Metadata-Version: 1.2
Name: bigquery-schema-generator
Version: 0.3.2
Summary: BigQuery schema generator
Home-page: https://github.com/bxparks/bigquery-schema-generator
Author: Brian T. Park
Author-email: brian@xparks.net
License: Apache 2.0
Description: BigQuery Schema Generator
        =========================
        
        This script generates the BigQuery schema from the newline-delimited
        JSON data records on the STDIN. The BigQuery data importer (``bq load``)
        uses only the first 100 lines when the schema auto-detection feature is
        enabled. In contrast, this script uses all data records to generate the
        schema.
        
        Usage:
        
        ::
        
            $ generate-schema < file.data.json > file.schema.json
        
        Version: 0.3.2 (2019-02-24)
        
        Background
        ----------
        
        Data can be imported into
        `BigQuery <https://cloud.google.com/bigquery/>`__ using the
        `bq <https://cloud.google.com/bigquery/bq-command-line-tool>`__ command
        line tool. It accepts a number of data formats including CSV or
        newline-delimited JSON. The data can be loaded into an existing table or
        a new table can be created during the loading process. The structure of
        the table is defined by its
        `schema <https://cloud.google.com/bigquery/docs/schemas>`__. The table's
        schema can be defined manually or the schema can be
        `auto-detected <https://cloud.google.com/bigquery/docs/schema-detect#auto-detect>`__.
        
        When the auto-detect feature is used, the BigQuery data importer
        examines only the first 100 records of the input data. In many cases,
        this is sufficient because the data records were dumped from another
        database and the exact schema of the source table was known. However,
        for data extracted from a service (e.g. using a REST API) the record
        fields could have been organically added at later dates. In this case,
        the first 100 records do not contain fields which are present in later
        records. The **bq load** auto-detection fails and the data fails to
        load.
        
        The **bq load** tool does not support the ability to process the entire
        dataset to determine a more accurate schema. This script fills in that
        gap. It processes the entire dataset given in the STDIN and outputs the
        BigQuery schema in JSON format on the STDOUT. This schema file can be
        fed back into the **bq load** tool to create a table that is more
        compatible with the data fields in the input dataset.
        
        Installation
        ------------
        
        Install from `PyPI <https://pypi.python.org/pypi>`__ repository using
        ``pip3``. If you want to install the package for your entire system
        globally, use
        
        ::
        
            $ sudo -H pip3 install bigquery_schema_generator
        
        If you are using a virtual environment (such as
        `venv <https://docs.python.org/3/library/venv.html>`__), then you don't
        need the ``sudo`` coommand, and you can just type:
        
        ::
        
            $ pip3 install bigquery_schema_generator
        
        A successful install should print out the following:
        
        ::
        
            Collecting bigquery-schema-generator
            Installing collected packages: bigquery-schema-generator
            Successfully installed bigquery-schema-generator-0.1.4
        
        The shell script ``generate-schema`` is installed in the same directory
        as ``pip3``.
        
        Ubuntu Linux
        ~~~~~~~~~~~~
        
        Under Ubuntu Linux, you should find the ``generate-schema`` script at
        ``/usr/local/bin/generate-schema``.
        
        MacOS
        ~~~~~
        
        If you installed Python from `Python Releases for Mac OS
        X <https://www.python.org/downloads/mac-osx/>`__, then
        ``/usr/local/bin/pip3`` is a symlink to
        ``/Library/Frameworks/Python.framework/Versions/3.6/bin/pip3``. So
        ``generate-schema`` is installed at
        ``/Library/Frameworks/Python.framework/Versions/3.6/bin/generate-schema``.
        
        The Python installer updates ``$HOME/.bash_profile`` to add
        ``/Library/Frameworks/Python.framework/Versions/3.6/bin`` to the
        ``$PATH`` environment variable. So you should be able to run the
        ``generate-schema`` command without typing in the full path.
        
        Usage
        -----
        
        The ``generate_schema.py`` script accepts a newline-delimited JSON data
        file on the STDIN. (CSV is not supported currently.) It scans every
        record in the input data file to deduce the table's schema. It prints
        the JSON formatted schema file on the STDOUT. There are at least 3 ways
        to run this script:
        
        **1) Shell script**
        
        If you installed using ``pip3``, then it should have installed a small
        helper script named ``generate-schema`` in your local ``./bin``
        directory of your current environment (depending on whether you are
        using a virtual environment).
        
        ::
        
            $ generate-schema < file.data.json > file.schema.json
        
        **2) Python module**
        
        You can invoke the module directly using:
        
        ::
        
            $ python3 -m bigquery_schema_generator.generate_schema < file.data.json > file.schema.json
        
        This is essentially what the ``generate-schema`` command does.
        
