Source code for apache_beam.yaml.yaml_combine

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"""This module defines the basic Combine operation."""

from typing import Any
from typing import Iterable
from typing import Mapping
from typing import Optional

import apache_beam as beam
from apache_beam import typehints
from apache_beam.typehints import row_type
from apache_beam.typehints import trivial_inference
from apache_beam.typehints.decorators import get_type_hints
from apache_beam.typehints.schemas import named_fields_from_element_type
from apache_beam.utils import python_callable
from apache_beam.yaml import options
from apache_beam.yaml import yaml_mapping
from apache_beam.yaml import yaml_provider

    'sum': sum,
    'max': max,
    'min': min,
    'all': all,
    'any': any,
    'mean': beam.transforms.combiners.MeanCombineFn(),
    'count': beam.transforms.combiners.CountCombineFn(),
    'group': beam.transforms.combiners.ToListCombineFn(),
    'concat': beam.transforms.combiners.ConcatListCombineFn(),

[docs]def normalize_combine(spec): """Expands various shorthand specs for combine (which can otherwise be quite verbose for simple cases.) We do this here so that it doesn't need to be done per language. The following are all equivalent:: dest: fn_type dest: value: dest fn: fn_type dest: value: dest fn: type: fn_type """ from apache_beam.yaml.yaml_transform import SafeLineLoader if spec['type'] == 'Combine': config = spec.get('config') if isinstance(config.get('group_by'), str): config['group_by'] = [config['group_by']] def normalize_agg(dest, agg): if isinstance(agg, str): agg = {'fn': agg} if 'value' not in agg and spec.get('language') != 'sql': agg['value'] = dest if isinstance(agg['fn'], str): agg['fn'] = {'type': agg['fn']} return agg if 'combine' not in config: raise ValueError('Missing combine parameter in Combine config.') config['combine'] = { dest: normalize_agg(dest, agg) for (dest, agg) in SafeLineLoader.strip_metadata(config['combine']).items() } return spec
[docs]class PyJsYamlCombine(beam.PTransform): def __init__( self, group_by: Iterable[str], combine: Mapping[str, Mapping[str, Any]], language: Optional[str] = None): self._group_by = group_by self._combine = combine self._language = language
[docs] def expand(self, pcoll): options.YamlOptions.check_enabled(pcoll.pipeline, 'Combine') input_types = dict(named_fields_from_element_type(pcoll.element_type)) all_fields = list(input_types.keys()) unknown_keys = set(self._group_by) - set(all_fields) if unknown_keys: raise ValueError(f'Unknown grouping columns: {list(unknown_keys)}') def create_combine_fn(fn_spec): if 'type' not in fn_spec: raise ValueError(f'CombineFn spec missing type: {fn_spec}') elif fn_spec['type'] in BUILTIN_COMBINE_FNS: return BUILTIN_COMBINE_FNS[fn_spec['type']] elif self._language == 'python': # TODO(yaml): Support output_type here as well. fn = python_callable.PythonCallableWithSource.load_from_source( fn_spec['type']) if 'config' in fn_spec: fn = fn(**fn_spec['config']) return fn else: raise TypeError('Unknown CombineFn: {fn_spec}') def extract_return_type(expr): if isinstance(expr, str) and expr in input_types: return input_types[expr] expr_hints = get_type_hints(expr) if (expr_hints and expr_hints.has_simple_output_type() and expr_hints.simple_output_type(None) != typehints.Any): return expr_hints.simple_output_type(None) elif callable(expr): return trivial_inference.infer_return_type(expr, [pcoll.element_type]) else: return Any # TODO(yaml): Support error handling. transform = beam.GroupBy(*self._group_by) output_types = [(k, input_types[k]) for k in self._group_by] for output, agg in self._combine.items(): expr = yaml_mapping._as_callable( all_fields, agg['value'], 'Combine', self._language) fn = create_combine_fn(agg['fn']) transform = transform.aggregate_field(expr, fn, output) # TODO(yaml): See if this logic can be pushed into GroupBy itself. expr_type = extract_return_type(expr) print('expr', expr, 'expr_type', expr_type) if isinstance(fn, beam.CombineFn): # TODO(yaml): Better inference on CombineFns whose outputs types are # functions of their input types combined_type = extract_return_type(fn) elif fn in (sum, min, max): combined_type = expr_type elif fn in (any, all): combined_type = bool else: combined_type = Any output_types.append((output, combined_type)) return pcoll | transform.with_output_types( row_type.RowTypeConstraint.from_fields(output_types))
@beam.ptransform.ptransform_fn def _SqlCombineTransform( pcoll, sql_transform_constructor, group_by, combine, language=None): options.YamlOptions.check_enabled(pcoll.pipeline, 'Combine') all_fields = [ x for x, _ in named_fields_from_element_type(pcoll.element_type) ] unknown_keys = set(group_by) - set(all_fields) if unknown_keys: raise ValueError(f'Unknown grouping columns: {list(unknown_keys)}') def combine_col(dest, fn_spec): if 'value' in fn_spec or 'config' in fn_spec['fn']: expr = '%s(%s)' % ( fn_spec['fn']['type'], ', '.join([fn_spec['value']] + list(fn_spec['fn'].get('config', {}).values()))) else: expr = fn_spec['fn']['type'] return f'{expr} as {dest}' return pcoll | sql_transform_constructor( 'SELECT %s FROM PCOLLECTION GROUP BY %s' % ( ', '.join( list(group_by) + [combine_col(dest, fn_spec) for dest, fn_spec in combine.items()]), ', '.join(group_by), ))
[docs]def create_combine_providers(): return [ yaml_provider.InlineProvider({ 'Combine-generic': PyJsYamlCombine, 'Combine-python': PyJsYamlCombine, 'Combine-javascript': PyJsYamlCombine, }), yaml_provider.SqlBackedProvider({ 'Combine-generic': _SqlCombineTransform, 'Combine-sql': _SqlCombineTransform, 'Combine-calcite': _SqlCombineTransform, }), ]