Location]:
    from . import gpslogger
    yield from gpslogger.locations()

If you want to disable a source, you have a few options.

If you’re using a local editable install or just want to quickly troubleshoot, you can just comment out the line in the locations function
Since these are decorated behind import_source, they automatically catch import/config errors, so instead of fatally erroring and crashing if you don’t have a module setup, it’ll warn you and continue to process the other sources. To get rid of the warnings, you can add the module you’re not planning on using to your core config, like:

class core:
    disabled_modules = (
        "my.location.gpslogger",
        "my.location.via_ip",
    )

… that suppresses the warning message and lets you use my.location.all without having to change any lines of code

Another benefit is that all the custom sources/data is localized to the all.py file, so a user can override the all.py (see the sections below on namespace packages) file in their own HPI repository, adding additional sources without having to maintain a fork and patching in changes as things eventually change. For a ‘real world’ example of that, see seanbreckenridges location and ip modules.

This is of course not required for personal or single file modules, its just the pattern that seems to have the least amount of friction for the user, while being extendable, and without using a bulky plugin system to let users add additional sources.

Another common way an all.py file is used is to merge data from a periodic export, and a GDPR export (e.g. see the stackexchange, or github modules)

module count

Having way too many modules could end up being an issue. For now, I’m basically happy to merge new modules - With the current module count, things don’t seem to break much, and most of them are modules I use myself, so they get tested with my own data.

For services I don’t use, I would prefer if they had tests/example data somewhere, else I can’t guarantee they’re still working…

Its great if when you start using HPI, you get a few modules ‘for free’ (perhaps github and reddit), but its likely not everyone uses the same services

This shouldn’t end up becoming a monorepo (a la Spacemacs) with hundreds of modules supporting every use case. Its hard to know what the common usecase is for everyone, and new services/companies which silo your data appear all the time…

Its also not obvious how people want to access their data. This problem is often mitigated by the output of HPI being python functions – one can always write a small script to take the output data from a module and wrangle it into some format you want

This is why HPI aims to be as extendable as possible. If you have some programming know-how, hopefully you’re able to create some basic modules for yourself - plug in your own data and gain the benefits of using the functions in my.core, the configuration layer and possibly libraries like cachew to ‘automatically’ cache your data

In some ways it may make sense to think of HPI as akin to emacs or a ones ‘dotfiles’. This provides a configuration layer and structure for you to access your data, and you can extend it to your own use case.

single file modules

… or, the question ‘should we split code from individual HPI files into setuptools packages’

It’s possible for a single HPI module or file to handle everything. Most of the python files in my/ are ‘single file’ modules

By everything, I mean:

Exporting data from an API/locating data on your disk/maybe saving data so you don’t lose it
Parsing data from some raw (JSON/SQLite/HTML) format
Merging different data sources into some common NamedTuple-like schema
caching expensive computation/merge results
configuration through my.config

For short modules which aren’t that complex, while developing your own personal modules, or while bootstrapping modules - this is actually fine.

From a users perspective, the ability to clone and install HPI as editable, add an new python file into my/, and it immediately be accessible as my.modulename is a pattern that should always be supported

However, as modules get more and more complex, especially if they include backing up/locating data from some location on your filesystem or interacting with a live API – ideally they should be split off into their own repositories. There are trade-offs to doing this, but they are typically worth it.

As an example of this, take a look at the my.github and the corresponding ghexport data exporter which saves github data.

Pros:
- This allows someone to install and use ghexport without having to setup HPI at all – its a standalone tool which means there’s less barrier to entry
- It being a separate repository means issues relating to exporting data and the DAL (loading the data) can be handled there, instead of in HPI
- This reduces complexity for someone looking at the my.github files trying to debug issues related to HPI. The functionality for ghexport can be tested independently of someone new to HPI trying to debug a configuration issue
- Is easier to combine additional data sources, like my.github.gdpr, which includes additional data from the GDPR export
Cons:
- Leads to some code duplication, as you can no longer use helper functions from my.core in the new repository
- Additional boilerplate - instructions, installation scripts, testing. It’s not required, but typically you want to leverage setuptools to allows pip install git+https... type installs, which are used in hpi module install
- Is difficult to convert to a namespace module/directory down the road

Not all HPI Modules are currently at that level of complexity – some are simple enough that one can understand the file by just reading it top to bottom. Some wouldn’t make sense to split off into separate modules for one reason or another.

A related concern is how to structure namespace packages to allow users to easily extend them, and how this conflicts with single file modules (Keep reading below for more information on namespace packages/extension) If a module is converted from a single file module to a namespace with multiple files, it seems this is a breaking change, see #89 for an example of this. The current workaround is to leave it a regular python package with an __init__.py for some amount of time and send a deprecation warning, and then eventually remove the __init__.py file to convert it into a namespace package. For an example, see the reddit init file.

