yet (another spark) etl framework
Project description
yetl
Install
pip install yetl-framework
Configuration framework for databricks pipelines. Define configuration and table dependencies in yaml config then get the table mappings config model:
Define your tables.
landing: # this is the landing stage in the deltalake house
read: # this is the type of spark asset that the pipeline needs to read
landing_dbx_patterns:
customer_details_1: null
customer_details_2: null
raw: # this is the bronze stage in the deltalake house
delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
raw_dbx_patterns: # this is the database name
customers: # this is a table name and it's subsequent properties
ids: id
depends_on:
- landing.landing_dbx_patterns.customer_details_1
- landing.landing_dbx_patterns.customer_details_2
warning_thresholds:
invalid_ratio: 0.1
invalid_rows: 0
max_rows: 100
min_rows: 5
exception_thresholds:
invalid_ratio: 0.2
invalid_rows: 2
max_rows: 1000
min_rows: 0
custom_properties:
process_group: 1
base: # this is the silver stage in the delta lakehouse
delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
# delta table properties can be set at stage level or table level
delta_properties:
delta.appendOnly: true
delta.autoOptimize.autoCompact: true
delta.autoOptimize.optimizeWrite: true
delta.enableChangeDataFeed: false
base_dbx_patterns: # this is a database name
customer_details_1: # this is a table name and it's subsequent properties
ids: id
depends_on:
- raw.raw_dbx_patterns.customers
# delta table properties can be set at stage level or table level
# table level properties will overwride stage level properties
delta_properties:
delta.enableChangeDataFeed: true
customer_details_2: # this is a table name and it's subsequent properties
ids: id
depends_on:
- raw.raw_dbx_patterns.customers
Define you load configuration:
version: 1.0.0
tables: ./tables.yaml
landing: # this is the landing stage in the deltalake house
read: # this is the type of spark asset that the pipeline needs to read from
trigger: customerdetailscomplete-{{filename_date_format}}*.flg
trigger_type: file
container: datalake
root: "/mnt/{{container}}/data/landing/dbx_patterns/{{table}}/{{path_date_format}}"
filename: "{{table}}-{{filename_date_format}}*.csv"
filename_date_format: "%Y%m%d"
path_date_format: "%Y%m%d"
format: cloudFiles
spark_schema: ../schema/{{table.lower()}}.yaml
options:
# autoloader
cloudFiles.format: csv
cloudFiles.schemaLocation: /mnt/{{container}}/checkpoint/{{checkpoint}}
cloudFiles.useIncrementalListing: auto
# schema
inferSchema: false
enforceSchema: true
columnNameOfCorruptRecord: _corrupt_record
# csv
header: false
mode: PERMISSIVE
encoding: windows-1252
delimiter: ","
escape: '"'
nullValue: ""
quote: '"'
emptyValue: ""
raw: # this is the bronze stage in the deltalake house
delta_lake: # this is the type of spark asset that the pipeline needs to read and write to
# delta table properties can be set at stage level or table level
delta_properties:
delta.appendOnly: true
delta.autoOptimize.autoCompact: true
delta.autoOptimize.optimizeWrite: true
delta.enableChangeDataFeed: false
managed: false
create_table: true
container: datalake
root: /mnt/{{container}}/data/raw
path: "{{database}}/{{table}}"
options:
checkpointLocation: /mnt/{{container}}/checkpoint/{{database}}_{{table}}
mergeSchema: true
Import the config objects into you pipeline:
from yetl import Config, Timeslice, StageType
pipeline = "auto_load_schema"
project = "test_project"
timeslice = Timeslice(day="*", month="*", year="*")
config = Config(
project=project, pipeline=pipeline
)
table_mapping = config.get_table_mapping(
timeslice=timeslice, stage=StageType.raw, table="customers"
)
print(table_mapping)
Use even less code and use the decorator pattern:
@yetl_flow(
project="test_project",
stage=StageType.raw
)
def auto_load_schema(table_mapping:TableMapping):
# << ADD YOUR PIPELINE LOGIC HERE - USING TABLE MAPPING CONFIG >>
return table_mapping # return whatever you want here.
result = auto_load_schema(table="customers")
Development Setup
pip install -r requirements.txt
Unit Tests
To run the unit tests with a coverage report.
pip install -e .
pytest test/unit --junitxml=junit/test-results.xml --cov=yetl --cov-report=xml --cov-report=html
Integration Tests
To run the integration tests with a coverage report.
pip install -e .
pytest test/integration --junitxml=junit/test-results.xml --cov=yetl --cov-report=xml --cov-report=html
Build
python setup.py sdist bdist_wheel
Publish
twine upload dist/*
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