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BigQuery schema generator from JSON or CSV data

Project description

This script generates the BigQuery schema from the newline-delimited data records on the STDIN. The records can be in JSON format or CSV format. 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
$ generate-schema --input_format csv < file.data.csv > file.schema.json

Version: 1.0 (2020-04-04)

Background

Data can be imported into BigQuery using the bq 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. The table’s schema can be defined manually or the schema can be auto-detected.

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 repository using pip3. There are too many ways to install packages in Python. The following are in order highest to lowest recommendation:

  1. If you are using a virtual environment (such as venv), then use:

    $ pip3 install bigquery_schema_generator
    
  2. If you aren’t using a virtual environment you can install into your local Python directory:

$ pip3 install --user bigquery_schema_generator
  1. If you want to install the package for your entire system globally, use

    $ sudo -H pip3 install bigquery_schema_generator
    

    but realize that you will be running code from PyPI as root so this has security implications.

Sometimes, your Python environment gets into a complete mess and the pip3 command won’t work. Try typing python3 -m pip instead.

A successful install should print out something like the following (the version number may be different):

Collecting bigquery-schema-generator
Installing collected packages: bigquery-schema-generator
Successfully installed bigquery-schema-generator-0.3.2

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, 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 or CSV data file on the STDIN. JSON input format has been tested extensively. CSV input format was added more recently (in v0.4) using the --input_format csv flag. The support is not as robust as JSON file. For example, CSV format supports only the comma-separator, and does not support the pipe (|) or tab (\t) character.

Unlike bq load, the generate_schema.py script reads 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, then you can invoke the Python script directly:

$ ./generate_schema.py < file.data.json > file.schema.json

Using the 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 \
    --autodetect \
    mydataset.mytable \
    file.data.json

If the input file is in CSV format, the first line will be the header line which enumerates the names of the columns. But this header line must be skipped when importing the file into the BigQuery table. We accomplish this using --skip_leading_rows flag:

$ bq load --source_format CSV \
    --schema file.schema.json \
    --skip_leading_rows 1 \
    mydataset.mytable \
    file.data.csv

Here is the equivalent bq load command for CSV files using autodetection:

$ bq load --source_format CSV \
    --autodetect \
    mydataset.mytable \
    file.data.csv

A useful flag for bq load, particularly for JSON files, 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 | python3 -m json.tool

(The python -m json.tool command will pretty-print the JSON formatted schema file. An alternative is the jq command.) The resulting schema file should be identical to file.schema.json.

Flag Options

The generate_schema.py script supports a handful of command line flags as shown by the --help flag below.

Help (--help)

Print the built-in help strings:

$ generate-schema --help
usage: generate_schema.py [-h] [--input_format INPUT_FORMAT] [--keep_nulls]
                          [--quoted_values_are_strings] [--infer_mode]
                          [--debugging_interval DEBUGGING_INTERVAL]
                          [--debugging_map] [--sanitize_names]

Generate BigQuery schema from JSON or CSV file.

optional arguments:
  -h, --help            show this help message and exit
  --input_format INPUT_FORMAT
                        Specify an alternative input format ('csv', 'json')
  --keep_nulls          Print the schema for null values, empty arrays or
                        empty records
  --quoted_values_are_strings
                        Quoted values should be interpreted as strings
  --infer_mode          Determine if mode can be 'NULLABLE' or 'REQUIRED'
  --debugging_interval DEBUGGING_INTERVAL
                        Number of lines between heartbeat debugging messages
  --debugging_map       Print the metadata schema_map instead of the schema
  --sanitize_names      Forces schema name to comply with BigQuery naming
                        standard

Input Format (--input_format)

Specifies the format of the input file, either json (default) or csv.

If csv file is specified, the --keep_nulls flag is automatically activated. This is required because CSV columns are defined positionally, so the schema file must contain all the columns specified by the CSV file, in the same order, even if the column contains an empty value for every record.

See Issue #26 for implementation details.

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"
  }
]

Infer Mode (--infer_mode)

Set the schema mode of a field to REQUIRED instead of the default NULLABLE if the field contains a non-null or non-empty value for every data record in the input file. This option is available only for CSV (--input_format csv) files. It is theoretically possible to implement this feature for JSON files, but too difficult to implement in practice because fields are often completely missing from a given JSON record (instead of explicitly being defined to be null).

See Issue #28 for implementation details.

