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"""Base operator for SQL to GCS operators."""
from __future__ import annotations

import abc
import json
import os
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Sequence

import pyarrow as pa
import pyarrow.parquet as pq
import unicodecsv as csv

from airflow.models import BaseOperator
from import GCSHook

    from airflow.utils.context import Context

[docs]class BaseSQLToGCSOperator(BaseOperator): """ Copy data from SQL to Google Cloud Storage in JSON, CSV, or Parquet format. :param sql: The SQL to execute. :param bucket: The bucket to upload to. :param filename: The filename to use as the object name when uploading to Google Cloud Storage. A ``{}`` should be specified in the filename to allow the operator to inject file numbers in cases where the file is split due to size. :param schema_filename: If set, the filename to use as the object name when uploading a .json file containing the BigQuery schema fields for the table that was dumped from the database. :param approx_max_file_size_bytes: This operator supports the ability to split large table dumps into multiple files (see notes in the filename param docs above). This param allows developers to specify the file size of the splits. Check to see the maximum allowed file size for a single object. :param export_format: Desired format of files to be exported. (json, csv or parquet) :param stringify_dict: Whether to dump Dictionary type objects (such as JSON columns) as a string. Applies only to CSV/JSON export format. :param field_delimiter: The delimiter to be used for CSV files. :param null_marker: The null marker to be used for CSV files. :param gzip: Option to compress file for upload (does not apply to schemas). :param schema: The schema to use, if any. Should be a list of dict or a str. Pass a string if using Jinja template, otherwise, pass a list of dict. Examples could be seen: /schemas#specifying_a_json_schema_file :param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud. :param delegate_to: The account to impersonate using domain-wide delegation of authority, if any. For this to work, the service account making the request must have domain-wide delegation enabled. :param parameters: a parameters dict that is substituted at query runtime. :param impersonation_chain: Optional service account to impersonate using short-term credentials, or chained list of accounts required to get the access_token of the last account in the list, which will be impersonated in the request. If set as a string, the account must grant the originating account the Service Account Token Creator IAM role. If set as a sequence, the identities from the list must grant Service Account Token Creator IAM role to the directly preceding identity, with first account from the list granting this role to the originating account (templated). :param upload_metadata: whether to upload the row count metadata as blob metadata :param exclude_columns: set of columns to exclude from transmission :param partition_columns: list of columns to use for file partitioning. In order to use this parameter, you must sort your dataset by partition_columns. Do this by passing an ORDER BY clause to the sql query. Files are uploaded to GCS as objects with a hive style partitioning directory structure (templated). """
[docs] template_fields: Sequence[str] = ( "sql", "bucket", "filename", "schema_filename", "schema", "parameters", "impersonation_chain", "partition_columns",
[docs] template_ext: Sequence[str] = (".sql",)
[docs] template_fields_renderers = {"sql": "sql"}
[docs] ui_color = "#a0e08c"
def __init__( self, *, sql: str, bucket: str, filename: str, schema_filename: str | None = None, approx_max_file_size_bytes: int = 1900000000, export_format: str = "json", stringify_dict: bool = False, field_delimiter: str = ",", null_marker: str | None = None, gzip: bool = False, schema: str | list | None = None, parameters: dict | None = None, gcp_conn_id: str = "google_cloud_default", delegate_to: str | None = None, impersonation_chain: str | Sequence[str] | None = None, upload_metadata: bool = False, exclude_columns: set | None = None, partition_columns: list | None = None, **kwargs, ) -> None: super().__init__(**kwargs) if exclude_columns is None: exclude_columns = set() self.sql = sql self.bucket = bucket self.filename = filename self.schema_filename = schema_filename self.approx_max_file_size_bytes = approx_max_file_size_bytes self.export_format = export_format.lower() self.stringify_dict = stringify_dict self.field_delimiter = field_delimiter self.null_marker = null_marker self.gzip = gzip self.schema = schema self.parameters = parameters self.gcp_conn_id = gcp_conn_id self.delegate_to = delegate_to self.impersonation_chain = impersonation_chain self.upload_metadata = upload_metadata self.exclude_columns = exclude_columns self.partition_columns = partition_columns
