Multiple UPDATE queries in Google BigQuery using python

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I'm trying to run multiple BigQuery queries via the python API but it seems like not all of them are being done, my guess is that I'm not giving them enough time to complete. What I have is something like:

from import bigquery
client = bigquery.Client()

query1 = "UPDATE ..."
query2 = "UPDATE ..."
query3 = "UPDATE ..."

My solution so far is inserting a sleep command before each client call, it works but it is kind of slow. Any hints or tips about how to do it on a more efficient way are appreciated.

BigQuery uses asynchronous jobs for queries. That means you can submit your query but it hasn’t necessarily finished yet. You can wait for it to finish if the next update requires the previous one to be complete.

If they aren’t tightly coupled, just send all of your updates and then wait at the end and all should complete. The return object of a query is that job, so you can use that to check the status of any of your update commands.

More info

Data manipulation language syntax | BigQuery, Sample to update data in BigQuery tables using DML query represent a dataset with multiple tables of analytics for user sessions and a table� To query your Google BigQuery data using Python, we need to connect the Python client to our BigQuery instance. We do so using a cloud client library for the Google BigQuery API. You can also choose to use any other third-party option to connect BigQuery with Python; the BigQuery-Python library by tylertreat is also a great option.

Now that BigQuery Scripting has been released, you can simply run all UPDATEs as a script:

from import bigquery
client = bigquery.Client()

query1 = "UPDATE ..."
query1 += "UPDATE ..."
query1 += "UPDATE ..."

Updating table data using the Data Manipulation Language, Batch mutations. A common scenario within OLAP systems involves updating existing data based on new information arriving from source� Query BigQuery using Ibis. Join multiple BigQuery tables together. Write a BigQuery user-defined function (UDF) in Python. Before you begin. Follow the instructions in the following guides to set up your environment to develop Python code that connects to Google Cloud Platform: Set up a Python development environment.

If you return the job to a variable, you can use job.state or job.error_result to determine if the job has finished. Then you can do something like:

j = client.query(query1) 
 while j.state == 'RUNNING':
if j.error_result is not None:

Performing large-scale mutations in BigQuery, Updating dataset properties. Contents; Required permissions; Updating dataset descriptions; Updating default table expiration times; Updating default partition� Open multiple Chrome tabs running BigQuery, with each tab containing a different query. For some, switching across queries in separate Chrome Tabs (holy RAM usage, Batman!) is manageable. For others, it can be annoying to find that specific query amongst a sea of indistinguishable tabs.

Updating view properties | BigQuery, Updating table properties. Required permissions; Updating a table's description; Updating a table's expiration time; Updating a table's schema definition. Follow along with the instructions below by using the sample files, which represent a dataset with multiple tables of analytics for user sessions and a table of users to be deleted. deletedUsersData.json ( download )

Updating dataset properties | BigQuery, dataOwner access gives the user the ability to create and update tables in the dataset. For more information on IAM roles and permissions in BigQuery, see� If you are creating the scheduled query using the classic BigQuery web UI, allow browser pop-ups from so that you can view the permissions window. You must allow the BigQuery Data Transfer Service permission to manage your scheduled query.

Managing tables | BigQuery, C# Go Java More. Node.js PHP Python Ruby. View on GitHub Feedback. Console.Write("\nQuery Results:\n------------\n"); foreach (var row in result) { Console. Turns out that there are no ways to execute multiple queries in either the BigQuery Composer or the the Google Cloud Shell. However, 1 workaround that I have found is to create a local text file in Cloud Shell which stores the queries, delimited by ";". And then set the IFS (Internal Field Separator) to ";" so that I can use a for loop to loop through the file and execute the queries one by