        **3) Python script**
        
        If you retrieved this code from its `GitHub
        repository <https://github.com/bxparks/bigquery-schema-generator>`__,
        then you can invoke the Python script directly:
        
        ::
        
            $ ./generate_schema.py < file.data.json > file.schema.json
        
        Schema Output
        ~~~~~~~~~~~~~
        
        The resulting schema file can be given to the **bq load** command using
        the ``--schema`` flag:
        
        ::
        
        
            $ bq load --source_format NEWLINE_DELIMITED_JSON \
                    --ignore_unknown_values \
                    --schema file.schema.json \
                    mydataset.mytable \
                    file.data.json
        
        where ``mydataset.mytable`` is the target table in BigQuery.
        
        For debugging purposes, here is the equivalent ``bq load`` command using
        schema autodetection:
        
        ::
        
            $ bq load --source_format NEWLINE_DELIMITED_JSON \
                --ignore_unknown_values \
                --autodetect \
                mydataset.mytable \
                file.data.json
        
        A useful flag for ``bq load`` is ``--ignore_unknown_values``, which
        causes ``bq load`` to ignore fields in the input data which are not
        defined in the schema. When ``generate_schema.py`` detects an
        inconsistency in the definition of a particular field in the input data,
        it removes the field from the schema definition. Without the
        ``--ignore_unknown_values``, the ``bq load`` fails when the inconsistent
        data record is read. Another useful flag during development and
        debugging is ``--replace`` which replaces any existing BigQuery table.
        
        After the BigQuery table is loaded, the schema can be retrieved using:
        
        ::
        
            $ bq show --schema mydataset.mytable | python -m json.tool
        
        (The ``python -m json.tool`` command will pretty-print the JSON
        formatted schema file.) This schema file should be identical to
        ``file.schema.json``.
        
        Flag Options
        ~~~~~~~~~~~~
        
        The ``generate_schema.py`` script supports a handful of command line
        flags:
        
        -  ``--help`` Prints the usage with the list of supported flags.
        -  ``--keep_nulls`` Print the schema for null values, empty arrays or
           empty records.
        -  ``--quoted_values_are_strings`` Quoted values should be interpreted
           as strings
        -  ``--debugging_interval lines`` Number of lines between heartbeat
           debugging messages. Default 1000.
        -  ``--debugging_map`` Print the metadata schema map for debugging
           purposes
        
        Help (``--help``)
        ^^^^^^^^^^^^^^^^^
        
        Print the built-in help strings:
        
        ::
        
            $ generate-schema --help
            usage: generate_schema.py [-h] [--keep_nulls]
                                      [--debugging_interval DEBUGGING_INTERVAL]
                                      [--debugging_map]
        
            Generate BigQuery schema.
        
            optional arguments:
              -h, --help            show this help message and exit
              --keep_nulls          Print the schema for null values, empty arrays or
                                    empty records.
              --quoted_values_are_strings
                                    Quoted values should be interpreted as strings
              --debugging_interval DEBUGGING_INTERVAL
                                    Number of lines between heartbeat debugging messages.
              --debugging_map       Print the metadata schema_map instead of the schema
                                    for debugging
        
        Keep Nulls (``--keep_nulls``)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Normally when the input data file contains a field which has a null,
        empty array or empty record as its value, the field is suppressed in the
        schema file. This flag enables this field to be included in the schema
        file.
        
        In other words, using a data file containing just nulls and empty
        values:
        
        ::
        
            $ generate_schema
            { "s": null, "a": [], "m": {} }
            ^D
            INFO:root:Processed 1 lines
            []
        
        With the ``keep_nulls`` flag, we get:
        
        ::
        
            $ generate-schema --keep_nulls
            { "s": null, "a": [], "m": {} }
            ^D
            INFO:root:Processed 1 lines
            [
              {
                "mode": "REPEATED",
                "type": "STRING",
                "name": "a"
              },
              {
                "mode": "NULLABLE",
                "fields": [
                  {
                    "mode": "NULLABLE",
                    "type": "STRING",
                    "name": "__unknown__"
                  }
                ],
                "type": "RECORD",
                "name": "d"
              },
              {
                "mode": "NULLABLE",
                "type": "STRING",
                "name": "s"
              }
            ]
        
        Quoted Values Are Strings (``--quoted_values_are_strings``)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        By default, quoted values are inspected to determine if they can be
        interpreted as ``DATE``, ``TIME``, ``TIMESTAMP``, ``BOOLEAN``,
        ``INTEGER`` or ``FLOAT``. This is consistent with the algorithm used by
        ``bq load``. However, for the ``BOOLEAN``, ``INTEGER``, or ``FLOAT``
        types, it is sometimes more useful to interpret those as normal strings
        instead. This flag disables type inference for ``BOOLEAN``, ``INTEGER``
        and ``FLOAT`` types inside quoted strings.
        
