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Quick Start

Say you are creating an ETL project that analyzes some tweets in the directory /tweet_research. First you should create a .paths.json file. (If you are using the reference implementation, execute pathsjson --init from your shell.) Next, imagine you filled in the file as,

{
    "__ENV": {
        "VERSION": "0.0.1"
    },
    "data_dir": ["$$_IMPLICIT_ROOT", "data"],
    "raw_dir": ["$data_dir", "raw"],
    "log_dir": ["$data_dir", "logs"],
    "clean_dir": ["$data_dir", "clean"],
    
    "twitter_errors": ["$log_dir", "$$VERSION", "twitter.log"],
    "tweets": ["$raw_dir", "$$VERSION", "tweets.jsonl"],
    "extract": ["$clean_dir", "$$VERSION", "extract.jsonl"]
}

The __ENV object is a special scope. It contains a simple mapping from key to string value that acts like a set of constants. In this example, I defined a VERSION string that I'll use in the paths. You access the __ENV variables by prefixing a name with $$.

The first path definition in this example is the data_dir. This one is common. The first element in that path is a special constant variable reference, $$_IMPLICIT_ROOT. For example, if you used the code,

from pathsjson.automagic import PATHS

from a file running with the $PWD of /tweets_research/tests, pathsjson will ascend the directory structure towards the root until it finds a .paths.json. (Think: like git). When it finds that file, the parent directory becomes the $$_IMPLICIT_ROOT. This is usually how we think about projects. So, in this case, it would be /tweets_research.

The next three paths establish (again, common) directories for storing raw, clean, and log data. They all reference the $data_dir path in their construction. By now the use of arrays of path elements should be clear: it doesn't matter what operation system you're on. It's the responsibility of pathsjson to resolve it portably.

Now, look at the twitter_errors path. This references both the $log_dir path and the $$VERSION variable. This way, when you change the version, your directory paths will adjust automatically, but you can keep the old data. (Useful to prevent those, "OH SHIT!" moments.)

If you're using the reference implementation in python, your code to write to the logs may look like,

with PATHS.resolve('twitter_errors').open("w") as fp:
    fp.write("cool")

This will automatically create the necessary directories (like mkdir -p) and then open the file for writing. If you want to debug, you can do something like,

with PATHS.resolve('twitter_errors', VERSION='debug').open("w") as fp:
    fp.write("cool")
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