Metadata-Version: 2.1
Name: dataops-salesforce-bulk
Version: 0.0.4
Summary: Python interface to the Salesforce.com Bulk API.
Home-page: https://github.com/puppetlabs/salesforce-bulk
Author: Scott Persinger
Author-email: scottp@heroku.com
License: MIT
Description: DataOps Salesforce Bulk
        ===============
        **This library was forked from the salesforce-bulk library. It adds a feature
        for dealing with pk chunking from Salesforce. Author credit is given to
        the author of the original salesforce-bulk library (https://pypi.org/project/salesforce-bulk/)**
        
        Python client library for accessing the asynchronous Salesforce.com Bulk
        API.
        
        Installation
        ------------
        
        ```
        pip install dataops-salesforce-bulk
        ```
        
        Authentication
        --------------
        
        To access the Bulk API you need to authenticate a user into Salesforce.
        The easiest way to do this is just to supply ``username``, ``password``
        and ``security_token``. This library will use the ``simple-salesforce``
        package to handle password based authentication.
        
        ::
        
            from salesforce_bulk import SalesforceBulk
        
            bulk = SalesforceBulk(username=username, password=password, security_token=security_token)
            ...
        
        Alternatively if you run have access to a session ID and instance\_url
        you can use those directly:
        
        ::
        
            from urlparse import urlparse
            from salesforce_bulk import SalesforceBulk
        
            bulk = SalesforceBulk(sessionId=sessionId, host=urlparse(instance_url).hostname)
            ...
        
        Operations
        ----------
        
        The basic sequence for driving the Bulk API is:
        
        1. Create a new job
        2. Add one or more batches to the job
        3. Close the job
        4. Wait for each batch to finish
        
        Bulk Query
        ----------
        
        ``bulk.create_query_job(object_name, contentType='JSON')``
        
        Using API v39.0 or higher, you can also use the queryAll operation:
        
        ``bulk.create_queryall_job(object_name, contentType='JSON')``
        
        Example
        
        ::
        
            from salesforce_bulk.util import IteratorBytesIO
            import json
            job = bulk.create_query_job("Contact", contentType='JSON')
            batch = bulk.query(job, "select Id,LastName from Contact")
            bulk.close_job(job)
            while not bulk.is_batch_done(batch):
                sleep(10)
        
            for result in bulk.get_all_results_for_query_batch(batch):
                result = json.load(IteratorBytesIO(result))
                for row in result:
                    print row # dictionary rows
        
        Same example but for CSV:
        
        ::
        
            import unicodecsv
            job = bulk.create_query_job("Contact", contentType='CSV')
            batch = bulk.query(job, "select Id,LastName from Contact")
            bulk.close_job(job)
            while not bulk.is_batch_done(batch):
                sleep(10)
        
            for result in bulk.get_all_results_for_query_batch(batch):
                reader = unicodecsv.DictReader(result, encoding='utf-8')
                for row in reader:
                    print row # dictionary rows
        
        Note that while CSV is the default for historical reasons, JSON should
        be prefered since CSV has some drawbacks including its handling of NULL
        vs empty string.
        
        PK Chunk Header
        ^^^^^^^^^^^^^^^
        
        If you are querying a large number of records you probably want to turn on `PK Chunking
        <https://developer.salesforce.com/docs/atlas.en-us.api_asynch.meta/api_asynch/async_api_headers_enable_pk_chunking.htm>`_:
        
        ``bulk.create_query_job(object_name, contentType='CSV', pk_chunking=True)``
        
        That will use the default setting for chunk size. You can use a different chunk size by providing a
        number of records per chunk:
        
        ``bulk.create_query_job(object_name, contentType='CSV', pk_chunking=100000)``
        
        Additionally if you want to do something more sophisticated you can provide a header value:
        
        ``bulk.create_query_job(object_name, contentType='CSV', pk_chunking='chunkSize=50000; startRow=00130000000xEftMGH')``
        
        Bulk Insert, Update, Delete
        ---------------------------
        
        All Bulk upload operations work the same. You set the operation when you
        create the job. Then you submit one or more documents that specify
        records with columns to insert/update/delete. When deleting you should
        only submit the Id for each record.
        
        For efficiency you should use the ``post_batch`` method to post each
        batch of data. (Note that a batch can have a maximum 10,000 records and
        be 1GB in size.) You pass a generator or iterator into this function and
        it will stream data via POST to Salesforce. For help sending CSV
        formatted data you can use the salesforce\_bulk.CsvDictsAdapter class.
        It takes an iterator returning dictionaries and returns an iterator
        which produces CSV data.
        
        Full example:
        
        ::
        
            from salesforce_bulk import CsvDictsAdapter
        
            job = bulk.create_insert_job("Account", contentType='CSV')
            accounts = [dict(Name="Account%d" % idx) for idx in xrange(5)]
            csv_iter = CsvDictsAdapter(iter(accounts))
            batch = bulk.post_batch(job, csv_iter)
            bulk.wait_for_batch(job, batch)
            bulk.close_job(job)
            print "Done. Accounts uploaded."
        
        Concurrency mode
        ^^^^^^^^^^^^^^^^
        
        When creating the job, pass ``concurrency='Serial'`` or
        ``concurrency='Parallel'`` to set the concurrency mode for the job.
        
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