airflow.providers.salesforce.hooks.salesforce¶
This module contains a Salesforce Hook which allows you to connect to your Salesforce instance, retrieve data from it, and write that data to a file for other uses.
Note
this hook also relies on the simple_salesforce package: https://github.com/simple-salesforce/simple-salesforce
Module Contents¶
-
class
airflow.providers.salesforce.hooks.salesforce.SalesforceHook(conn_id: str)[source]¶ Bases:
airflow.hooks.base.BaseHookCreate new connection to Salesforce and allows you to pull data out of SFDC and save it to a file.
You can then use that file with other Airflow operators to move the data into another data source.
- Parameters
conn_id (str) -- the name of the connection that has the parameters we need to connect to Salesforce. The connection should be type http and include a user's security token in the Extras field.
Note
For the HTTP connection type, you can include a JSON structure in the Extras field. We need a user's security token to connect to Salesforce. So we define it in the Extras field as {"security_token":"YOUR_SECURITY_TOKEN"}
For sandbox mode, add {"domain":"test"} in the Extras field
-
make_query(self, query: str, include_deleted: bool = False, query_params: Optional[dict] = None)[source]¶ Make a query to Salesforce.
-
describe_object(self, obj: str)[source]¶ Get the description of an object from Salesforce. This description is the object's schema and some extra metadata that Salesforce stores for each object.
-
get_object_from_salesforce(self, obj: str, fields: Iterable[str])[source]¶ Get all instances of the object from Salesforce. For each model, only get the fields specified in fields.
- All we really do underneath the hood is run:
SELECT <fields> FROM <obj>;
-
classmethod
_to_timestamp(cls, column: pd.Series)[source]¶ Convert a column of a dataframe to UNIX timestamps if applicable
- Parameters
column (pandas.Series) -- A Series object representing a column of a dataframe.
- Returns
a new series that maintains the same index as the original
- Return type
-
write_object_to_file(self, query_results: List[dict], filename: str, fmt: str = 'csv', coerce_to_timestamp: bool = False, record_time_added: bool = False)[source]¶ Write query results to file.
- Acceptable formats are:
- csv:
comma-separated-values file. This is the default format.
- json:
JSON array. Each element in the array is a different row.
- ndjson:
JSON array but each element is new-line delimited instead of comma delimited like in json
This requires a significant amount of cleanup. Pandas doesn't handle output to CSV and json in a uniform way. This is especially painful for datetime types. Pandas wants to write them as strings in CSV, but as millisecond Unix timestamps.
By default, this function will try and leave all values as they are represented in Salesforce. You use the coerce_to_timestamp flag to force all datetimes to become Unix timestamps (UTC). This is can be greatly beneficial as it will make all of your datetime fields look the same, and makes it easier to work with in other database environments
- Parameters
query_results (list of dict) -- the results from a SQL query
filename (str) -- the name of the file where the data should be dumped to
fmt (str) -- the format you want the output in. Default: 'csv'
coerce_to_timestamp (bool) -- True if you want all datetime fields to be converted into Unix timestamps. False if you want them to be left in the same format as they were in Salesforce. Leaving the value as False will result in datetimes being strings. Default: False
record_time_added (bool) -- True if you want to add a Unix timestamp field to the resulting data that marks when the data was fetched from Salesforce. Default: False
- Returns
the dataframe that gets written to the file.
- Return type
pandas.Dataframe
-
object_to_df(self, query_results: List[dict], coerce_to_timestamp: bool = False, record_time_added: bool = False)[source]¶ Export query results to dataframe.
By default, this function will try and leave all values as they are represented in Salesforce. You use the coerce_to_timestamp flag to force all datetimes to become Unix timestamps (UTC). This is can be greatly beneficial as it will make all of your datetime fields look the same, and makes it easier to work with in other database environments
- Parameters
query_results (list of dict) -- the results from a SQL query
coerce_to_timestamp (bool) -- True if you want all datetime fields to be converted into Unix timestamps. False if you want them to be left in the same format as they were in Salesforce. Leaving the value as False will result in datetimes being strings. Default: False
record_time_added (bool) -- True if you want to add a Unix timestamp field to the resulting data that marks when the data was fetched from Salesforce. Default: False
- Returns
the dataframe.
- Return type
pandas.Dataframe