Its quite a pain to have to convert a file from a single file module to a namespace module, so if there’s any possibility that you might convert it to a namespace package, might as well just start it off as one, to avoid the pain down the road. As an example, say you were creating something to parse zsh history. Instead of creating my/zsh.py, it would be better to create my/zsh/parser.py. That lets users override the file using editable/namespace packages, and it also means in the future its much more trivial to extend it to something like:

my/zsh
├── all.py -- e.g. combined/unique/sorted zsh history
├── aliases.py -- parse zsh alias files
├── common.py  -- shared models/merging code
├── compdump.py -- parse zsh compdump files
└── parser.py -- parse individual zsh history files

There’s no requirement to follow this entire structure when you start off, the entire module could live in my/zsh/parser.py, including all the merging/parsing/locating code. It just avoids the trouble in the future, and the only downside is having to type a bit more when importing from it.

Adding new modules

As always, if the changes you wish to make are small, or you just want to add a few modules, you can clone and edit an editable install of HPI. See SETUP for more information

The “proper way” (unless you want to contribute to the upstream) is to create a separate file hierarchy and add your module to PYTHONPATH (or use ‘editable namespace packages’ as described below, which also modifies your computed sys.path)

You can check my own personal overlay as a reference.

For example, if you want to add an awesomedatasource, it could be:

custom_module
└── my
    └──awesomedatasource.py

You can use all existing HPI modules in awesomedatasource.py, including my.config and everything from my.core. hpi modules or hpi doctor commands should also detect your extra modules.

In addition, you can override the builtin HPI modules too:
```
custom_lastfm_overlay
└── my
    └──lastfm.py
    
```
Now if you add custom_lastfm_overlay *in front* of ~PYTHONPATH~, all the downstream scripts using my.lastfm will load it from custom_lastfm_overlay instead.

This could be useful to monkey patch some behaviours, or dynamically add some extra data sources – anything that comes to your mind. You can check my.calendar.holidays in my personal overlay as a reference.

Namespace Packages

Note: this section covers some of the complexities and benefits with this being a namespace package and/or editable install, so it assumes some familiarity with python/imports

HPI is installed as a namespace package, which allows an additional way to add your own modules. For the details on namespace packages, see PEP420, or the packaging docs for a summary, but for our use case, a sufficient description might be: Namespace packages let you split a package across multiple directories on disk.

Without adding a bulky/boilerplate-y plugin framework to HPI, as that increases the barrier to entry, namespace packages offers an alternative with little downsides.

Creating a separate file hierarchy still allows you to keep up to date with any changes from this repository by running git pull on your local clone of HPI periodically (assuming you’ve installed it as an editable package (pip install -e .)), while creating your own modules, and possibly overwriting any files you wish to override/overlay.

In order to do that, like stated above, you could edit the PYTHONPATH variable, which in turn modifies your computed sys.path, which is how python determines the search path for modules. This is sort of what with_my allows you to do.

In the context of HPI, it being a namespace package means you can have a local clone of this repository, and your own ‘HPI’ modules in a separate folder, which then get combined into the my package.

As an example, say you were trying to override the my.lastfm file, to include some new feature. You could create a new file hierarchy like:

.
├── my
│   ├── lastfm.py
│   └── some_new_module.py
└── setup.py

Where lastfm.py is your version of my.lastfm, which you’ve copied from this repository and applied your changes to. The setup.py would be something like:

from setuptools import setup, find_namespace_packages

# should use a different name,
# so its possible to differentiate between HPI installs
setup(
    name=f"my-HPI-overlay",
    zip_safe=False,
    packages=find_namespace_packages(".", include=("my*")),
)

Then, running python3 -m pip install -e . in that directory would install that as part of the namespace package, and assuming (see below for possible issues) this appears on sys.path before the upstream repository, your lastfm.py file overrides the upstream. Adding more files, like my.some_new_module into that directory immediately updates the global my package – allowing you to quickly add new modules without having to re-install.

If you install both directories as editable packages (which has the benefit of any changes you making in either repository immediately updating the globally installed my package), there are some concerns with which editable install appears on your sys.path first. If you wanted your modules to override the upstream modules, yours would have to appear on the sys.path first (this is the same reason that custom_lastfm_overlay must be at the front of your PYTHONPATH). For more details and examples on dealing with editable namespace packages in the context of HPI, see the reorder_editable repository.

There is no limit to how many directories you could install into a single namespace package, which could be a possible way for people to install additional HPI modules, without worrying about the module count here becoming too large to manage.

There are some other users who have begun publishing their own modules as namespace packages, which you could potentially install and use, in addition to this repository, if any of those interest you. If you want to create your own you can use the template to get started.

Though, enabling this many modules may make hpi doctor look pretty busy. You can explicitly choose to enable/disable modules with a list of modules/regexes in your core config, see here for an example.

You may use the other modules or my overlay as reference, but python packaging is already a complicated issue, before adding complexities like namespace packages and editable installs on top of it… If you’re having trouble extending HPI in this fashion, you can open an issue here, preferably with a link to your code/repository and/or setup.py you’re trying to use.