        ::
        
            $ generate-schema
            { "name": "1" }
            ^D
            [
              {
                "mode": "NULLABLE",
                "name": "name",
                "type": "INTEGER"
              }
            ]
        
            $ generate-schema --quoted_values_are_strings
            { "name": "1" }
            ^D
            [
              {
                "mode": "NULLABLE",
                "name": "name",
                "type": "STRING"
              }
            ]
        
        Debugging Interval (``--debugging_interval``)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        By default, the ``generate_schema.py`` script prints a short progress
        message every 1000 lines of input data. This interval can be changed
        using the ``--debugging_interval`` flag.
        
        ::
        
            $ generate-schema --debugging_interval 50 < file.data.json > file.schema.json
        
        Debugging Map (``--debugging_map``)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        
        Instead of printing out the BigQuery schema, the ``--debugging_map``
        prints out the bookkeeping metadata map which is used internally to keep
        track of the various fields and their types that were inferred using the
        data file. This flag is intended to be used for debugging.
        
        ::
        
            $ generate-schema --debugging_map < file.data.json > file.schema.json
        
        Schema Types
        ------------
        
        Supported Types
        ~~~~~~~~~~~~~~~
        
        The **bq show --schema** command produces a JSON schema file that uses
        the older `Legacy SQL date
        types <https://cloud.google.com/bigquery/data-types>`__. For
        compatibility, **generate-schema** script will also generate a schema
        file using the legacy data types.
        
        The supported types are:
        
        -  ``BOOLEAN``
        -  ``INTEGER``
        -  ``FLOAT``
        -  ``STRING``
        -  ``TIMESTAMP``
        -  ``DATE``
        -  ``TIME``
        -  ``RECORD``
        
        The ``generate-schema`` script supports both ``NULLABLE`` and
        ``REPEATED`` modes of all of the above types.
        
        The supported format of ``TIMESTAMP`` is as close as practical to the
        `bq load
        format <https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#timestamp-type>`__:
        
        ::
        
            YYYY-[M]M-[D]D[( |T)[H]H:[M]M:[S]S[.DDDDDD]][time zone]
        
        which appears to be an extension of the `ISO 8601
        format <https://en.wikipedia.org/wiki/ISO_8601>`__. The difference from
        ``bq load`` is that the ``[time zone]`` component can be only \* ``Z``
        \* ``UTC`` (same as ``Z``) \* ``(+|-)H[H][:M[M]]``
        
        The suffix ``UTC`` is not standard ISO 8601 nor `documented by
        Google <https://cloud.google.com/bigquery/docs/reference/standard-sql/data-types#time-zones>`__
        but the ``UTC`` suffix is used by ``bq extract`` and the web interface.
        (See `Issue
        19 <https://github.com/bxparks/bigquery-schema-generator/issues/19>`__.)
        
        Timezone names from the `tz database <http://www.iana.org/time-zones>`__
        (e.g. "America/Los\_Angeles") are *not* supported by
        ``generate-schema``.
        
        The following types are *not* supported at all:
        
        -  ``BYTES``
        -  ``DATETIME`` (unable to distinguish from ``TIMESTAMP``)
        
        Type Inferrence Rules
        ~~~~~~~~~~~~~~~~~~~~~
        
        The ``generate-schema`` script attempts to emulate the various type
        conversion and compatibility rules implemented by **bq load**:
        
        -  ``INTEGER`` can upgrade to ``FLOAT``
        
           -  if a field in an early record is an ``INTEGER``, but a subsequent
              record shows this field to have a ``FLOAT`` value, the type of the
              field will be upgraded to a ``FLOAT``
           -  the reverse does not happen, once a field is a ``FLOAT``, it will
              remain a ``FLOAT``
        
        -  conflicting ``TIME``, ``DATE``, ``TIMESTAMP`` types upgrades to
           ``STRING``
        
           -  if a field is determined to have one type of "time" in one record,
              then subsequently a different "time" type, then the field will be
              assigned a ``STRING`` type
        
        -  ``NULLABLE RECORD`` can upgrade to a ``REPEATED RECORD``
        
           -  a field may be defined as ``RECORD`` (aka "Struct") type with
              ``{ ... }``
           -  if the field is subsequently read as an array with a
              ``[{ ... }]``, the field is upgraded to a ``REPEATED RECORD``
        
        -  a primitive type (``FLOAT``, ``INTEGER``, ``STRING``) cannot upgrade
           to a ``REPEATED`` primitive type
        
           -  there's no technical reason why this cannot be allowed, but **bq
              load** does not support it, so we follow its behavior
        
