[jira] [Updated] (AIRFLOW-1632) MySQL to GCS fails for date/datetime before ~1850

["Michael Ghen (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/AIRFLOW-1632?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Michael Ghen updated AIRFLOW-1632:
----------------------------------
    Summary: MySQL to GCS fails for date/datetime before ~1850  (was: MySQL to GCS fails for dates before ~1850)

> MySQL to GCS fails for date/datetime before ~1850
> -------------------------------------------------
>
>                 Key: AIRFLOW-1632
>                 URL: https://issues.apache.org/jira/browse/AIRFLOW-1632
>             Project: Apache Airflow
>          Issue Type: Bug
>          Components: gcp
>         Environment: Google Cloud Platform
>            Reporter: Michael Ghen
>            Assignee: Michael Ghen
>            Priority: Minor
>             Fix For: 1.9.0
>
>
> For tables in MySQL that use a "date" or "datetime" type, a dag that exports from MySQL to Google Cloud Storage and then loads from GCS to BigQuery will fail when the dates are before 1970.
> When the table is exported as JSON to a GCS bucket, dates and datetimes are converted to timestamps using:
> {code}
> time.mktime(value.timetuple())
> {code} 
> This creates a problem when you try parse a date that can't be converted to a UNIX timestamp. For example:
> {code}
> >>> value = datetime.date(1850,1,1)
> >>> time.mktime(value.timetuple())
> Traceback (most recent call last):
>   File "<stdin>", line 1, in <module>
> ValueError: year out of range
> {code}
> *Steps to reproduce*
> 0. Set up a MySQL connection and GCP connection in Airflow.
> 1. Create a MySQL table with a "date" field and put some data into the table. 
> {code}
> CREATE TABLE table_with_date (
> date_field date,
> datetime_field datetime
> );
> INSERT INTO table_with_date (date_field, datetime_field) VALUES ('1850-01-01',NOW());
> {code}
> 2. Create a DAG that will export the data from the MySQL to GCS and then load from GCS to BigQuery (use the schema file). For example:
> {code}
> extract = MySqlToGoogleCloudStorageOperator(
>         task_id="extract_table",
>         mysql_conn_id='mysql_connection',
>         google_cloud_storage_conn_id='gcp_connection',
>         sql="SELECT * FROM table_with_date",
>         bucket='gcs-bucket',
>         filename='table_with_date.json',
>         schema_filename='schemas/table_with_date.json',
>         dag=dag)
> load = GoogleCloudStorageToBigQueryOperator(
>         task_id="load_table",
>         bigquery_conn_id='gcp_connection',
>         google_cloud_storage_conn_id='gcp_connection',
>         bucket='gcs-bucket',
>         destination_project_dataset_table="dataset.table_with_date",
>         source_objects=['table_with_date.json'],
>         schema_object='schemas/table_with_date.json',
>         source_format='NEWLINE_DELIMITED_JSON',
>         create_disposition='CREATE_IF_NEEDED',
>         write_disposition='WRITE_TRUNCATE',
>         dag=dag)
> load.set_upstream(extract)
> {code}
> 3. Run the DAG 
> Expected: The DAG runs successfully.
> Actual: The `load_table` task fails with error:
> {code}
> ...
> {u'reason': u'invalid', 
> u'message': u'JSON parsing error in row starting at position 0: Could not convert value to string. Field: date_field; Value: 1504929600.000000', 
> u'location': u'gs://gcs-bucket/table_with_date.json'
> ...
> {code}
> *Comments:*
> Seems like this was just a simple oversight in the section of `airflow/contrib/operators/mysql_to_gcs.py` where the types get converted. In `convert_types` both `date` and `datetime` types get converted to timestamps but in `type_map` there is no mapping for `FIELD_TYPE.date`. This small bug almost turned my team off from using airflow because we had a lot of tables that didn't flow into BigQuery because we used the `date` type a lot.



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