3
votes

We are using BigQuery Python API to run some analyzes. To do so, we created the following adapter:

def stream_data(self, table, data, schema, how=None):
    r = self.connector.tables().list(projectId=self._project_id,
                                     datasetId='lbanor').execute()
    table_exists = [row['tableReference']['tableId'] for row in
                    r['tables'] if
                    row['tableReference']['tableId'] == table]
    if table_exists:
        if how == 'WRITE_TRUNCATE':
            self.connector.tables().delete(projectId=self._project_id,
                                           datasetId='lbanor',
                                           tableId=table).execute()
            body = {
                'tableReference': {
                    'tableId': table,
                    'projectId': self._project_id,
                    'datasetId': 'lbanor'
                },
                'schema': schema
            }
            self.connector.tables().insert(projectId=(
                                           self._project_id),
                                           datasetId='lbanor',
                                           body=body).execute()
    else:
        body = {
            'tableReference': {
                'tableId': table,
                'projectId': self._project_id,
                'datasetId': 'lbanor'
            },
            'schema': schema
        }
        self.connector.tables().insert(projectId=(
                                       self._project_id),
                                       datasetId='lbanor',
                                       body=body).execute()

    body = {
        'rows': [
            {
                'json': data,
                'insertId': str(uuid.uuid4())
            }
        ]
    }
    self.connector.tabledata().insertAll(projectId=(
                                         self._project_id),
                                         datasetId='lbanor',
                                         tableId=table,
                                               body=body).execute(num_retries=5)

where connector is just the build object.

Its main purpose is to stream data to the given table. And in case the table already exists and "how" input is passed as "WRITE_TRUNCATE", then the table is first deleted and created again. After that, proceed doing the data stream.

Everything worked fine when the table is not deleted over and over again.

For instance, this is the result when we run the script without simulating the write truncation option (a for loop keeps calling stream_data with how=None):

[
  {
    "date": "2016-04-25",
    "unix_date": "1461606664981207",
    "init_cv_date": "2016-03-12",
    "end_cv_date": "2016-03-25",
    "days_trained": "56",
    "days_validated": "14",
    "navigated_score": "1",
    "carted_score": "3",
    "purchased_score": "10",
    "description": "First trial of top seller alg. No filter nor any condition is applied. Skus not present in train count as rank=0.5",
    "metric": "rank",
    "result": "0.31729249914663893"
  },
  {
    "date": "2016-04-25",
    "unix_date": "1461606599745107",
    "init_cv_date": "2016-03-06",
    "end_cv_date": "2016-03-25",
    "days_trained": "80",
    "days_validated": "20",
    "navigated_score": "1",
    "carted_score": "3",
    "purchased_score": "10",
    "description": "First trial of top seller alg. No filter nor any condition is applied. Skus not present in train count as rank=0.5",
    "metric": "rank",
    "result": "0.32677143128667446"
  },
  {
    "date": "2016-04-25",
    "unix_date": "1461606688950415",
    "init_cv_date": "2016-03-14",
    "end_cv_date": "2016-03-25",
    "days_trained": "48",
    "days_validated": "12",
    "navigated_score": "1",
    "carted_score": "3",
    "purchased_score": "10",
    "description": "First trial of top seller alg. No filter nor any condition is applied. Skus not present in train count as rank=0.5",
    "metric": "rank",
    "result": "0.3129267723358932"
  },
  {
    "date": "2016-04-25",
    "unix_date": "1461606707195122",
    "init_cv_date": "2016-03-16",
    "end_cv_date": "2016-03-25",
    "days_trained": "40",
    "days_validated": "10",
    "navigated_score": "1",
    "carted_score": "3",
    "purchased_score": "10",
    "description": "First trial of top seller alg. No filter nor any condition is applied. Skus not present in train count as rank=0.5",
    "metric": "rank",
    "result": "0.310620987663015"
  },
  {
    "date": "2016-04-25",
    "unix_date": "1461606622432947",
    "init_cv_date": "2016-03-08",
    "end_cv_date": "2016-03-25",
    "days_trained": "72",
    "days_validated": "18",
    "navigated_score": "1",
    "carted_score": "3",
    "purchased_score": "10",
    "description": "First trial of top seller alg. No filter nor any condition is applied. Skus not present in train count as rank=0.5",
    "metric": "rank",
    "result": "0.32395802949369296"
  }
]

But when we use the same adapter with the input how="WRITE_TRUNCATE", its behavior changed and became unpredictable.

Sometimes it works and data is saved to the table. But sometimes, even though no error is raised, no data is saved to the table.

When trying to query the table, no data is returned. It just returns "Query returned zero results".

Something went wrong when deleting the table, creating it again and streaming the data. Are we making some mistake?

If you need more info please let me know. Thanks in advance!

1

1 Answers

6
votes

See Jordan Tigani answer and Sean Chen's comment to https://stackoverflow.com/a/36417177/132438 (both BigQuery engineers).

Summary is:

  • When re-creating or truncating a table "You'll need to wait >2 minutes before streaming in order to avoid data being dropped.

So that would explain why you are getting this non-deterministic behavior.