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An analytics team is running interactive dashboard queries in BigQuery using an Enterprise Edition reservation configured with baseline slots and autoscaling capacity. During peak reporting hours, numerous concurrent queries execute simultaneously, diluting the slot allocation per query and causing unacceptable latency. Additionally, finance reports discrepancies between query-level slot runtime metrics and compute reservation charges.
Which combination of configuration and observability practices should the data engineering team implement to stabilize query performance and correctly analyze compute costs?
Configure ignore_idle_slots to true on the reservation, and calculate compute billing by aggregating total_slot_ms from INFORMATION_SCHEMA.JOBS grouped by reservation_id.
Assign all queries to run as batch query jobs rather than interactive jobs, and use INFORMATION_SCHEMA.RECOMMENDATIONS to verify monthly slot reductions.
Set default_interactive_query_queue_timeout_ms to -1 in INFORMATION_SCHEMA.EFFECTIVE_PROJECT_OPTIONS, and increase baseline slots to match peak concurrency demand.
Set a non-zero target_job_concurrency on the reservation using ALTER RESERVATION, and monitor autoscaling capacity costs through reservation-level capacity compute metrics rather than aggregating slot-milliseconds from INFORMATION_SCHEMA.JOBS.
Configure ignore_idle_slots to true on the reservation, and calculate compute billing by aggregating total_slot_ms from INFORMATION_SCHEMA.JOBS grouped by reservation_id.
Assign all queries to run as batch query jobs rather than interactive jobs, and use INFORMATION_SCHEMA.RECOMMENDATIONS to verify monthly slot reductions.
Set default_interactive_query_queue_timeout_ms to -1 in INFORMATION_SCHEMA.EFFECTIVE_PROJECT_OPTIONS, and increase baseline slots to match peak concurrency demand.
Set a non-zero target_job_concurrency on the reservation using ALTER RESERVATION, and monitor autoscaling capacity costs through reservation-level capacity compute metrics rather than aggregating slot-milliseconds from INFORMATION_SCHEMA.JOBS.
This solution configures a specific target job concurrency on the BigQuery reservation using the ALTER RESERVATION SET OPTIONS statement or the Google Cloud console, while adopting reservation-level capacity monitoring for billing evaluation rather than individual job metrics.
StudyCerts
StudyCertstarget_job_concurrency = 0), which may allow too many queries to execute at the same time and spread slot capacity too thinly. Setting a non-zero target_job_concurrency imposes an upper bound on simultaneous executions within that reservation, ensuring each running query receives a guaranteed minimum slot allocation while excess queries queue safely.INFORMATION_SCHEMA.JOBS ensures accurate financial tracking.This approach directly resolves query contention by tuning reservation concurrency settings and aligns monitoring practices with the true billing mechanics of BigQuery autoscaling editions.