본문으로 건너뛰기
cccskills
무료GitHub에 공개

cloud-logging-cross-project-configuration

Configure Google Cloud logs across multiple projects, either by storing them centrally or querying them together where they already reside. The skill guides routing, access permissions, and log views, and helps diagnose missing logs and permission failures.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Configure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.

연동 도구Google Cloud
설치 방법 보기

이럴 때 유용합니다

  • Collect logs from multiple projects in one central bucket
  • Query distributed logs without copying their data
  • Set log access boundaries by project or log type
  • Diagnose missing logs and routing permission failures

포함된 파일(1)

  • SKILL.md18.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Configuring Cross-Project Logging

This skill describes how to use gcloud commands to configure Cloud Logging so that you store log data in a central location, regardless of the point of origin. The skill also describes how to query log data when that data is stored in multiple projects.

[!IMPORTANT] Sandbox Network Limitation (CRITICAL for Agent Testing): During evaluation or in restricted sandboxed environments, network traffic to GCP APIs is blocked. Do NOT run network discovery commands to find resource names, project IDs, or organization IDs. Always use the exact project IDs or placeholders provided in the user prompt or instructions, for example, {project_id}, {source_project_id}, {central_project_id}. Assume these resources exist and proceed directly with configuration commands. Running these discovery commands will cause the execution to hang and timeout.

Safety and Confirmation Tiers (CRITICAL)

Before executing any commands on behalf of the user, you MUST adhere to the following safety tiers based on the action requested:

  1. Tier R: Read-Only
    • Description: Commands that only read state or query logs.
    • Example commands:
      • gcloud logging read
      • gcloud logging buckets list
    • Rule: No confirmation needed. You may execute these commands immediately to gather information.
  2. Tier M: Mutation (Non-Billing)
    • Description: Configuration modifications or free metadata creations that do not incur direct storage or billing costs and do not affect resource security/access policies.
    • Example commands:
      • gcloud logging views create
      • gcloud logging views update
      • gcloud logging scopes create
      • gcloud logging buckets create
    • Rule: No confirmation needed. You may execute these commands immediately to apply configurations.
  3. Tier B: Billing and Security-Sensitive Mutations (High-Risk)
    • Description: Operations that create billing-inducing resources or integrations, or modify security and IAM access control policies (presenting a risk of privilege escalation).
    • Example commands:
      • gcloud logging metrics create
      • gcloud logging links create
      • gcloud projects add-iam-policy-binding
    • Rule: Interactive confirmation required. These commands create resources that incur billing costs or alter security access. You MUST present the exact, literal command and receive user confirmation before executing. NEVER execute in the same turn as asking.
  4. Tier D: Causes irreversible data loss
    • Description: Actions that permanently discard or delete logs, for example sink exclusions.
    • Example commands:
      • gcloud logging buckets delete
      • gcloud logging sinks update --add-exclusion
    • Rule: Explicit typed confirmation required. These commands discard or delete logs immediately and irreversibly, or they may result in log data not being stored. You MUST ask for explicit typed confirmation, for example, "Yes, discard logs", and halt execution until the user replies.

Decision Matrix: Centralized Storage vs. Distributed Storage with Read-Time Aggregation

Use this decision matrix to evaluate and choose between Centralized Storage and Read-Time Aggregation. With centralized storage, log data is routed to one log bucket, regardless of where the data originates. You write queries against the centralized log bucket. With read-time aggregation, log data is stored by the resource where it originates. However, a single query aggregates the data by querying all resources.

After you have determined the optimal architecture for handling cross-project logs, follow the corresponding configuration steps detailed below.

CriterionCentralized StorageRead-Time Aggregation
GCP Project ScaleScales to thousands ofBest for < 375 projects.
: : projects. : :
Log StorageConsolidated in a singleResides in originating
: : log bucket. : resources. :
SQL AnalyticsEasy; unified queryingHard; requires querying
: : via Observability : multiple log buckets. :
: : Analytics. : :
Access ControlScoped access via logRequires IAM access to
: : views on the centralized : all views on resources :
: : log bucket. : that store log data. :
ConfigurationOptions vary based onWill not interfere with
: Complexity : Project, Folder, : bucket-based log-based :
: : Organization structure. : metrics. :
CostPotential for duplicateCost-effective; no data
: : storage of log buckets : replication. :
: : if exclusions aren't : :
: : set. : :

