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"""Base operator for SQL to GCS operators."""
from __future__ import annotations
import abc
import csv
import json
import os
from tempfile import NamedTemporaryFile
from typing import TYPE_CHECKING, Any, Sequence
import pyarrow as pa
import pyarrow.parquet as pq
from airflow.models import BaseOperator
from airflow.providers.google.cloud.hooks.gcs import GCSHook
if TYPE_CHECKING:
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 https://cloud.google.com/storage/quotas
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: https://cloud.google.com/bigquery/docs
/schemas#specifying_a_json_schema_file
:param gcp_conn_id: (Optional) The connection ID used to connect to Google Cloud.
: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).
:param write_on_empty: Optional parameter to specify whether to write a file if the
export does not return any rows. Default is False so we will not write a file
if the export returns no rows.
:param parquet_row_group_size: The approximate number of rows in each row group
when using parquet format. Using a large row group size can reduce the file size
and improve the performance of reading the data, but it needs more memory to
execute the operator. (default: 100000)
"""
[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"}
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",
impersonation_chain: str | Sequence[str] | None = None,
upload_metadata: bool = False,
exclude_columns: set | None = None,
partition_columns: list | None = None,
write_on_empty: bool = False,
parquet_row_group_size: int = 100000,
**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.impersonation_chain = impersonation_chain
self.upload_metadata = upload_metadata
self.exclude_columns = exclude_columns
self.partition_columns = partition_columns
self.write_on_empty = write_on_empty
self.parquet_row_group_size = parquet_row_group_size
[docs] def execute(self, context: Context):
if self.partition_columns:
self.log.info(
f"Found partition columns: {','.join(self.partition_columns)}. "
"Assuming the SQL statement is properly sorted by these columns in "
"ascending or descending order."
)
self.log.info("Executing query")
cursor = self.query()
# If a schema is set, create a BQ schema JSON file.
if self.schema_filename:
self.log.info("Writing local schema file")
schema_file = self._write_local_schema_file(cursor)
# Flush file before uploading
schema_file["file_handle"].flush()
self.log.info("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
self.log.info("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()
self.log.info("Uploading chunk file #%d to GCS.", counter)
self._upload_to_gcs(file_to_upload)
self.log.info("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)
]
@staticmethod
def _write_rows_to_parquet(parquet_writer: pq.ParquetWriter, rows):
rows_pydic: dict[str, list[Any]] = {col: [] for col in parquet_writer.schema.names}
for row in rows:
for cell, col in zip(row, parquet_writer.schema.names):
rows_pydic[col].append(cell)
tbl = pa.Table.from_pydict(rows_pydic, parquet_writer.schema)
parquet_writer.write_table(tbl)
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 = [schema_tuple[0] for schema_tuple in 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)
rows_buffer = []
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":
# Write out the remaining rows in the buffer
if rows_buffer:
self._write_rows_to_parquet(parquet_writer, rows_buffer)
rows_buffer = []
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 or 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 or self.null_marker for value in row]
rows_buffer.append(row)
if len(rows_buffer) >= self.parquet_row_group_size:
self._write_rows_to_parquet(parquet_writer, rows_buffer)
rows_buffer = []
else:
row = self.convert_types(schema, col_type_dict, row)
row_dict = dict(zip(schema, row))
json.dump(row_dict, tmp_file_handle, sort_keys=True, ensure_ascii=False)
# Append newline to make dumps BigQuery compatible.
tmp_file_handle.write("\n")
# Stop if the file exceeds the file size limit.
fppos = tmp_file_handle.tell()
tmp_file_handle.seek(0, os.SEEK_END)
file_size = tmp_file_handle.tell()
tmp_file_handle.seek(fppos, os.SEEK_SET)
if file_size >= self.approx_max_file_size_bytes:
file_no += 1
if self.export_format == "parquet":
# Write out the remaining rows in the buffer
if rows_buffer:
self._write_rows_to_parquet(parquet_writer, rows_buffer)
rows_buffer = []
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":
# Write out the remaining rows in the buffer
if rows_buffer:
self._write_rows_to_parquet(parquet_writer, rows_buffer)
rows_buffer = []
parquet_writer.close()
# Last file may have 0 rows, don't yield if empty
# However, if it is the first file and self.write_on_empty is True, then yield to write an empty file
if file_to_upload["file_row_count"] > 0 or (file_no == 0 and self.write_on_empty):
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(mode="w", encoding="utf-8", 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, delimiter=self.field_delimiter)
csv_writer.writerow(schema)
return csv_writer
def _configure_parquet_file(self, file_handle, parquet_schema) -> pq.ParquetWriter:
parquet_writer = pq.ParquetWriter(file_handle.name, 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."""
@abc.abstractmethod
[docs] def field_to_bigquery(self, field) -> dict[str, str]:
"""Convert a DBAPI field to BigQuery schema format."""
@abc.abstractmethod
[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: https://cloud.google.com/bigquery/docs/schemas"
"#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:
self.log.info("Using user schema")
schema = self.schema
else:
self.log.info("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)
self.log.info("Using schema for %s", self.schema_filename)
self.log.debug("Current schema: %s", schema)
tmp_schema_file_handle = NamedTemporaryFile(mode="w", encoding="utf-8", delete=True)
tmp_schema_file_handle.write(schema)
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,
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,
)