        -  a ``DATETIME`` field is always inferred to be a ``TIMESTAMP``
        
           -  the format of these two fields is identical (in the absence of
              timezone)
           -  we follow the same logic as **bq load** and always infer these as
              ``TIMESTAMP``
        
        -  ``BOOLEAN``, ``INTEGER``, and ``FLOAT`` can appear inside quoted
           strings
        
           -  In other words, ``"true"`` (or ``"True"`` or ``"false"``, etc) is
              considered a BOOLEAN type, ``"1"`` is considered an INTEGER type,
              and ``"2.1"`` is considered a FLOAT type. Luigi Mori
              (jtschichold@) added additional logic to replicate the type
              conversion logic used by ``bq load`` for these strings.
           -  This type inference inside quoted strings can be disabled using
              the ``--quoted_values_are_strings`` flag
           -  (See `Issue
              #22 <https://github.com/bxparks/bigquery-schema-generator/issues/22>`__
              for more details.)
        
        -  ``INTEGER`` values overflowing a 64-bit signed integer upgrade to
           ``FLOAT``
        
           -  integers greater than ``2^63-1`` (9223372036854775807)
           -  integers less than ``-2^63`` (-9223372036854775808)
           -  (See `Issue
              #18 <https://github.com/bxparks/bigquery-schema-generator/issues/18>`__
              for more details)
        
        Examples
        --------
        
        Here is an example of a single JSON data record on the STDIN (the ``^D``
        below means typing Control-D, which indicates "end of file" under Linux
        and MacOS):
        
        ::
        
            $ generate-schema
            { "s": "string", "b": true, "i": 1, "x": 3.1, "t": "2017-05-22T17:10:00-07:00" }
            ^D
            INFO:root:Processed 1 lines
            [
              {
                "mode": "NULLABLE",
                "name": "b",
                "type": "BOOLEAN"
              },
              {
                "mode": "NULLABLE",
                "name": "i",
                "type": "INTEGER"
              },
              {
                "mode": "NULLABLE",
                "name": "s",
                "type": "STRING"
              },
              {
                "mode": "NULLABLE",
                "name": "t",
                "type": "TIMESTAMP"
              },
              {
                "mode": "NULLABLE",
                "name": "x",
                "type": "FLOAT"
              }
            ]
        
        In most cases, the data file will be stored in a file:
        
        ::
        
            $ cat > file.data.json
            { "a": [1, 2] }
            { "i": 3 }
            ^D
        
            $ generate-schema < file.data.json > file.schema.json
            INFO:root:Processed 2 lines
        
            $ cat file.schema.json
            [
              {
                "mode": "REPEATED",
                "name": "a",
                "type": "INTEGER"
              },
              {
                "mode": "NULLABLE",
                "name": "i",
                "type": "INTEGER"
              }
            ]
        
        Benchmarks
        ----------
        
        I wrote the ``bigquery_schema_generator/anonymize.py`` script to create
        an anonymized data file ``tests/testdata/anon1.data.json.gz``:
        
        ::
        
            $ ./bigquery_schema_generator/anonymize.py < original.data.json \
                > anon1.data.json
            $ gzip anon1.data.json
        
        This data file is 290MB (5.6MB compressed) with 103080 data records.
        
        Generating the schema using
        
        ::
        
            $ bigquery_schema_generator/generate_schema.py < anon1.data.json \
                > anon1.schema.json
        
        took 77s on a Dell Precision M4700 laptop with an Intel Core i7-3840QM
        CPU @ 2.80GHz, 32GB of RAM, Ubuntu Linux 17.10, Python 3.6.3.
        
        System Requirements
        -------------------
        
        This project was initially developed on Ubuntu 17.04 using Python 3.5.3.
        I have tested it on:
        
        -  Ubuntu 18.04, Python 3.6.7
        -  Ubuntu 17.10, Python 3.6.3
        -  Ubuntu 17.04, Python 3.5.3
        -  Ubuntu 16.04, Python 3.5.2
        -  MacOS 10.14.2, `Python
           3.6.4 <https://www.python.org/downloads/release/python-364/>`__
        -  MacOS 10.13.2, `Python
           3.6.4 <https://www.python.org/downloads/release/python-364/>`__
        
        Changelog
        ---------
        
        See `CHANGELOG.md <CHANGELOG.md>`__.
        
        Authors
        -------
        
        -  Created by Brian T. Park (brian@xparks.net).
        -  Additional type inference logic by Luigi Mori (jtschichold@).
        -  Flag to disable type inference inside quoted strings by Daniel Ecer
           (de-code@).
        
        License
        -------
        
        Apache License 2.0
        
Platform: UNKNOWN
Requires-Python: ~=3.5
