Source code for airflow.contrib.hooks.salesforce_hook

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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:
import json
import logging
import time

from simple_salesforce import Salesforce
from airflow.hooks.base_hook import BaseHook

import pandas as pd

[docs]class SalesforceHook(BaseHook): def __init__( self, conn_id, *args, **kwargs ): """ Create 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 :param conn_id: 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"}` """ self.conn_id = conn_id self._args = args self._kwargs = kwargs # get the connection parameters self.connection = self.get_connection(conn_id) self.extras = self.connection.extra_dejson
[docs] def sign_in(self): """ Sign into Salesforce. If we have already signed it, this will just return the original object """ if hasattr(self, 'sf'): return self.sf # connect to Salesforce sf = Salesforce( username=self.connection.login, password=self.connection.password, security_token=self.extras['security_token'],, sandbox=self.extras.get('sandbox', False) ) self.sf = sf return sf
[docs] def make_query(self, query): """ Make a query to Salesforce. Returns result in dictionary :param query: The query to make to Salesforce """ self.sign_in()"Querying for all objects") query = self.sf.query_all(query) "Received results: Total size: %s; Done: %s", query['totalSize'], query['done'] ) query = json.loads(json.dumps(query)) return query
[docs] def describe_object(self, obj): """ 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 :param obj: Name of the Salesforce object that we are getting a description of. """ self.sign_in() return json.loads(json.dumps(self.sf.__getattr__(obj).describe()))
[docs] def get_available_fields(self, obj): """ Get a list of all available fields for an object. This only returns the names of the fields. """ self.sign_in() desc = self.describe_object(obj) return [f['name'] for f in desc['fields']]
[docs] def _build_field_list(fields): # join all of the fields in a comma separated list return ",".join(fields)
[docs] def get_object_from_salesforce(self, obj, fields): """ 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>; """ field_string = self._build_field_list(fields) query = "SELECT {0} FROM {1}".format(field_string, obj) "Making query to Salesforce: %s", query if len(query) < 30 else " ... ".join([query[:15], query[-15:]]) ) return self.make_query(query)
[docs] def _to_timestamp(cls, col): """ Convert a column of a dataframe to UNIX timestamps if applicable :param col: A Series object representing a column of a dataframe. """ # try and convert the column to datetimes # the column MUST have a four digit year somewhere in the string # there should be a better way to do this, # but just letting pandas try and convert every column without a format # caused it to convert floats as well # For example, a column of integers # between 0 and 10 are turned into timestamps # if the column cannot be converted, # just return the original column untouched try: col = pd.to_datetime(col) except ValueError: log.warning( "Could not convert field to timestamps: %s", ) return col # now convert the newly created datetimes into timestamps # we have to be careful here # because NaT cannot be converted to a timestamp # so we have to return NaN converted = [] for i in col: try: converted.append(i.timestamp()) except ValueError: converted.append( except AttributeError: converted.append( # return a new series that maintains the same index as the original return pd.Series(converted, index=col.index)
[docs] def write_object_to_file( self, query_results, filename, fmt="csv", coerce_to_timestamp=False, record_time_added=False ): """ 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 :param query_results: the results from a SQL query :param filename: the name of the file where the data should be dumped to :param fmt: the format you want the output in. *Default:* csv. :param coerce_to_timestamp: 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. *Defaults to False* :param record_time_added: *(optional)* 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*. """ fmt = fmt.lower() if fmt not in ['csv', 'json', 'ndjson']: raise ValueError("Format value is not recognized: {0}".format(fmt)) # this line right here will convert all integers to floats if there are # any None/np.nan values in the column # that's because None/np.nan cannot exist in an integer column # we should write all of our timestamps as FLOATS in our final schema df = pd.DataFrame.from_records(query_results, exclude=["attributes"]) df.columns = [c.lower() for c in df.columns] # convert columns with datetime strings to datetimes # not all strings will be datetimes, so we ignore any errors that occur # we get the object's definition at this point and only consider # features that are DATE or DATETIME if coerce_to_timestamp and df.shape[0] > 0: # get the object name out of the query results # it's stored in the "attributes" dictionary # for each returned record object_name = query_results[0]['attributes']['type']"Coercing timestamps for: %s", object_name) schema = self.describe_object(object_name) # possible columns that can be converted to timestamps # are the ones that are either date or datetime types # strings are too general and we risk unintentional conversion possible_timestamp_cols = [ i['name'].lower() for i in schema['fields'] if i['type'] in ["date", "datetime"] and i['name'].lower() in df.columns ] df[possible_timestamp_cols] = df[possible_timestamp_cols].apply( lambda x: self._to_timestamp(x) ) if record_time_added: fetched_time = time.time() df["time_fetched_from_salesforce"] = fetched_time # write the CSV or JSON file depending on the option # NOTE: # datetimes here are an issue. # There is no good way to manage the difference # for to_json, the options are an epoch or a ISO string # but for to_csv, it will be a string output by datetime # For JSON we decided to output the epoch timestamp in seconds # (as is fairly standard for JavaScript) # And for csv, we do a string if fmt == "csv": # there are also a ton of newline objects # that mess up our ability to write to csv # we remove these newlines so that the output is a valid CSV format"Cleaning data and writing to CSV") possible_strings = df.columns[df.dtypes == "object"] df[possible_strings] = df[possible_strings].apply( lambda x: x.str.replace("\r\n", "") ) df[possible_strings] = df[possible_strings].apply( lambda x: x.str.replace("\n", "") ) # write the dataframe df.to_csv(filename, index=False) elif fmt == "json": df.to_json(filename, "records", date_unit="s") elif fmt == "ndjson": df.to_json(filename, "records", lines=True, date_unit="s") return df

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