mirror of
https://github.com/dtomlinson91/street_group_tech_test
synced 2025-12-22 11:55:45 +00:00
80376a662e
* adding initial skeleton * updating .gitignore * updating dev dependencies * adding report.py * updating notes * adding prospector.yaml * updating beam to install gcp extras * adding documentation * adding data exploration report + code * adding latest beam pipeline code * adding latest beam pipeline code * adding debug.py * adding latesty beam pipeline code * adding latest beam pipeline code * adding latest beam pipeline code * updating .gitignore * updating folder structure for data input/output * updating prospector.yaml * adding latest beam pipeline code * updating prospector.yaml * migrate beam pipeline to main.py * updating .gitignore * updating .gitignore * adding download script for data set * adding initial docs * moving inputs/outputs to use pathlib * removing shard_name_template from output file * adding pyenv 3.7.9 * removing requirements.txt for documentation * updating README.md * updating download data script for new location in GCS * adding latest beam pipeline code for dataflow * adding latest beam pipeline code for dataflow * adding latest beam pipeline code for dataflow * moving dataflow notes * updating prospector.yaml * adding latest beam pipeline code for dataflow * updating beam pipeline to use GroupByKey * updating download_data script with new bucket * update prospector.yaml * update dataflow documentation with new commands for vpc * adding latest beam pipeline code for dataflow with group optimisation * updating dataflow documentation * adding latest beam pipeline code for dataflow with group optimisation * updating download_data script with pp-2020 dataset * adding temporary notes * updating dataflow notes * adding latest beam pipeline code * updating dataflow notes * adding latest beam pipeline code for dataflow * adding debug print * moving panda-profiling report into docs * updating report.py * adding entrypoint command * adding initial docs * adding commands.md to notes * commenting out debug imports * updating documentation * updating latest beam pipeline with default inputs * updating poetry * adding requirements.txt * updating documentation
96 lines
3.4 KiB
Markdown
96 lines
3.4 KiB
Markdown
# Approach
|
|
|
|
The general approach to the pipeline is:
|
|
|
|
## Loading stage
|
|
|
|
- Load using `#!python beam.io.ReadFromText()`
|
|
- Split the string loaded by `,` as it's a comma delimited `.csv`.
|
|
- Strip the leading/trailing `"` marks.
|
|
|
|
The result is an array with each element representing a single column in that row.
|
|
|
|
## Cleaning stage
|
|
|
|
Already discussed.
|
|
|
|
## Create a mapping table
|
|
|
|
The mapping table takes each row and creates a `(key,value)` pair with:
|
|
|
|
- The key being the id across all columns (`id_all_columns`).
|
|
- The value being the raw data as an array.
|
|
|
|
The mapping table is then condensed to a single dictionary with these key, value pairs (automatically deduplicating repeated rows) and is used as a side input further down the pipeline.
|
|
|
|
This mapping table is created to ensure the `GroupByKey` operation is as quick as possible. The more data you have to process in a `GroupByKey`, the longer the operation takes. By doing the `GroupByKey` using just the ids, the pipeline can process the files much quicker than if we included the raw data in this operation.
|
|
|
|
## Prepare stage
|
|
|
|
- Take the mapping table data (before it is condensed) and create a unique id ignoring the price and date (`id_without_price_date`).
|
|
|
|
This id will not be unique: for properties with more than one transaction they will share this id.
|
|
|
|
- Create a `(key, value)` pair with:
|
|
- The key being `id_without_price_date`.
|
|
- The value being `id_all_columns`.
|
|
- Group by `id_without_price_date`.
|
|
|
|
This results in a PCollection that looks like: `(id_without_price_date, [id_all_columns,...])`
|
|
|
|
- Deduplicate the `id_all_columns` inside this array to eliminate repeated rows that are exactly the same.
|
|
- Use the mapping table as a side input to reinsert the raw data using the `id_all_columns`.
|
|
|
|
<details>
|
|
<summary>Example for No.1 B90 3LA</summary>
|
|
|
|
Mapping table (pre condensed):
|
|
|
|
```json
|
|
('fd4634faec47c29de40bbf7840723b41', ['317500', '2020-11-13 00:00', 'B90 3LA', '1', '', 'VERSTONE ROAD', 'SHIRLEY', 'SOLIHULL', 'SOLIHULL', 'WEST MIDLANDS', ''])
|
|
('fd4634faec47c29de40bbf7840723b41', ['317500', '2020-11-13 00:00', 'B90 3LA', '1', '', 'VERSTONE ROAD', 'SHIRLEY', 'SOLIHULL', 'SOLIHULL', 'WEST MIDLANDS', ''])
|
|
```
|
|
|
|
Mapping table (condensed):
|
|
|
|
```json
|
|
{'fd4634faec47c29de40bbf7840723b41': ['317500', '2020-11-13 00:00', 'B90 3LA', '1', '', 'VERSTONE ROAD', 'SHIRLEY', 'SOLIHULL', 'SOLIHULL', 'WEST MIDLANDS', '']}
|
|
```
|
|
|
|
Prepared (key, value):
|
|
|
|
```json
|
|
('fe205bfe66bc7f18c50c8f3d77ec3e30', 'fd4634faec47c29de40bbf7840723b41')
|
|
('fe205bfe66bc7f18c50c8f3d77ec3e30', 'fd4634faec47c29de40bbf7840723b41')
|
|
```
|
|
|
|
Prepared (GroupByKey):
|
|
|
|
```json
|
|
('fe205bfe66bc7f18c50c8f3d77ec3e30', ['fd4634faec47c29de40bbf7840723b41', 'fd4634faec47c29de40bbf7840723b41'])
|
|
```
|
|
|
|
Prepared (Deduplicated):
|
|
|
|
```json
|
|
('fe205bfe66bc7f18c50c8f3d77ec3e30', ['fd4634faec47c29de40bbf7840723b41'])
|
|
```
|
|
|
|
Use mapping table as side input:
|
|
|
|
```json
|
|
('fe205bfe66bc7f18c50c8f3d77ec3e30', ['317500', '2020-11-13 00:00', 'B90 3LA', '1', '', 'VERSTONE ROAD', 'SHIRLEY', 'SOLIHULL', 'SOLIHULL', 'WEST MIDLANDS', ''])
|
|
```
|
|
|
|
</details>
|
|
|
|
## Format stage
|
|
|
|
This stage takes the result and constructs a `json` object out of the grouped data. The schema for this output is discussed in the following page.
|
|
|
|
## Save stage
|
|
|
|
- The PCollection is combined with `#!python beam.combiners.ToList()`
|
|
- Apply `json.dumps()` for proper quotation marks for strings.
|
|
- Write to text with `#!python beam.io.WriteToText`.
|