# -*- coding: utf-8 -*-
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import sys
import json
import time
import base64
from airflow.contrib.hooks.gcs_hook import GoogleCloudStorageHook
from airflow.hooks.mysql_hook import MySqlHook
from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from datetime import date, datetime
from decimal import Decimal
from MySQLdb.constants import FIELD_TYPE
from tempfile import NamedTemporaryFile
from six import string_types
PY3 = sys.version_info[0] == 3
[docs]class MySqlToGoogleCloudStorageOperator(BaseOperator):
"""
Copy data from MySQL to Google cloud storage in JSON format.
"""
template_fields = ('sql', 'bucket', 'filename', 'schema_filename', 'schema')
template_ext = ('.sql',)
ui_color = '#a0e08c'
@apply_defaults
def __init__(self,
sql,
bucket,
filename,
schema_filename=None,
approx_max_file_size_bytes=1900000000,
mysql_conn_id='mysql_default',
google_cloud_storage_conn_id='google_cloud_default',
schema=None,
delegate_to=None,
*args,
**kwargs):
"""
:param sql: The SQL to execute on the MySQL 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 MySQL.
: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 mysql_conn_id: Reference to a specific MySQL hook.
:type mysql_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 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
:type schema: str or list
: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.
"""
super(MySqlToGoogleCloudStorageOperator, 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.mysql_conn_id = mysql_conn_id
self.google_cloud_storage_conn_id = google_cloud_storage_conn_id
self.schema = schema
self.delegate_to = delegate_to
[docs] def execute(self, context):
cursor = self._query_mysql()
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_mysql(self):
"""
Queries mysql and returns a cursor to the results.
"""
mysql = MySqlHook(mysql_conn_id=self.mysql_conn_id)
conn = mysql.get_conn()
cursor = conn.cursor()
cursor.execute(self.sql)
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))
col_type_dict = self._get_col_type_dict()
file_no = 0
tmp_file_handle = NamedTemporaryFile(delete=True)
tmp_file_handles = {self.filename.format(file_no): tmp_file_handle}
for row in cursor:
# Convert datetime objects to utc seconds, and decimals to floats.
# Convert binary type object to string encoded with base64.
row = self._convert_types(schema, col_type_dict, row)
row_dict = dict(zip(schema, row))
# TODO validate that row isn't > 2MB. BQ enforces a hard row size of 2MB.
s = json.dumps(row_dict)
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:
file_no += 1
tmp_file_handle = NamedTemporaryFile(delete=True)
tmp_file_handles[self.filename.format(file_no)] = tmp_file_handle
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_str = None
tmp_schema_file_handle = NamedTemporaryFile(delete=True)
if self.schema is not None and isinstance(self.schema, string_types):
schema_str = self.schema
elif self.schema is not None and isinstance(self.schema, list):
schema_str = json.dumps(self.schema)
else:
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])
# Always allow TIMESTAMP to be nullable. MySQLdb returns None types
# for required fields because some MySQL timestamps can't be
# represented by Python's datetime (e.g. 0000-00-00 00:00:00).
if field[6] or field_type == 'TIMESTAMP':
field_mode = 'NULLABLE'
else:
field_mode = 'REQUIRED'
schema.append({
'name': field_name,
'type': field_type,
'mode': field_mode,
})
schema_str = json.dumps(schema)
if PY3:
schema_str = schema_str.encode('utf-8')
tmp_schema_file_handle.write(schema_str)
self.log.info('Using schema for %s: %s', self.schema_filename, schema_str)
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')
def _convert_types(self, schema, col_type_dict, row):
"""
Takes a value from MySQLdb, 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. Binary type fields are encoded with base64,
as imported BYTES data must be base64-encoded according to Bigquery SQL
date type documentation: https://cloud.google.com/bigquery/data-types
"""
converted_row = []
for col_name, col_val in zip(schema, row):
if type(col_val) in (datetime, date):
col_val = time.mktime(col_val.timetuple())
elif isinstance(col_val, Decimal):
col_val = float(col_val)
elif col_type_dict.get(col_name) == "BYTES":
col_val = base64.standard_b64encode(col_val)
if PY3:
col_val = col_val.decode('ascii')
else:
col_val = col_val
converted_row.append(col_val)
return converted_row
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, string_types):
schema = json.loads(self.schema)
elif isinstance(self.schema, list):
schema = self.schema
elif self.schema is not None:
self.log.warn('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.warn('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
[docs] @classmethod
def type_map(cls, mysql_type):
"""
Helper function that maps from MySQL fields to BigQuery fields. Used
when a schema_filename is set.
"""
d = {
FIELD_TYPE.INT24: 'INTEGER',
FIELD_TYPE.TINY: 'INTEGER',
FIELD_TYPE.BIT: 'INTEGER',
FIELD_TYPE.DATETIME: 'TIMESTAMP',
FIELD_TYPE.DATE: 'TIMESTAMP',
FIELD_TYPE.DECIMAL: 'FLOAT',
FIELD_TYPE.NEWDECIMAL: 'FLOAT',
FIELD_TYPE.DOUBLE: 'FLOAT',
FIELD_TYPE.FLOAT: 'FLOAT',
FIELD_TYPE.LONG: 'INTEGER',
FIELD_TYPE.LONGLONG: 'INTEGER',
FIELD_TYPE.SHORT: 'INTEGER',
FIELD_TYPE.TIMESTAMP: 'TIMESTAMP',
FIELD_TYPE.YEAR: 'INTEGER',
}
return d[mysql_type] if mysql_type in d else 'STRING'