11:I[80,["231","static/chunks/231-f2f18d7e4b16846f.js","5362","static/chunks/5362-5965a3776db5ac03.js","8704","static/chunks/app/updates/%5Bid%5D/page-38cd4f1673ebad97.js"],"UpdateDetailClient"] 12:T4ad,from google.cloud import bigquery # Initialize BigQuery client client = bigquery.Client(project="") # Define continuous query with stateful operations (JOINs and windowing) query = """ SELECT event_timestamp, user_id, event_type, SUM(event_value) OVER ( PARTITION BY user_id ORDER BY event_timestamp ROWS BETWEEN 10 PRECEDING AND CURRENT ROW ) as rolling_sum, COUNT(*) OVER ( PARTITION BY user_id ORDER BY event_timestamp RANGE BETWEEN INTERVAL 1 HOUR PRECEDING AND CURRENT ROW ) as hourly_count, t1.user_id, t2.user_segment FROM `..` t1 LEFT JOIN `..` t2 ON t1.user_id = t2.user_id ORDER BY event_timestamp DESC """ # Configure job for continuous query job_config = bigquery.QueryJobConfig( continuous=True, destination="..", use_legacy_sql=False, write_disposition=bigquery.WriteDisposition.WRITE_APPEND, ) # Submit continuous query job query_job = client.query(query, job_config=job_config) print(f"Started continuous query job: {query_job.job_id}") print(f"Job state: {query_job.state}")13:T5ba,# Note: Continuous queries are not directly provisioned via Terraform resource. # Use google_bigquery_dataset and google_bigquery_table, then submit continuous queries via gcloud or Python client. resource "google_bigquery_dataset" "continuous_queries_dataset" { dataset_id = "" friendly_name = "Continuous Queries Dataset" description = "Dataset for storing continuous query results with stateful operations" location = "US" project = "" } resource "google_bigquery_table" "output_table" { dataset_id = google_bigquery_dataset.continuous_queries_dataset.dataset_id table_id = "

You can now use stateful operations in continuous queries, which let you perform complex analysis by retaining...

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