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iew or delete revisions traffic migration gradual rollouts rollbacks configure services overview capacity memory limits cpu limits gpu gpu configuration gpu performance best practices request timeout maximum concurrent requests about maximum concurrent requests per instance configure maximum concurrent requests billing optimize service configurations with recommender environment container port and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes cifs smb ephemeral disk execution environment sandboxes container health checks http 2 requests secrets service identity scaling about instance autoscaling for services maximum instances about maximum instances for services configure maximum instances minimum instances configure custom scaling controls manual scaling metadata description labels tags source deploy configurations supported language runtimes and base images configure automatic base image updates build environment variables build service account build worker pools invoke and trigger services invoke with https requests host a webhook target stream with websockets overview build a websocket chat service tutorial invoke asynchronously invoke services on a schedule create a workflow invoke services as part of a workflow connect a series of services from cloud functions and cloud run tutorial execute asynchronous tasks call a service from a pub sub push subscription trigger service from pub sub integrate image processing into pub sub sample tutorial trigger from events create triggers with eventarc pub sub triggers create pub sub eventarc triggers trigger functions from pub sub using eventarc trigger functions from routed log entries cloud storage triggers create triggers with cloud storage trigger services from cloud storage using eventarc trigger functions from cloud storage using eventarc firestore triggers create triggers with firestore trigger functions from events in a firestore database connect with other services using grpc best practices general development tips for services cost optimization optimize java services optimize python services optimize node js services load testing best practices understand zonal redundancy functions best practices overview configure event driven function retries execute job tasks to completion create jobs execute jobs execute jobs execute scheduled jobs execute jobs from workflows configure jobs container entrypoint cpu limits memory limits gpu gpu configuration gpu best practices environment variables container health checks volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk labels maximum retries parallelism secrets service identity task timeout tags manage jobs view or delete jobs view or stop job executions best practices jobs retries and checkpoints cost optimization perform continuous background work deploy worker pools deploy worker pools deploy worker pools from source code manage worker pools view or delete worker pools view or delete worker pool revisions instance splits and rollbacks configure worker pools capacity memory limits cpu limits gpu gpu configuration gpu best practices environment container and entrypoint environment variables volume mounts cloud storage volumes nfs volumes in memory volumes using cifs smb network file systems ephemeral disk container health checks secrets service identity instance count metadata description labels scale based on external metrics autoscale worker pools with external metrics kafka autoscaler host github runners with worker pools autoscale worker pools based on prometheus metrics autoscale worker pools with pub sub pull subscriptions automate scaling with workflows cost optimization configure networking best practices for cloud run networking configure private networking send traffic to vpc network overview direct vpc register private ips for worker pools using cloud dns dual stack ipv4 and ipv6 migrate standard vpc connector to direct vpc vpc connectors send traffic to shared vpc network overview direct vpc migrate shared vpc connector to direct vpc connectors in service projects connectors in host project static outbound ip address network security restrict endpoint ingress services use vpc service controls vpc sc cloud service mesh secure security design overview authenticate requests overview allow public access custom audiences authenticate developers service to service authenticate users end user authentication tutorial secure your resources access control with iam configure iap for cloud run introduction to service identity protect services with cloud armor use binary authorization use cloud run threat detection use customer managed encryption keys manage custom constraints for projects view software supply chain security insights secure cloud run services tutorial multi tenant platforms running untrusted code monitor and log monitoring and logging overview view built in metrics write prometheus metrics write opentelemetry metrics log and view logs audit logging error reporting use distributed tracing for services run ai solutions overview explore resources ai agents overview build and deploy a2a agents overview deploy a2a agents build and deploy adk agents build and deploy n8n agents mcp servers overview build and deploy a remote mcp server tools code execution browser automation inference with gpus overview services run llm inference on cloud run gpus with ollama run agents with gemma 4 models on cloud run run opencv on cloud run with gpu acceleration run llm inference on cloud run gpus with hugging face transformers js jobs fine tune llms using gpus with cloud run jobs run batch inference using gpus with cloud run jobs gpu accelerated video transcoding with ffmpeg ai assisted development and vibe coding introduction to cloud run for ai assisted developers cookbook migrate an existing web service from app engine from cloud run functions 1st gen from aws lambda from heroku from cloud foundry migration overview choose an oci compliant strategy migrate to oci containers migrate configuration sample migration spring music from vmware tanzu from a vm using migrate to containers from kubernetes to gke troubleshoot introduction troubleshoot errors local troubleshooting tutorial known issues samples all cloud run code samples all cloud run functions code samples code samples for all products ai and ml application development application hosting compute data analytics and pipelines databases distributed hybrid and multicloud industry solutions migration networking observability and monitoring security storage access and resources management costs and usage management infrastructure as code sdk languages frameworks and tools home documentation application hosting cloud run guides send feedback logging and viewing logs in cloud run stay organized with collections save and categorize content based on your preferences this page describes the logs available when using cloud run and how to view and write logs cloud run has several types of logs and these are automatically sent to cloud logging request logs services only logs of requests sent to cloud run services these logs are created automatically container logs services jobs and worker pools logs emitted from the instances typically from your own code written to supported locations as described in writing container logs system logs services jobs and worker pools platform generated logs containing information about your workloads