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Asgarde allows simplifying error handling with Apache Beam Python, with less code, more concise and expressive code.

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# Asgarde

This module allows simplifying error handling with Apache Beam Python.

## Versions compatibility between Beam and Asgarde

| Asgarde      | Beam |
| -----------  | ----------- |
| 0.16.0       | \>= 2.37.0   |

## Installation of project

The project is hosted on PyPi repository.\
You can install it with all the build tools compatibles with PyPi and pip.

#### PyPi

##### Example with pip command line from bash

```bash
pip install asgarde==0.16.0
```

##### Example with requirements.txt

requirements.txt file

```text
asgarde==0.16.0
```

```bash
pip install -r requirements.txt
```

##### Example with Pipenv

PipFile

```text
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"

[packages]
asgarde = "==0.16.0"

[requires]
python_version = "3.8"
```

```bash
pipenv shell

pipenv install
```

- pipenv shell creates a virtual env 
- `pipenv install` installs all the packages specified in the Pipfile
- A PipFile.lock is generated with a hash on installed packages

https://pipenv.pypa.io/en/latest/

## Example of native error handling with Beam

The following example shows error handling in each step with usual Beam code.

```python
@dataclass
class TeamInfo:
    name: str
    country: str
    city: str

@dataclass
class Failure:
    pipeline_step: str
    input_element: str
    exception: Exception

team_names = [
    'PSG',
    'OL',
    'Real',
    'ManU'
]
    
team_countries = {
    'PSG': 'France',
    'OL': 'France',
    'Real': 'Spain',
    'ManU': 'England'
}

team_cities = {
    'PSG': 'Paris',
    'OL': 'France',
    'Real': 'Madrid',
    'ManU': 'Manchester'
}

class MapToTeamWithCountry(DoFn):

def process(self, element, *args, **kwargs):
        try:
            team_name: str = element

yield TeamInfo(
                name=team_name,
                country=team_countries[team_name],
                city=''
            )
        except Exception as err:
            failure = Failure(
                pipeline_step="Map 1",
                input_element=element,
                exception=err
            )

yield pvalue.TaggedOutput(FAILURES, failure)

class MapToTeamWithCity(DoFn):

def process(self, element, *args, **kwargs):
        try:
            team_info: TeamInfo = element
            city: str = team_cities[team_info.name]

yield TeamInfo(
                name=team_info.name,
                country=team_info.country,
                city=city
            )
        except Exception as err:
            failure = Failure(
                pipeline_step="Map 2",
                input_element=element,
                exception=err
            )

yield pvalue.TaggedOutput(FAILURES, failure)

class FilterFranceTeams(DoFn):

def process(self, element, *args, **kwargs):
        try:
            team_info: TeamInfo = element

if team_info.country == 'France':
                yield element
        except Exception as err:
            failure = Failure(
                pipeline_step="Filter France teams",
                input_element=element,
                exception=err
            )

yield pvalue.TaggedOutput(FAILURES, failure)

# In Beam pipeline.
input_teams: PCollection[str] = p | 'Read' >> beam.Create(team_names)

outputs_map1, failures_map1 = (input_teams | 'Map to team with country' >> ParDo(MapToTeamWithCountry())
                               .with_outputs(FAILURES, main='outputs'))

outputs_map2, failures_map2 = (outputs_map1 | 'Map to team with city' >> ParDo(MapToTeamWithCity())
                               .with_outputs(FAILURES, main='outputs'))

outputs_filter, failures_filter = (outputs_map2 | 'Filter France teams' >> ParDo(FilterFranceTeams())
                                   .with_outputs(FAILURES, main='outputs'))

all_failures = (failures_map1, failures_map2, failures_filter) | 'All Failures PCollections' >> beam.Flatten()
```

This example starts with an input `PCollection` containing team names.\
Then 3 operations and steps are applied : 2 maps and 1 filter.

For each operation a custom `DoFn` class is proposed and must override `process` function containing the 
transformation logic.\
A `try except bloc` is added to catch all the possible errors.\
In the `Except` bloc a `Failure` object is built with input element and current exception. This object is then added on 
a `tuple tag` dedicated to errors.\
This `tag` mechanism allows having multi sink in the pipeline and a dead letter queue for failures.

There are some inconveniences : 
- We have to repeat many technical codes and same logic like `try except bloc`, `tuple tags`, 
`failure logic` and all this logic can be centralized.
- If we want to intercept all the possible errors in the pipeline, we have to repeat the recovery of output and failure in each step.
- All the failures `PCollection` must be concatenated at end.
- The code is verbose.

The repetition of technical codes is error-prone and less maintainable.