An Extendable module structure

In this context, ‘overlay’/’override’ means you create your own namespace package/file structure like described above, and since your files are in front of the upstream repository files in the computed sys.path (either by using namespace modules, the PYTHONPATH or with_my), your file overrides the upstream repository

Related issues: #102, #89, #154

The main goals are:

low effort: ideally it should be a matter of a few lines of code to override something.
good interop: e.g. ability to keep with the upstream, use modules coming from separate repositories, etc.
ideally mypy friendly. This kind of means ‘not too dynamic and magical’, which is ultimately a good thing even if you don’t care about mypy.

all.py using modules/sources behind import_source is the solution we’ve arrived at in HPI, because it meets all of these goals:

it doesn’t require an additional plugin system, is just python imports and namespace packages
is generally mypy friendly (the only exception is the import_source decorator, but that typically returns nothing if the import failed)
doesn’t require you to maintain a fork of this repository, though you can maintain a separate HPI repository (so no patching/merge conflicts)
allows you to easily add/remove sources to the all.py module, either by:
- overriding an all.py in your own repository
- just commenting out the source/adding 2 lines to import and yield from your new source
- doing nothing! (import_source will catch the error and just warn you and continue to work without changing any code)

It could be argued that namespace packages and editable installs are a bit complex for a new user to get the hang of, and this is true. But fortunately import_source means any user just using HPI only needs to follow the instructions when a warning is printed, or peruse the docs here a bit – there’s no need to clone or create your own override to just use the all.py file.

There’s no requirement to use this for individual modules, it just seems to be the best solution we’ve arrived at so far

Logging guidelines

HPI doesn’t enforce any specific logging mechanism, you’re free to use whatever you prefer in your modules.

However there are some general guidelines for developing modules that can make them more pleasant to use.

each module should have its unique logger, the easiest way to ensure that is simply use module’s __name__ attribute as the logger name
In addition, this ensures the logger hierarchy reflect the package hierarchy. For instance, if you initialize the logger for my.module with specific settings, the logger for my.module.helper would inherit these settings. See more on that in python docs.

As a bonus, if you use the module __name__, this logger will be automatically be picked up and used by cachew.
often modules are processing multiple files, extracting data from each one (incremental/synthetic exports)
It’s nice to log each file name you’re processing as logger.info so the user of module gets a sense of progress. If possible, add the index of file you’re processing and the total count.
```
def process_all_data():
    paths = inputs()
    total = len(paths)
    width = len(str(total))
    for idx, path in enumerate(paths):
        # :>{width} to align the logs vertically
        logger.info(f'processing [{idx:>{width}}/{total:>{width}}] {path}')
        yield from process_path(path)
    
```
If there is a lot of logging happening related to a specific path, instead of adding path to each logging message manually, consider using LoggerAdapter.
log exceptions, but sparingly
Generally it’s a good practice to call logging.exception from the except clause, so it’s immediately visible where the errors are happening.

However, in HPI, instead of crashing on exceptions we often behave defensively and yield them instead (see mypy assisted error handling).

In this case logging every time may become a bit spammy, so use exception logging sparingly in this case. Typically it’s best to rely on the downstream data consumer to handle the exceptions properly.
instead of logging.getLogger, it’s best to use my.core.make_logger
```
from my.core import make_logger

logger = make_logger(__name__)

# or to set a custom level
logger = make_logger(__name__, level='warning')
    
```
This sets up some nicer defaults over standard logging module:
- colored logs (via colorlog library)
- INFO as the initial logging level (instead of default ERROR)
- logging full exception trace when even when logging outside of the exception handler
  This is particularly useful for mypy assisted error handling.
  
  By default, logging only logs the exception message (without the trace) in this case, which makes errors harder to debug.
- control logging level from the shell via LOGGING_LEVEL_* env variable
  This can be useful to suppress logging output if it’s too spammy, or showing more output for debugging.
  
  E.g. LOGGING_LEVEL_my_instagram_gdpr=DEBUG hpi query my.instagram.gdpr.messages
- experimental: passing env variable LOGGING_COLLAPSE=<loglevel> will “collapse” logging with the same level
  Instead of printing new logging line each time, it will ‘redraw’ the last logged line with a new logging message.
  
  This can be convenient if there are too many logs, you just need logging to get a sense of progress.
- experimental: passing env variable ENLIGHTEN_ENABLE=yes will display TUI progress bars in some cases
  See https://github.com/Rockhopper-Technologies/enlighten#readme
  
  This can be convenient for showing the progress of parallel processing of different files from HPI:
```
ghexport.dal[111]  29%|████████████████████                          |  29/100 [00:03<00:07, 10.03 files/s]
rexport.dal[comments]  17%|████████                                  | 115/682 [00:03<00:14, 39.15 files/s]
my.instagram.android   0%|▎                                          |    3/2631 [00:02<34:50, 1.26 files/s]
        
```

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TOC

all.py

module count

single file modules

Adding new modules

Namespace Packages

An Extendable module structure

Logging guidelines

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TOC

all.py

module count

single file modules

Adding new modules

Namespace Packages

An Extendable module structure

Logging guidelines