Source code for airflow.providers.amazon.aws.transfers.mongo_to_s3
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from __future__ import annotations
import json
from typing import TYPE_CHECKING, Any, Iterable, Sequence, cast
from bson import json_util
from airflow.models import BaseOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.mongo.hooks.mongo import MongoHook
if TYPE_CHECKING:
from airflow.utils.context import Context
[docs]class MongoToS3Operator(BaseOperator):
"""Operator meant to move data from mongo via pymongo to s3 via boto.
.. seealso::
For more information on how to use this operator, take a look at the guide:
:ref:`howto/operator:MongoToS3Operator`
:param mongo_conn_id: reference to a specific mongo connection
:param aws_conn_id: reference to a specific S3 connection
:param mongo_collection: reference to a specific collection in your mongo db
:param mongo_query: query to execute. A list including a dict of the query
:param mongo_projection: optional parameter to filter the returned fields by
the query. It can be a list of fields names to include or a dictionary
for excluding fields (e.g ``projection={"_id": 0}`` )
:param s3_bucket: reference to a specific S3 bucket to store the data
:param s3_key: in which S3 key the file will be stored
:param mongo_db: reference to a specific mongo database
:param replace: whether or not to replace the file in S3 if it previously existed
:param allow_disk_use: enables writing to temporary files in the case you are handling large dataset.
This only takes effect when `mongo_query` is a list - running an aggregate pipeline
:param compression: type of compression to use for output file in S3. Currently only gzip is supported.
"""
[docs] template_fields: Sequence[str] = ("s3_bucket", "s3_key", "mongo_query", "mongo_collection")
[docs] template_fields_renderers = {"mongo_query": "json"}
def __init__(
self,
*,
mongo_conn_id: str = "mongo_default",
aws_conn_id: str = "aws_default",
mongo_collection: str,
mongo_query: list | dict,
s3_bucket: str,
s3_key: str,
mongo_db: str | None = None,
mongo_projection: list | dict | None = None,
replace: bool = False,
allow_disk_use: bool = False,
compression: str | None = None,
**kwargs,
) -> None:
super().__init__(**kwargs)
self.mongo_conn_id = mongo_conn_id
self.aws_conn_id = aws_conn_id
self.mongo_db = mongo_db
self.mongo_collection = mongo_collection
# Grab query and determine if we need to run an aggregate pipeline
self.mongo_query = mongo_query
self.is_pipeline = isinstance(self.mongo_query, list)
self.mongo_projection = mongo_projection
self.s3_bucket = s3_bucket
self.s3_key = s3_key
self.replace = replace
self.allow_disk_use = allow_disk_use
self.compression = compression
[docs] def execute(self, context: Context):
"""Is written to depend on transform method"""
s3_conn = S3Hook(self.aws_conn_id)
# Grab collection and execute query according to whether or not it is a pipeline
if self.is_pipeline:
results = MongoHook(self.mongo_conn_id).aggregate(
mongo_collection=self.mongo_collection,
aggregate_query=cast(list, self.mongo_query),
mongo_db=self.mongo_db,
allowDiskUse=self.allow_disk_use,
)
else:
results = MongoHook(self.mongo_conn_id).find(
mongo_collection=self.mongo_collection,
query=cast(dict, self.mongo_query),
projection=self.mongo_projection,
mongo_db=self.mongo_db,
)
# Performs transform then stringifies the docs results into json format
docs_str = self._stringify(self.transform(results))
s3_conn.load_string(
string_data=docs_str,
key=self.s3_key,
bucket_name=self.s3_bucket,
replace=self.replace,
compression=self.compression,
)
@staticmethod
def _stringify(iterable: Iterable, joinable: str = "\n") -> str:
"""
Takes an iterable (pymongo Cursor or Array) containing dictionaries and
returns a stringified version using python join
"""
return joinable.join([json.dumps(doc, default=json_util.default) for doc in iterable])
@staticmethod
