I have a dataset in BigQuery. This dataset contains multiple tables.
I am doing the following steps programmatically using the BigQuery API:
Querying the tables in the dataset - Since my response is too large, I am enabling allowLargeResults parameter and diverting my response to a destination table.
I am then exporting the data from the destination table to a GCS bucket.
Requirements:
Suppose my process fails at Step 2, I would like to re-run this step.
But before I re-run, I would like to check/verify that the specific destination table named 'xyz' already exists in the dataset.
If it exists, I would like to re-run step 2.
If it does not exist, I would like to do foo.
How can I do this?
Thanks in advance.
// tableExists checks wheter a table exists on a given dataset.
In the Google Cloud console, go to the BigQuery page. In the Explorer pane, expand the project and dataset nodes of the table snapshot you want to restore from. Click the name of the table snapshot. In the table snapshot pane that appears, click Restore.
$dataset = $bigQuery->dataset($datasetId); $table = $dataset->table($tableId); $table->delete();
Alex F's solution works on v0.27, but will not work on later versions. In order to migrate to v0.28+, the below solution will work.
from google.cloud import bigquery
project_nm = 'gc_project_nm'
dataset_nm = 'ds_nm'
table_nm = 'tbl_nm'
client = bigquery.Client(project_nm)
dataset = client.dataset(dataset_nm)
table_ref = dataset.table(table_nm)
def if_tbl_exists(client, table_ref):
from google.cloud.exceptions import NotFound
try:
client.get_table(table_ref)
return True
except NotFound:
return False
if_tbl_exists(client, table_ref)
Here is a python snippet that will tell whether a table exists (deleting it in the process--careful!):
def doesTableExist(project_id, dataset_id, table_id):
bq.tables().delete(
projectId=project_id,
datasetId=dataset_id,
tableId=table_id).execute()
return False
Alternately, if you'd prefer not deleting the table in the process, you could try:
def doesTableExist(project_id, dataset_id, table_id):
try:
bq.tables().get(
projectId=project_id,
datasetId=dataset_id,
tableId=table_id).execute()
return True
except HttpError, err
if err.resp.status <> 404:
raise
return False
If you want to know where bq
came from, you can call build_bq_client
from here: http://code.google.com/p/bigquery-e2e/source/browse/samples/ch12/auth.py
In general, if you're using this to test whether you should run a job that will modify the table, it can be a good idea to just do the job anyway, and use WRITE_TRUNCATE
as a write disposition.
Another approach can be to create a predictable job id, and retry the job with that id. If the job already exists, the job already ran (you might want to double check to make sure the job didn't fail, however).
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With