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    "info": {
        "author": "Milan Lukac",
        "author_email": "milan.lukac@kiwi.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Environment :: Plugins",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7"
        ],
        "description": "# pg2avro\n\nPostgres to Avro generator.\n\n## Features\n\n- Generate Avro schema from column definition.\n- Generate  data format consumable for Avro serialization.\n\n# Usage\n\n## Generating schema\n\nMethod: `pg2avro.get_avro_schema`\n\n```\nget_avro_schema(\n    \"mytable\", \n    \"public\", \n    [\n        # Dictionary mode\n        {\n            \"name\": \"column_name_1\",\n            \"type\": \"int2\",\n            \"nullable\": False,\n        },\n        # SqlAlchemy mode\n        SqlAlchemyColumn(ARRAY(TEXT), name=\"column_name_2\"),\n        ...\n    ]\n)\n\n```\n\nSchema generator needs the following information:\n- table name\n- namespace (`schema` in SQL, `dataset` in Big Query etc.)\n- columns - iterable of columns, each element with:\n    - name\n    - type - `_` prefix is used to indicate array types\n    - nullable (optional, `True` assumed if not provided)\n- column mapping - optional `ColumnMapping` object with column mappings (see below for more info).\n\nColumn data can be passed in multiple formats.\n\n### Supported column formats\n\n- Dictionary with required keys and data\n- SqlAlchemy Column object\n- Any object with compatible attributes and required data\n- Dictionary or object with required data, but without compatible attributes/keys, supplied with ColumnMapping.\n\nNote: this mode supports **generating schema from raw postgres data** - `udt_name` can be used to generate the schema.\n```\ncolumns = [\n    CustomColumn(name=\"column_name\", udt_name=\"int2\", is_nullable=False),\n]\n\nget_avro_schema(\n    table_name,\n    namespace,\n    columns,\n    ColumnMapping(name=\"name\", type=\"udt_name\", nullable=\"is_nullable\"),\n)\n```\n\n## Generating rows data\n\nMethod: `pg2avro.get_avro_row_dict`\n\nThis method requires rows data and schema to generate the rows with.\n\n### Supported row formats\n\n- Dictionary with keys corresponding to schema field names\n- Object with keys corresponding to schema field names (works the same as dictionary with corresponding fields)\n- Tuple with data in the same order as fields specified in schema\n\n```\ncolumns = [\n    {\"name\": \"name\", \"type\": \"varchar\", \"nullable\": False},\n    {\"name\": \"number\", \"type\": \"float4\", \"float4\", \"nullable\": False},\n]\nschema = get_avro_schema(table_name, namespace, columns)\nrows = [\n    {\"name\": \"John\", \"number\": 1.0},\n    RowObject(name=\"Jack\", number=2.0),\n    (\"Jim\", 3.0),\n]\ndata = [get_avro_row_dict(row, schema) for row in rows]\n\n```\n\n",
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        "license": "MIT",
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        "name": "pg2avro",
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        "summary": "Utility generating avro files from postgres.",
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