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asgarde-0.16.0
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مشاهده بیشترتوضیحات
Allows simplifying error handling with Apache Beam
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | asgarde-0.16.0 |
| نام | asgarde |
| نسخه کتابخانه | 0.16.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Mazlum TOSUN |
| ایمیل نویسنده | mazlum.tosun@gmail.com |
| آدرس صفحه اصلی | https://github.com/tosun-si/pasgarde |
| آدرس اینترنتی | https://pypi.org/project/asgarde/ |
| مجوز | - |
# 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
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6.10 | Python |
نحوه نصب
نصب پکیج whl asgarde-0.16.0:
pip install asgarde-0.16.0.whl
نصب پکیج tar.gz asgarde-0.16.0:
pip install asgarde-0.16.0.tar.gz