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

Sanitize Names (--sanitize_names)

BigQuery column names are restricted to certain characters and length. With this flag, column names are sanitizes so that any character outside of ASCII letters, numbers and underscore ([a-zA-Z0-9_]) are converted to an underscore. (For example “go&2#there!” is converted to “go_2_there_”.) Names longer than 128 characters are truncated to 128.

Schema Types

Supported Types

The bq show –schema command produces a JSON schema file that uses the older Legacy SQL date 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:

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. The difference from bq load is that the [time zone] component can be only * Z * UTC (same as Z) * (+|-)H[H][:M[M]]

Note that BigQuery supports up to 6 decimal places after the integer ‘second’ component. generate-schema follows the same restriction for compatibility. If your input file contains more than 6 decimal places, you need to write a data cleansing filter to fix this.

The suffix UTC is not standard ISO 8601 nor documented by Google but the UTC suffix is used by bq extract and the web interface. (See Issue 19.)

Timezone names from the tz database (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 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 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"
  }
]

Here is the schema generated from a CSV input file. The first line is the header containing the names of the columns, and the schema lists the columns in the same order as the header:

$ generate-schema --input_format csv
e,b,c,d,a
1,x,true,,2.0
2,x,,,4
3,,,,
^D
INFO:root:Processed 3 lines
[
  {
    "mode": "NULLABLE",
    "name": "e",
    "type": "INTEGER"
  },
  {
    "mode": "NULLABLE",
    "name": "b",
    "type": "STRING"
  },
  {
    "mode": "NULLABLE",
    "name": "c",
    "type": "BOOLEAN"
  },
  {
    "mode": "NULLABLE",
    "name": "d",
    "type": "STRING"
  },
  {
    "mode": "NULLABLE",
    "name": "a",
    "type": "FLOAT"
  }
]

Here is an example of the schema generated with the --infer_mode flag:

$ generate-schema --input_format csv --infer_mode
name,surname,age
John
Michael,,
Maria,Smith,30
Joanna,Anders,21
^D
INFO:root:Processed 4 lines
[
  {
    "mode": "REQUIRED",
    "name": "name",
    "type": "STRING"
  },
  {
    "mode": "NULLABLE",
    "name": "surname",
    "type": "STRING"
  },
  {
    "mode": "NULLABLE",
    "name": "age",
    "type": "INTEGER"
  }
]

Using As a Library

The bigquery_schema_generator module can be used as a library by an external Python client code by creating an instance of SchemaGenerator and calling the run(input, output) method:

from bigquery_schema_generator.generate_schema import SchemaGenerator

generator = SchemaGenerator(
    input_format=input_format,
    infer_mode=infer_mode,
    keep_nulls=keep_nulls,
    quoted_values_are_strings=quoted_values_are_strings,
    debugging_interval=debugging_interval,
    debugging_map=debugging_map)
generator.run(input_file, output_file)

If you need to process the generated schema programmatically, use the deduce_schema() method and process the resulting schema_map and error_log data structures like this:

from bigquery_schema_generator.generate_schema import SchemaGenerator
...
schema_map, error_logs = generator.deduce_schema(input_file)

for error in error_logs:
    logging.info("Problem on line %s: %s", error['line'], error['msg'])

schema = generator.flatten_schema(schema_map)
json.dump(schema, output_file, indent=2)

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 67s on a Dell Precision M4700 laptop with an Intel Core i7-3840QM CPU @ 2.80GHz, 32GB of RAM, Ubuntu Linux 18.04, Python 3.6.7.

System Requirements

This project was initially developed on Ubuntu 17.04 using Python 3.5.3, but it now requires Python 3.6 or higher, I think mostly due to the use of f-strings.

I have tested it on:

  • Ubuntu 18.04, Python 3.7.7
  • Ubuntu 18.04, Python 3.6.7
  • Ubuntu 17.10, Python 3.6.3
  • MacOS 10.14.2, Python 3.6.4
  • MacOS 10.13.2, Python 3.6.4

The GitHub Actions continuous integration pipeline validates on Python 3.6, 3.7 and 3.8.

Changelog

See CHANGELOG.md.

Authors

  • Created by Brian T. Park (brian@xparks.net).
  • Type inference inside quoted strings by Luigi Mori (jtschichold@).
  • Flag to disable type inference inside quoted strings by Daniel Ecer (de-code@).
  • Support for CSV files and detection of REQUIRED fields by Sandor Korotkevics (korotkevics@).
  • Better support for using bigquery_schema_generator as a library from an external Python code by StefanoG_ITA (StefanoGITA@).
  • Sanitizing of column names to valid BigQuery characters and length by Jon Warghed (jonwarghed@).

License

Apache License 2.0

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