Professional Cloud DevOps Engineer
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An enterprise organization is experiencing surging Cloud Logging ingestion costs across multiple production Google Cloud projects. An analysis of log volume via Cloud Monitoring Metrics Explorer reveals two major contributors to the high ingestion bill:
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StudyCertskube-system and monitoring-system.You must implement a logging optimization strategy that eliminates redundant log ingestion and minimizes storage costs while preserving critical GKE workload logs, Kubernetes audit trails, and essential Dataflow troubleshooting capabilities.
What should you do to achieve this objective?
Configure exclusion filters on the _Default log sinks targeting GKE system namespaces with resource.type=("k8s_container" OR "k8s_pod") AND resource.labels.namespace_name=("kube-system" OR "monitoring-system"), and exclude Dataflow step logs where labels."dataflow.googleapis.com/log_type"!="system" AND labels."dataflow.googleapis.com/log_type"!="supportability".
Configure an intercepting aggregated sink at the folder level routed directly to a central Cloud Storage bucket with an exclusion filter matching resource.type=("k8s_cluster" OR "dataflow_job").
Update the inclusion filter of the project-level _Default sinks to empty string (""), and set a 10% sample() exclusion rate across all resource.type values.
Create an organization-level non-intercepting aggregated sink routed to Pub/Sub with an inclusion filter for Dataflow and GKE logs, and disable the _Required sink in each underlying project.
Configure exclusion filters on the _Default log sinks targeting GKE system namespaces with resource.type=("k8s_container" OR "k8s_pod") AND resource.labels.namespace_name=("kube-system" OR "monitoring-system"), and exclude Dataflow step logs where labels."dataflow.googleapis.com/log_type"!="system" AND labels."dataflow.googleapis.com/log_type"!="supportability".
Log exclusion filters in Google Cloud Logging are rules attached to log sinks (such as the default _Default sink or user-defined aggregated sinks) that explicitly discard matching log entries from being routed to and ingested by downstream storage destinations like log buckets.
resource.type=("k8s_container" OR "k8s_pod") combined with resource.labels.namespace_name=("kube-system" OR "monitoring-system"), high-frequency system pod telemetry is dropped before ingestion into the project _Default bucket, while user application workloads running in custom namespaces continue to be ingested normally.resource.type="dataflow_step" where labels."dataflow.googleapis.com/log_type"!="system" AND labels."dataflow.googleapis.com/log_type"!="supportability" removes noisy, low-value worker thread execution output while guaranteeing that essential system lifecycle messages and supportability records needed by developers and Google support remain available in the Job details console.This approach directly stops the ingestion of non-critical, high-volume log streams at the Log Router level while precisely retaining all telemetry required for Day-2 cluster operations and pipeline diagnostics.
Configure an intercepting aggregated sink at the folder level routed directly to a central Cloud Storage bucket with an exclusion filter matching resource.type=("k8s_cluster" OR "dataflow_job").
Update the inclusion filter of the project-level _Default sinks to empty string (""), and set a 10% sample() exclusion rate across all resource.type values.
Create an organization-level non-intercepting aggregated sink routed to Pub/Sub with an inclusion filter for Dataflow and GKE logs, and disable the _Required sink in each underlying project.