[docs] def execute(self, context: Context): if self.partition_columns: f"Found partition columns: {','.join(self.partition_columns)}. " "Assuming the SQL statement is properly sorted by these columns in " "ascending or descending order." )"Executing query") cursor = self.query() # If a schema is set, create a BQ schema JSON file. if self.schema_filename:"Writing local schema file") schema_file = self._write_local_schema_file(cursor) # Flush file before uploading schema_file["file_handle"].flush()"Uploading schema file to GCS.") self._upload_to_gcs(schema_file) schema_file["file_handle"].close() counter = 0 files = [] total_row_count = 0 total_files = 0"Writing local data files") for file_to_upload in self._write_local_data_files(cursor): # Flush file before uploading file_to_upload["file_handle"].flush()"Uploading chunk file #%d to GCS.", counter) self._upload_to_gcs(file_to_upload)"Removing local file") file_to_upload["file_handle"].close() # Metadata to be outputted to Xcom total_row_count += file_to_upload["file_row_count"] total_files += 1 files.append( { "file_name": file_to_upload["file_name"], "file_mime_type": file_to_upload["file_mime_type"], "file_row_count": file_to_upload["file_row_count"], } ) counter += 1 file_meta = { "bucket": self.bucket, "total_row_count": total_row_count, "total_files": total_files, "files": files, } return file_meta
[docs] def convert_types(self, schema, col_type_dict, row) -> list: """Convert values from DBAPI to output-friendly formats.""" return [ self.convert_type(value, col_type_dict.get(name), stringify_dict=self.stringify_dict) for name, value in zip(schema, row)
] def _write_local_data_files(self, cursor): """ Takes a cursor, and writes results to a local file. :return: A dictionary where keys are filenames to be used as object names in GCS, and values are file handles to local files that contain the data for the GCS objects. """ org_schema = list(map(lambda schema_tuple: schema_tuple[0], cursor.description)) schema = [column for column in org_schema if column not in self.exclude_columns] col_type_dict = self._get_col_type_dict() file_no = 0 file_mime_type = self._get_file_mime_type() file_to_upload, tmp_file_handle = self._get_file_to_upload(file_mime_type, file_no) if self.export_format == "csv": csv_writer = self._configure_csv_file(tmp_file_handle, schema) if self.export_format == "parquet": parquet_schema = self._convert_parquet_schema(cursor) parquet_writer = self._configure_parquet_file(tmp_file_handle, parquet_schema) prev_partition_values = None curr_partition_values = None for row in cursor: if self.partition_columns: row_dict = dict(zip(schema, row)) curr_partition_values = tuple( [row_dict.get(partition_column, "") for partition_column in self.partition_columns] ) if prev_partition_values is None: # We haven't set prev_partition_values before. Set to current prev_partition_values = curr_partition_values elif prev_partition_values != curr_partition_values: # If the partition values differ, write the current local file out # Yield first before we write the current record file_no += 1 if self.export_format == "parquet": parquet_writer.close() file_to_upload["partition_values"] = prev_partition_values yield file_to_upload file_to_upload, tmp_file_handle = self._get_file_to_upload(file_mime_type, file_no) if self.export_format == "csv": csv_writer = self._configure_csv_file(tmp_file_handle, schema) if self.export_format == "parquet": parquet_writer = self._configure_parquet_file(tmp_file_handle, parquet_schema) # Reset previous to current after writing out the file prev_partition_values = curr_partition_values # Incrementing file_row_count after partition yield ensures all rows are written file_to_upload["file_row_count"] += 1 # Proceed to write the row to the localfile if self.export_format == "csv": row = self.convert_types(schema, col_type_dict, row) if self.null_marker is not None: row = [value if value is not None else self.null_marker for value in row] csv_writer.writerow(row) elif self.export_format == "parquet": row = self.convert_types(schema, col_type_dict, row) if self.null_marker is not None: row = [value if value is not None else self.null_marker for value in row] row_pydic = {col: [value] for col, value in zip(schema, row)} tbl = pa.Table.from_pydict(row_pydic, parquet_schema) parquet_writer.write_table(tbl) else: row = self.convert_types(schema, col_type_dict, row) row_dict = dict(zip(schema, row)) tmp_file_handle.write( json.dumps(row_dict, sort_keys=True, ensure_ascii=False).encode("utf-8") ) # Append newline to make dumps BigQuery compatible. tmp_file_handle.write(b"\n") # Stop if the file exceeds the file size limit. fppos = tmp_file_handle.tell(), os.SEEK_END) file_size = tmp_file_handle.tell(), os.SEEK_SET) if file_size >= self.approx_max_file_size_bytes: file_no += 1 if self.export_format == "parquet": parquet_writer.close() file_to_upload["partition_values"] = curr_partition_values yield file_to_upload file_to_upload, tmp_file_handle = self._get_file_to_upload(file_mime_type, file_no) if self.export_format == "csv": csv_writer = self._configure_csv_file(tmp_file_handle, schema) if self.export_format == "parquet": parquet_writer = self._configure_parquet_file(tmp_file_handle, parquet_schema) if self.export_format == "parquet": parquet_writer.close() # Last file may have 0 rows, don't yield if empty if file_to_upload["file_row_count"] > 0: file_to_upload["partition_values"] = curr_partition_values yield file_to_upload def _get_file_to_upload(self, file_mime_type, file_no): """Returns a dictionary that represents the file to upload""" tmp_file_handle = NamedTemporaryFile(delete=True) return ( { "file_name": self.filename.format(file_no), "file_handle": tmp_file_handle, "file_mime_type": file_mime_type, "file_row_count": 0, }, tmp_file_handle, ) def _get_file_mime_type(self): if self.export_format == "csv": file_mime_type = "text/csv" elif self.export_format == "parquet": file_mime_type = "application/octet-stream" else: file_mime_type = "application/json" return file_mime_type def _configure_csv_file(self, file_handle, schema): """Configure a csv writer with the file_handle and write schema as headers for the new file. """ csv_writer = csv.writer(file_handle, encoding="utf-8", delimiter=self.field_delimiter) csv_writer.writerow(schema) return csv_writer def _configure_parquet_file(self, file_handle, parquet_schema): parquet_writer = pq.ParquetWriter(, parquet_schema) return parquet_writer def _convert_parquet_schema(self, cursor): type_map = { "INTEGER": pa.int64(), "FLOAT": pa.float64(), "NUMERIC": pa.float64(), "BIGNUMERIC": pa.float64(), "BOOL": pa.bool_(), "STRING": pa.string(), "BYTES": pa.binary(), "DATE": pa.date32(), "DATETIME": pa.date64(), "TIMESTAMP": pa.timestamp("s"), } columns = [field[0] for field in cursor.description] bq_fields = [self.field_to_bigquery(field) for field in cursor.description] bq_types = [bq_field.get("type") if bq_field is not None else None for bq_field in bq_fields] pq_types = [type_map.get(bq_type, pa.string()) for bq_type in bq_types] parquet_schema = pa.schema(zip(columns, pq_types)) return parquet_schema @abc.abstractmethod
[docs] def query(self): """Execute DBAPI query."""
[docs] def field_to_bigquery(self, field) -> dict[str, str]: """Convert a DBAPI field to BigQuery schema format."""
[docs] def convert_type(self, value, schema_type, **kwargs): """Convert a value from DBAPI to output-friendly formats."""
def _get_col_type_dict(self): """Return a dict of column name and column type based on self.schema if not None.""" schema = [] if isinstance(self.schema, str): schema = json.loads(self.schema) elif isinstance(self.schema, list): schema = self.schema elif self.schema is not None: self.log.warning("Using default schema due to unexpected type. Should be a string or list.") col_type_dict = {} try: col_type_dict = {col["name"]: col["type"] for col in schema} except KeyError: self.log.warning( "Using default schema due to missing name or type. Please " "refer to:" "#specifying_a_json_schema_file" ) return col_type_dict def _write_local_schema_file(self, cursor): """ Takes a cursor, and writes the BigQuery schema for the results to a local file system. Schema for database will be read from cursor if not specified. :return: A dictionary where key is a filename to be used as an object name in GCS, and values are file handles to local files that contains the BigQuery schema fields in .json format. """ if self.schema:"Using user schema") schema = self.schema else:"Starts generating schema") schema = [ self.field_to_bigquery(field) for field in cursor.description if field[0] not in self.exclude_columns ] if isinstance(schema, list): schema = json.dumps(schema, sort_keys=True)"Using schema for %s", self.schema_filename) self.log.debug("Current schema: %s", schema) tmp_schema_file_handle = NamedTemporaryFile(delete=True) tmp_schema_file_handle.write(schema.encode("utf-8")) schema_file_to_upload = { "file_name": self.schema_filename, "file_handle": tmp_schema_file_handle, "file_mime_type": "application/json", } return schema_file_to_upload def _upload_to_gcs(self, file_to_upload): """Upload a file (data split or schema .json file) to Google Cloud Storage.""" hook = GCSHook( gcp_conn_id=self.gcp_conn_id, delegate_to=self.delegate_to, impersonation_chain=self.impersonation_chain, ) is_data_file = file_to_upload.get("file_name") != self.schema_filename metadata = None if is_data_file and self.upload_metadata: metadata = {"row_count": file_to_upload["file_row_count"]} object_name = file_to_upload.get("file_name") if is_data_file and self.partition_columns: # Add partition column values to object_name partition_values = file_to_upload.get("partition_values") head_path, tail_path = os.path.split(object_name) partition_subprefix = [ f"{col}={val}" for col, val in zip(self.partition_columns, partition_values) ] object_name = os.path.join(head_path, *partition_subprefix, tail_path) hook.upload( self.bucket, object_name, file_to_upload.get("file_handle").name, mime_type=file_to_upload.get("file_mime_type"), gzip=self.gzip if is_data_file else False, metadata=metadata,

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