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    "info": {
        "author": "Scott Persinger",
        "author_email": "scottp@heroku.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
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        "description": ".. figure:: https://travis-ci.org/heroku/salesforce-bulk.svg?branch=master\n   :alt: travis-badge\n\nSalesforce Bulk\n===============\n\nPython client library for accessing the asynchronous Salesforce.com Bulk\nAPI.\n\nInstallation\n------------\n\n``pip install salesforce-bulk``\n\nAuthentication\n--------------\n\nTo access the Bulk API you need to authenticate a user into Salesforce.\nThe easiest way to do this is just to supply ``username``, ``password``\nand ``security_token``. This library will use the ``simple-salesforce``\npackage to handle password based authentication.\n\n::\n\n    from salesforce_bulk import SalesforceBulk\n\n    bulk = SalesforceBulk(username=username, password=password, security_token=security_token)\n    ...\n\nAlternatively if you run have access to a session ID and instance\\_url\nyou can use those directly:\n\n::\n\n    from urlparse import urlparse\n    from salesforce_bulk import SalesforceBulk\n\n    bulk = SalesforceBulk(sessionId=sessionId, host=urlparse(instance_url).hostname)\n    ...\n\nOperations\n----------\n\nThe basic sequence for driving the Bulk API is:\n\n1. Create a new job\n2. Add one or more batches to the job\n3. Close the job\n4. Wait for each batch to finish\n\nBulk Query\n----------\n\n``bulk.create_query_job(object_name, contentType='JSON')``\n\nUsing API v39.0 or higher, you can also use the queryAll operation:\n\n``bulk.create_queryall_job(object_name, contentType='JSON')``\n\nExample\n\n::\n\n    from salesforce_bulk.util import IteratorBytesIO\n    import json\n    job = bulk.create_query_job(\"Contact\", contentType='JSON')\n    batch = bulk.query(job, \"select Id,LastName from Contact\")\n    bulk.close_job(job)\n    while not bulk.is_batch_done(batch):\n        sleep(10)\n\n    for result in bulk.get_all_results_for_query_batch(batch):\n        result = json.load(IteratorBytesIO(result))\n        for row in result:\n            print row # dictionary rows\n\nSame example but for CSV:\n\n::\n\n    import unicodecsv\n    job = bulk.create_query_job(\"Contact\", contentType='CSV')\n    batch = bulk.query(job, \"select Id,LastName from Contact\")\n    bulk.close_job(job)\n    while not bulk.is_batch_done(batch):\n        sleep(10)\n\n    for result in bulk.get_all_results_for_query_batch(batch):\n        reader = unicodecsv.DictReader(result, encoding='utf-8')\n        for row in reader:\n            print row # dictionary rows\n\nNote that while CSV is the default for historical reasons, JSON should\nbe prefered since CSV has some drawbacks including its handling of NULL\nvs empty string.\n\nPK Chunk Header\n^^^^^^^^^^^^^^^\n\nIf you are querying a large number of records you probably want to turn on `PK Chunking\n<https://developer.salesforce.com/docs/atlas.en-us.api_asynch.meta/api_asynch/async_api_headers_enable_pk_chunking.htm>`_:\n\n``bulk.create_query_job(object_name, contentType='CSV', pk_chunking=True)``\n\nThat will use the default setting for chunk size. You can use a different chunk size by providing a\nnumber of records per chunk:\n\n``bulk.create_query_job(object_name, contentType='CSV', pk_chunking=100000)``\n\nAdditionally if you want to do something more sophisticated you can provide a header value:\n\n``bulk.create_query_job(object_name, contentType='CSV', pk_chunking='chunkSize=50000; startRow=00130000000xEftMGH')``\n\nBulk Insert, Update, Delete\n---------------------------\n\nAll Bulk upload operations work the same. You set the operation when you\ncreate the job. Then you submit one or more documents that specify\nrecords with columns to insert/update/delete. When deleting you should\nonly submit the Id for each record.\n\nFor efficiency you should use the ``post_batch`` method to post each\nbatch of data. (Note that a batch can have a maximum 10,000 records and\nbe 1GB in size.) You pass a generator or iterator into this function and\nit will stream data via POST to Salesforce. For help sending CSV\nformatted data you can use the salesforce\\_bulk.CsvDictsAdapter class.\nIt takes an iterator returning dictionaries and returns an iterator\nwhich produces CSV data.\n\nFull example:\n\n::\n\n    from salesforce_bulk import CsvDictsAdapter\n\n    job = bulk.create_insert_job(\"Account\", contentType='CSV')\n    accounts = [dict(Name=\"Account%d\" % idx) for idx in xrange(5)]\n    csv_iter = CsvDictsAdapter(iter(accounts))\n    batch = bulk.post_batch(job, csv_iter)\n    bulk.wait_for_batch(job, batch)\n    bulk.close_job(job)\n    print \"Done. Accounts uploaded.\"\n\nConcurrency mode\n^^^^^^^^^^^^^^^^\n\nWhen creating the job, pass ``concurrency='Serial'`` or\n``concurrency='Parallel'`` to set the concurrency mode for the job.\n\n\n",
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