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        "author": "Sumner Magruder",
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        "description": "# About\nParPar (Parallel Parser) is a light tool which makes it easy to distribute a function across a large file.\n\nParPar is meant to work on serialized data where some values are highly repeated across records for a given field. e.g.\n\n```\na 1 1\na 1 2\na 2 3\na 2 4\na 3 5\na 3 6\nb 1 7\nb 1 8\nb 2 9\nb 2 10\nb 3 11\nb 3 12\n```\n\nalthough we have 12 records, for column 1 there are only 2 unique values. Likewise, for column 2 there are only 3 unique values. We could break this file up into smaller files under a directory:\n\n```\n<out-dir>/<col-1-value>/<col-2-value>\n```\nor vis versa.\n\n# How to use.\n\n1. Start by importing the ParPar class:\n\n```python\nfrom parpar import ParPar\n```\n\n2. Initialize an instance:\n\n```python\nppf = ParPar()\n```\n\n3. Shard a large file into sub-files:\n\n```python\n# shard by columns\nppf.shard(\n  <input-file>, <output-directory>,\n  <columns>, <delim>, <newline>\n)\n\n# shard by lines\nppf.shard_by_lines(\n  <input-file>, <output-directory>,\n  <number_of_lines>, <delim>, <newline>\n)\n```\n\n4. Check to make sure the number of records are the same:\n\n```python\nfiles = ppf.shard_files(<output-directory>)\nrecords = ppf.sharded_records(files)\n\nfrom parpar.utils import filelines\n\nprint(records == filelines(<input-file>))\n```\n\n\n5. Map an arbitrary function across each _line_ of all shared files:\n\n```python\ndef foo(line, *args, **kwargs):\n    pass\n\nargs = [1, 2, 3]\nkwargs = {'a': 'x', 'b': 'y'}\n\nppf.shard_line_apply(<output-directory>,\n  foo, args, kwargs,\n  processes=<number-of-processes>\n)\n```\n\n\n6. Map an arbitrary function across all shared files:\n\n```python\ndef bar(file, *args, **kwargs):\n    pass\n\nargs = [1, 2, 3]\nkwargs = {'a': 'x', 'b': 'y'}\n\nppf.shard_file_apply(<output-directory>,\n  bar, args, kwargs,\n  processes=<number-of-processes>\n)\n```\n\n\n",
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