Basic tutorial for BigQuery with Python


BigQuery is a fully-managed enterprise data warehouse for analystics.
It is cheap and high-scalable. In this article, I would like to share basic tutorial for BigQuery with Python.

🐹 Installation

pip install google-cloud-bigquery

😎 Create credentials

please see https://cloud.google.com/bigquery/docs/reference/libraries .

Additionally, please set the PATH to environment variables.

export GOOGLE_APPLICATION_CREDENTIALS="/home/user/Downloads/[FILE_NAME].json"

🐝 Create a dataset if not existing

Create a dataset if there is not the dataset:

from google.cloud import bigquery

def bq_create_dataset():
    bigquery_client = bigquery.Client()
    dataset_ref = bigquery_client.dataset('my_datasset_id')

    try:
        bigquery_client.get_dataset(dataset_ref)
    except NotFound:
        dataset = bigquery.Dataset(dataset_ref)
        dataset = bigquery_client.create_dataset(dataset)
        print('Dataset {} created.'.format(dataset.dataset_id))

🐯 Create a table if not existing

Create a table if there is not the table:

from google.cloud import bigquery

def bq_create_table():
    bigquery_client = bigquery.Client()
    dataset_ref = bigquery_client.dataset('my_datasset_id')

    # Prepares a reference to the table
    table_ref = dataset_ref.table('my_table_name')

    try:
        bigquery_client.get_table(table_ref)
    except NotFound:
        schema = [
            bigquery.SchemaField('name', 'STRING', mode='REQUIRED'),
            bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
        ]
        table = bigquery.Table(table_ref, schema=schema)
        table = bigquery_client.create_table(table)
        print('table {} created.'.format(table.table_id))

🚜 Schema info

You can add description or required option to schema information.

BQ_TABLE_SCHEMA = [
    bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED', description='Age'),
    bigquery.SchemaField('name', 'STRING', description='Name'),
    bigquery.SchemaField('created_at', 'TIMESTAMP', mode='REQUIRED', description='Date and time when the record was created')
]

If you want to know more detail for SchemaField method, please see bigquery.schema.SchemaField

🎳 Insert rows

Upload tuple object to BigQuery. It use stream buffer, so I don’t recommend it.

from google.cloud import bigquery

def export_items_to_bigquery():
    # Instantiates a client
    bigquery_client = bigquery.Client()

    # Prepares a reference to the dataset
    dataset_ref = bigquery_client.dataset('my_datasset_id')

    table_ref = dataset_ref.table('my_table_id')
    table = bigquery_client.get_table(table_ref)  # API call

    rows_to_insert = [
        (u'Phred Phlyntstone', 32),
        (u'Wylma Phlyntstone', 29),
    ]
    errors = bigquery_client.insert_rows(table, rows_to_insert)  # API request
    assert errors == []

🗻 Check data exist

from google.cloud import bigquery

def exist_record()
    bigquery_client = bigquery.Client()

    query = ('SELECT id FROM `{}.{}.{}` WHERE id="{}" LIMIT 1'
            .format('my_project_id', 'my_datasset_id', 'my_table_id', 'my_selected_id'))

    try:
        query_job = bigquery_client.query(query)
        is_exist = len(list(query_job.result())) >= 1
        print('Exist id: {}'.format('my_selected_id') if is_exist else 'Not exist id: {}'.format('my_selected_id'))
        return is_exist
    except Exception as e:
        print("Error")
        print(e)

    return False

🏈 Upload a csv to google cloud storage and load the csv

This is a sample which is uploading a CSV file to google cloud storage and load the CSV file to BigQuery.

Before coding, please execute as follows:

pip install google-cloud-storage

After installing google-cloud-storage, add following functions:

from google.cloud import bigquery
from google.cloud import storage

def gcs_upload_blob():
    """Uploads a file to the bucket."""
    storage_client = storage.Client()
    bucket = storage_client.get_bucket('my_bucket_name')
    blob = bucket.blob('gs://path/to/file.name')

    blob.upload_from_filename('/path/to/source.file')

    print('File {} uploaded to {}.'.format(
        '/path/to/source.csv',
        'gs://path/to/upload.csv'))


def bq_load_csv_in_gcs():
    bigquery_client = bigquery.Client()
    dataset_ref = bigquery_client.dataset()

    job_config = bigquery.LoadJobConfig()
    schema = [
        bigquery.SchemaField('name', 'STRING', mode='REQUIRED'),
        bigquery.SchemaField('age', 'INTEGER', mode='REQUIRED'),
    ]
    job_config.schema = schema
    job_config.skip_leading_rows = 1

    load_job = bigquery_client.load_table_from_uri(
        'gs://path/to/upload.csv',
        dataset_ref.table('my_table_id'),
        job_config=job_config)

    assert load_job.job_type == 'load'

    load_job.result()  # Waits for table load to complete.

    assert load_job.state == 'DONE'

🎃 Appendix

Web Console / Enable to standardSQL

If you want to delete some records in BigQuery, please add #standardSQL, like this:

#standardSQL
DELETE articles WHERE title = 'sample'

More Detail: Setting a query prefix

🐠 References

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