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import sys
import json
import time
import datetime
from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
from airflow.hooks.postgres_hook import PostgresHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from decimal import Decimal
from tempfile import NamedTemporaryFile
PY3 = sys.version_info[0] == 3
[docs]class PostgresToGoogleCloudStorageOperator(BaseOperator):
"""
Copy data from Postgres to Google Cloud Storage in JSON format.
"""
template_fields = ('sql', 'bucket', 'filename', 'schema_filename',
'parameters')
template_ext = ('.sql', )
ui_color = '#a0e08c'
@apply_defaults
def __init__(self,
sql,
bucket,
filename,
schema_filename=None,
approx_max_file_size_bytes=1900000000,
postgres_conn_id='postgres_default',
google_cloud_storage_conn_id='google_cloud_default',
delegate_to=None,
parameters=None,
*args,
**kwargs):
"""
:param sql: The SQL to execute on the Postgres table.
:type sql: string
:param bucket: The bucket to upload to.
:type bucket: string
: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.
:type filename: string
: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 Postgres.
:type schema_filename: string
:param approx_max_file_size_bytes: This operator supports the ability
to split large table dumps into multiple files (see notes in the
filenamed param docs above). Google Cloud Storage allows for files
to be a maximum of 4GB. This param allows developers to specify the
file size of the splits.
:type approx_max_file_size_bytes: long
:param postgres_conn_id: Reference to a specific Postgres hook.
:type postgres_conn_id: string
:param google_cloud_storage_conn_id: Reference to a specific Google
cloud storage hook.
:type google_cloud_storage_conn_id: string
:param delegate_to: The account to impersonate, 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.
:type parameters: dict
"""
super(PostgresToGoogleCloudStorageOperator, self).__init__(*args, **kwargs)
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.postgres_conn_id = postgres_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.delegate_to = delegate_to
self.parameters = parameters
def execute(self, context):
cursor = self._query_postgres()
files_to_upload = self._write_local_data_files(cursor)
# If a schema is set, create a BQ schema JSON file.
if self.schema_filename:
files_to_upload.update(self._write_local_schema_file(cursor))
# Flush all files before uploading
for file_handle in files_to_upload.values():
file_handle.flush()
self._upload_to_gcs(files_to_upload)
# Close all temp file handles.
for file_handle in files_to_upload.values():
file_handle.close()
def _query_postgres(self):
"""
Queries Postgres and returns a cursor to the results.
"""
postgres = PostgresHook(postgres_conn_id=self.postgres_conn_id)
conn = postgres.get_conn()
cursor = conn.cursor()
cursor.execute(self.sql, self.parameters)
return cursor
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.
"""
schema = list(map(lambda schema_tuple: schema_tuple[0], cursor.description))
tmp_file_handles = {}
row_no = 0
def _create_new_file():
handle = NamedTemporaryFile(delete=True)
filename = self.filename.format(len(tmp_file_handles))
tmp_file_handles[filename] = handle
return handle
# Don't create a file if there is nothing to write
if cursor.rowcount > 0:
tmp_file_handle = _create_new_file()
for row in cursor:
# Convert datetime objects to utc seconds, and decimals to floats
row = map(self.convert_types, row)
row_dict = dict(zip(schema, row))
s = json.dumps(row_dict, sort_keys=True)
if PY3:
s = s.encode('utf-8')
tmp_file_handle.write(s)
# Append newline to make dumps BigQuery compatible.
tmp_file_handle.write(b'\n')
# Stop if the file exceeds the file size limit.
if tmp_file_handle.tell() >= self.approx_max_file_size_bytes:
tmp_file_handle = _create_new_file()
row_no += 1
self.log.info('Received %s rows over %s files', row_no, len(tmp_file_handles))
return tmp_file_handles
def _write_local_schema_file(self, cursor):
"""
Takes a cursor, and writes the BigQuery schema for the results to a
local file system.
: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.
"""
schema = []
for field in cursor.description:
# See PEP 249 for details about the description tuple.
field_name = field[0]
field_type = self.type_map(field[1])
field_mode = 'REPEATED' if field[1] in (1009, 1005, 1007,
1016) else 'NULLABLE'
schema.append({
'name': field_name,
'type': field_type,
'mode': field_mode,
})
self.log.info('Using schema for %s: %s', self.schema_filename, schema)
tmp_schema_file_handle = NamedTemporaryFile(delete=True)
s = json.dumps(schema, sort_keys=True)
if PY3:
s = s.encode('utf-8')
tmp_schema_file_handle.write(s)
return {self.schema_filename: tmp_schema_file_handle}
def _upload_to_gcs(self, files_to_upload):
"""
Upload all of the file splits (and optionally the schema .json file) to
Google Cloud Storage.
"""
hook = GoogleCloudStorageHook(
google_cloud_storage_conn_id=self.google_cloud_storage_conn_id,
delegate_to=self.delegate_to)
for object, tmp_file_handle in files_to_upload.items():
hook.upload(self.bucket, object, tmp_file_handle.name,
'application/json')
[docs] @classmethod
def convert_types(cls, value):
"""
Takes a value from Postgres, and converts it to a value that's safe for
JSON/Google Cloud Storage/BigQuery. Dates are converted to UTC seconds.
Decimals are converted to floats. Times are converted to seconds.
"""
if type(value) in (datetime.datetime, datetime.date):
return time.mktime(value.timetuple())
elif type(value) == datetime.time:
formated_time = time.strptime(str(value), "%H:%M:%S")
return datetime.timedelta(
hours=formated_time.tm_hour,
minutes=formated_time.tm_min,
seconds=formated_time.tm_sec).seconds
elif isinstance(value, Decimal):
return float(value)
else:
return value
[docs] @classmethod
def type_map(cls, postgres_type):
"""
Helper function that maps from Postgres fields to BigQuery fields. Used
when a schema_filename is set.
"""
d = {
1114: 'TIMESTAMP',
1184: 'TIMESTAMP',
1082: 'TIMESTAMP',
1083: 'TIMESTAMP',
1005: 'INTEGER',
1007: 'INTEGER',
1016: 'INTEGER',
20: 'INTEGER',
21: 'INTEGER',
23: 'INTEGER',
16: 'BOOLEAN',
700: 'FLOAT',
701: 'FLOAT',
1700: 'FLOAT'
}
return d[postgres_type] if postgres_type in d else 'STRING'