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updating documentation

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Daniel Tomlinson
2021-09-28 00:29:41 +01:00
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docs/documentation/usage.md
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This page documents how to run the pipeline locally to complete the task for the [dataset for 2020](https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#section-1).
The pipeline also runs in GCP using DataFlow and is discussed further on but can be viewed here. We also discuss how to adapt the pipeline so it can run against [the full dataset](https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#single-file).
The pipeline also runs in GCP using DataFlow and is discussed further on but can be viewed [here](../dataflow/index.md). We also discuss how to adapt the pipeline so it can run against [the full dataset](https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads#single-file).
## Download dataset
@@ -20,20 +20,20 @@ to download the data for 2020 and place in the input directory above.
## Entrypoint
The entrypoint to the pipeline is `analyse-properties`.
The entrypoint to the pipeline is `analyse_properties.main`.
## Available options
Running
```bash
analyse-properties --help
python -m analyse_properties.main --help
```
gives the following output:
```bash
usage: analyse-properties [-h] [--input INPUT] [--output OUTPUT]
usage: analyse_properties.main [-h] [--input INPUT] [--output OUTPUT]
optional arguments:
-h, --help show this help message and exit
@@ -43,14 +43,17 @@ optional arguments:
The default value for input is `./data/input/pp-2020.csv` and the default value for output is `./data/output/pp-2020`.
If passing in values for `input`/`output` these should be **full** paths to the files. The test will parse these inputs as a `str()` and pass this to `#!python beam.io.ReadFromText()`.
## Run the pipeline
To run the pipeline and complete the task run:
```bash
analyse-properties --runner DirectRunner
python -m analyse_properties.main \
--runner DirectRunner \
--input ./data/input/pp-2020.csv \
--output ./data/output/pp-2020
```
from the root of the repo.
The pipeline will use the 2020 dataset located in `./data/input` and output the resulting `.json` to `./data/output`.