## Example of error handling using Asgarde library

```python
# Beam pipeline with Asgarde library.
input_teams: PCollection[str] = p | 'Read' >> beam.Create(team_names)

result = (CollectionComposer.of(input_teams)
            .map('Map with country', lambda tname: TeamInfo(name=tname, country=team_countries[tname], city=''))
            .map('Map with city', lambda tinfo: TeamInfo(name=tinfo.name, country=tinfo.country, city=team_cities[tinfo.name]))
            .filter('Filter french team', lambda tinfo: tinfo.country == 'France'))

result_outputs: PCollection[TeamInfo] = result.outputs
result_failures: PCollection[Failure] = result.failures
```

### CollectionComposer class

Asgarde proposes a `CollectionComposer` wrapper class instantiated from a `PCollection`.

### Operators exposed by CollectionComposer class

The `CollectionComposer` class exposes the following operators : `map`, `flatMap` and `filter`.

These classical operators takes a function, the implementation can be : 
- A `lambda expression`
- A `method` having the same signature of the expected `function`

### Failure object exposed by Asgarde

Behind the scene, for each step the `CollectionComposer` class adds `try except` bloc and `tuple tag logic` with output
and failure `sinks`.

The bad sink is based on a `Failure` object proposed by the library :

```python
@dataclass
class Failure:
    pipeline_step: str
    input_element: str
    exception: Exception
```

This object contains the current pipeline step name, input element with string form and current exception.

Input element on Failure object are built following these rules :
- If the current element in the `PCollection` is a `dict`, the Json string form of this `dict` is retrieved
- For all others types, the `string` form of object is retrieved. If developers want to bring their own serialization
logic, they have to override `__str__` method in the object, example for a `dataclass` :

```python
import dataclasses
import json
from dataclasses import dataclass

@dataclass
class Team:
    name: str

def __str__(self) -> str:
        return json.dumps(dataclasses.asdict(self))
```

### Result of CollectionComposer flow

The `CollectionComposer` class after applying and chaining different operations, returns a `tuple` with : 
- Output `PCollection`
- Failures `PCollection`

```python
result = (CollectionComposer.of(input_teams)
            .map('Map with country', lambda tname: TeamInfo(name=tname, country=team_countries[tname], city=''))
            .map('Map with city', lambda tinfo: TeamInfo(name=tinfo.name, country=tinfo.country, city=team_cities[tinfo.name]))
            .filter('Filter french team', lambda tinfo: tinfo.country == 'France'))

result_outputs: PCollection[TeamInfo] = result.outputs
result_failures: PCollection[Failure] = result.failures
```

### Example of a flow with side inputs

`Asgarde` allows applying transformations with error handling and passing `side inputs`.\
The syntax is the same as usual Beam pipeline with `AsDict` or `AsList` passed as function parameters.

```python
def to_team_with_city(self, team_name: str, team_countries: Dict[str, str]) -> TeamInfo:
    return TeamInfo(name=team_name, country=team_countries[team_name], city='')

team_countries = {
    'PSG': 'France',
    'OL': 'France',
    'Real': 'Spain',
    'ManU': 'England'
}

# Side inputs.
countries_side_inputs = p | 'Countries' >> beam.Create(team_countries)

# Beam Pipeline.
result = (CollectionComposer.of(input_teams)
            .map('Map with country', self.to_team_with_city, team_countries=AsDict(countries_side_inputs))
            .map('Map with city', lambda ti: TeamInfo(name=ti.name, country=ti.country, city=team_cities[ti.name]))
            .filter('Filter french team', lambda ti: ti.country == 'France'))

result_outputs: PCollection[str] = result.outputs
result_failures: PCollection[Failure] = result.failures
```

### Asgarde and error handling with Beam DoFn lifecyle

`Asgarde` allows interacting with `DoFn` lifecycle while chaining transformation with error handling, example :

```python
(CollectionComposer.of(input_teams)
     .map('Map to Team info',
          input_element_mapper=lambda team_name: TeamInfo(name=team_name, country='test', city='test'),
          setup_action=lambda: print('Setup action'),
          start_bundle_action=lambda: print('Start bundle action'),
          finish_bundle_action=lambda: print('Finish bundle action'),
          teardown_action=lambda: print('Teardown action'))
     )
```

The `map` and `flat_map` methods of `CollectionComposer` class propose the following functions to interact with 
`DoFn` lifecycle :
- setup_action
- start_bundle_action
- finish_bundle_action
- teardown_action

These functions take a `function` without input parameter and return `None`, it corresponds to an action executed 
in the dedicated lifecycle method :

https://beam.apache.org/documentation/transforms/python/elementwise/pardo/

### Advantage of using Asgarde

`Asgarde` presents the following advantages :
- Simplifies error handling with less code and more expressive and concise code
- No need to repeat same technical code for error handling like `try except` bloc, `tuple tags` and concatenation of all the pipeline failures
- Allows interacting with Beam lifecycle while chaining the transformation and error handling

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