| # |
| # Licensed to the Apache Software Foundation (ASF) under one or more |
| # contributor license agreements. See the NOTICE file distributed with |
| # this work for additional information regarding copyright ownership. |
| # The ASF licenses this file to You under the Apache License, Version 2.0 |
| # (the "License"); you may not use this file except in compliance with |
| # the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # |
| # pytype: skip-file |
| |
| import array |
| import json |
| from collections import OrderedDict |
| |
| import numpy as np |
| from avro.schema import Parse |
| |
| from apache_beam.typehints import trivial_inference |
| from apache_beam.typehints import typehints |
| |
| try: |
| import pyarrow as pa |
| except ImportError: |
| pa = None |
| |
| |
| def infer_element_type(elements): |
| """For internal use only; no backwards-compatibility guarantees. |
| |
| Infer a Beam type for a list of elements. |
| |
| Args: |
| elements (List[Any]): A list of elements for which the type should be |
| inferred. |
| |
| Returns: |
| A Beam type encompassing all elements. |
| """ |
| element_type = typehints.Union[[ |
| trivial_inference.instance_to_type(e) for e in elements |
| ]] |
| return element_type |
| |
| |
| def infer_typehints_schema(data): |
| """For internal use only; no backwards-compatibility guarantees. |
| |
| Infer Beam types for tabular data. |
| |
| Args: |
| data (List[dict]): A list of dictionaries representing rows in a table. |
| |
| Returns: |
| An OrderedDict mapping column names to Beam types. |
| """ |
| column_data = OrderedDict() |
| for row in data: |
| for key, value in row.items(): |
| column_data.setdefault(key, []).append(value) |
| column_types = OrderedDict([ |
| (key, infer_element_type(values)) for key, values in column_data.items() |
| ]) |
| return column_types |
| |
| |
| def infer_avro_schema(data, use_fastavro=False): |
| """For internal use only; no backwards-compatibility guarantees. |
| |
| Infer avro schema for tabular data. |
| |
| Args: |
| data (List[dict]): A list of dictionaries representing rows in a table. |
| use_fastavro (bool): A flag indicating whether the schema should be |
| constructed using fastavro. |
| |
| Returns: |
| An avro schema object. |
| """ |
| _typehint_to_avro_type = { |
| type(None): "null", |
| int: "int", |
| float: "double", |
| str: "string", |
| bytes: "bytes", |
| np.ndarray: "bytes", |
| array.array: "bytes", |
| } |
| |
| def typehint_to_avro_type(value): |
| if isinstance(value, typehints.UnionConstraint): |
| return sorted( |
| typehint_to_avro_type(union_type) for union_type in value.union_types) |
| else: |
| return _typehint_to_avro_type[value] |
| |
| column_types = infer_typehints_schema(data) |
| avro_fields = [{ |
| "name": str(key), "type": typehint_to_avro_type(value) |
| } for key, |
| value in column_types.items()] |
| schema_dict = { |
| "namespace": "example.avro", |
| "name": "User", |
| "type": "record", |
| "fields": avro_fields |
| } |
| if use_fastavro: |
| from fastavro import parse_schema |
| return parse_schema(schema_dict) |
| else: |
| return Parse(json.dumps(schema_dict)) |
| |
| |
| def infer_pyarrow_schema(data): |
| """For internal use only; no backwards-compatibility guarantees. |
| |
| Infer PyArrow schema for tabular data. |
| |
| Args: |
| data (List[dict]): A list of dictionaries representing rows in a table. |
| |
| Returns: |
| A PyArrow schema object. |
| """ |
| column_data = OrderedDict() |
| for row in data: |
| for key, value in row.items(): |
| column_data.setdefault(key, []).append(value) |
| column_types = OrderedDict([ |
| (key, pa.array(value).type) for key, value in column_data.items() |
| ]) |
| return pa.schema(list(column_types.items())) |