these logs are written to varlog system view logs you can view logs for your service job or worker pool in several ways use the cloud run page in the google cloud console use google cloud cli to view logs using gcloud services only use cloud logging logs explorer in the google cloud console use cloud code services only both of the console methods of viewing logs examine the same logs stored in cloud logging but the cloud logging logs explorer provides more details and more filtering capabilities view logs in cloud run you can view logs in the cloud run page for the following resources services jobs worker pools view logs for a service in the google cloud console go to the cloud run services page go to cloud run click the service in the displayed list click the logs tab to get the request and container logs for all revisions of this service you can filter by log severity level view logs for a job to view job logs in the cloud run page in the google cloud console go to the cloud run jobs page go to cloud run locate the job in the jobs list and click on it click the logs tab to get the container logs for all executions of this job you can filter by log severity level alternatively if you want to see the logs pre filtered for a specific job execution click on the job execution and then click the logs tab view logs for a worker pool to view worker pool logs in the cloud run page in the google cloud console go to the cloud run worker pools page go to cloud run click the worker pool in the displayed list click the logs tab to get the request and container logs for all revisions of this worker pool you can filter by log severity level view service logs using google cloud cli you can use google cloud cli to view tailing logs or read existing logs for a cloud run service in the command line by default the logs are formatted in a single line format optimized for the console to tail logs you need to install the log streaming component in google cloud cli if the component isn t installed you will be prompted to install it when required view tailing logs in the command line preview command line log tail for cloud run services this feature is subject to the pre ga offerings terms in the general service terms section of the service specific terms pre ga features are available as is and might have limited support for more information see the launch stage descriptions for a cloud run service you can tail logs in real time from your cloud run service directly in the command line gcloud beta run services logs tail service project project id replace the following service the name of the cloud run service project id the google cloud project id you can view your project id by running the command gcloud config get value project read logs in the command line for a cloud run service you can read existing logs in either of two ways in a console optimized format gcloud run services logs read service limit 10 project project id directly from cloud logging gcloud logging read resource type cloud_run_revision and resource labels service_name service project project id limit 10 replace the following service the name of the cloud run service project id the google cloud project id you can view your project id by running the command gcloud config get value project view logs in cloud logging note selecting a specific worker pool in the log fields pane is not implemented to filter for a specific worker pool add the following to the query resource labels worker_pool_name worker_pool_name to view your cloud run logs in the cloud logging logs explorer go to the logs explorer page in the google cloud console go to the logs explorer page select an existing google cloud project at the top of the page or create a new project using the drop down menus select the resource cloud run revision for a service cloud run job for a job cloud run worker pool for a worker pool for more information see using the logs explorer view service logs in cloud code to view your logs in cloud code read the intellij and visual studio code guides read logs programmatically if you want to read the logs programmatically you can use one of these methods use a log sink to pub sub and a script to pull from pub sub call the logging api through the client libraries for your programming language call the logging api rest endpoints directly instance scaling logs format and contents when new instances start for your cloud run service cloud logging includes log entries under the varlog system log name explaining why each instance was created the log entry follows this format starting new instance reason reason description the following table provides a breakdown of instance descriptions reason description manual_or_customer_min_instance instance started because of customer configured minimum instances or manual scaling autoscaling instance started due to configured scaling factors such as cpu utilization request throughput and so forth or not enough existing capacity for current traffic deployment_rollout instance started due to traffic shifting between revisions due to deployment traffic split adjustment or deployment health check write container logs when you write logs from your service job or worker pool they will be picked up automatically by cloud logging so long as the logs are written to any of these locations standard output stdout or standard error stderr streams any files under the var log directory syslog dev log logs written using cloud logging client libraries which are available for many popular languages most developers are expected to write logs using standard output and standard error the container logs written to these supported locations are automatically associated with the cloud run service revision and location the cloud run worker pool revision and location or with the cloud run job exceptions contained in these logs are captured by and reported in error reporting the integrated logging balances reliability and resource usage and should work for most applications writing log entries using integrated logging does not consume quota for the number of entries write requests per minute of the cloud logging api if your application has requirements for higher volume or reliability we recommend using the cloud logging api directly either as a library within your application or as a separate sidecar container use simple text vs structured json in logs when you write logs you can send a simple text string or send a single line of serialized json also called structured data this is picked up and parsed by cloud logging and is placed into jsonpayload in contrast the simple text message is placed in textpayload write structured logs the following snippet shows how to write structured log entries it also shows how to correlate log messages with the corresponding request log node js uncomment and populate this variable in your code const project the project id of your function or cloud run service build structured log messages as an object const globallogfields add log correlation to nest all log messages beneath request log in log viewer this only works for http based invocations where req is defined if typeof req undefined const traceheader req header x cloud trace context if traceheader project const trace traceheader split globallogfields logging googleapis com trace projects project traces trace complete a structured log entry const entry object assign severity notice message this is the default di...