Architecture

Centralized Storage (Log Routing)

graph LR
    subgraph "Source Project(s)"
        Log[Resource Logs] --> Sink["Sink: route-to-central-project"]
    end

    subgraph "Central Project"
        Sink --> Bucket["Bucket: central-logs-bucket (us-central1)"]
    end

Read-Time Aggregation (Log Scopes)

graph LR
    subgraph "Source Project 1"
        Log1[Resource Logs] --> Bucket1["Bucket: _Default"]
    end

    subgraph "Source Project 2"
        Log2[Resource Logs] --> Bucket2["Bucket: _Default"]
    end

    subgraph "Scoping Project (No Log Storage)"
        Scope["Log scope: central-query-scope"]
        Scope -.-> View1["_AllLogs View on Bucket1"]
        Scope -.-> View2["_AllLogs View on Bucket2"]
    end

Setup Steps: Centralized Storage (Log Routing)

Use these steps to route logs from one or more source projects to a central log bucket in a project. Create or select the Google Cloud project that you will use for storing your log data. This is the central project.

1. Create log bucket in central project (Tier M)

Create a custom log bucket with Log Analytics enabled.

[Tip] Use regional log buckets, for example, set the location to us-central1. Don't use the global location. This approach ensures compatibility with Observability Analytics and SQL querying.

gcloud logging buckets create {bucket_id} \
    --project={central_project_id} \
    --location={region} \
    --retention-days={retention_days} \
    --enable-analytics

2. Create log sink in central project (Tier M)

Create a project-level sink in the central project pointing to the central log bucket. This sink routes logs that land in the central project's Log Router into the central log bucket.

Assuming the newly created bucket is specifically for centralizing data from other projects (and the central project is not one of the source projects), the central sink should exclude the central project's own logs. Include the _Required audit log exclusions on this central sink to avoid redundancy between {bucket_id} and the _Required bucket.

gcloud logging sinks create {sink_name} \
    logging.googleapis.com/projects/{central_project_id}/locations/{region}/buckets/{bucket_id} \
    --project={central_project_id} \
    --exclusion=filter='logName:"projects/{central_project_id}/"' \
    --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/activity")' \
    --exclusion=filter='LOG_ID("externalaudit.googleapis.com/activity")' \
    --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/system_event")' \
    --exclusion=filter='LOG_ID("externalaudit.googleapis.com/system_event")' \
    --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/access_transparency")' \
    --exclusion=filter='LOG_ID("externalaudit.googleapis.com/access_transparency")'

3. Create log sink in source resource (Tier M)

To route logs to the central project, you must create a log sink in each source organization, folder, or project. While you can configure a sink to route only a subset of logs using the --log-filter argument, recommended practice is to route all non-audit logs, and then restrict access or partition logs at the destination using custom Log Views on the centralized log bucket.

  • For an organization-level log sink

    gcloud logging sinks create {sink_name} \
        logging.googleapis.com/projects/{central_project_id} \
        --organization={source_organization_id} \
        --include-children \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/activity")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/activity")' \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/system_event")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/system_event")' \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/access_transparency")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/access_transparency")'
    
  • For project-level log sinks

    gcloud logging sinks create {sink_name} \
        logging.googleapis.com/projects/{central_project_id} \
        --project={source_project_id} \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/activity")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/activity")' \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/system_event")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/system_event")' \
        --exclusion=filter='LOG_ID("cloudaudit.googleapis.com/access_transparency")' \
        --exclusion=filter='LOG_ID("externalaudit.googleapis.com/access_transparency")'
    

4. Grant IAM permissions to sink writers (Tier B)

[!IMPORTANT] Security Action (Tier B): Granting IAM permissions changes access control policy and must be explicitly confirmed by the user before execution.

To allow the source sinks to route logs to the central project's router, and to allow the central sink to write logs to the central bucket:

  1. Grant Logs Writer permission to the source sink: Retrieve the writerIdentity of the source log sink and grant it roles/logging.logWriter on the central project.

    # Get the writer identity of the source sink
    gcloud logging sinks describe {sink_name} \
        --project={source_project_id} \
        --format="value(writerIdentity)"
    

    The output is the {source_writer_identity} value (for example serviceAccount:...) for the following command:

    # Grant Logs Writer permissions on the central project
    gcloud projects add-iam-policy-binding {central_project_id} \
        --member={source_writer_identity} \
        --role=roles/logging.logWriter
    
  2. Grant Bucket Writer permission to the central sink: Retrieve the writerIdentity of the central log sink and grant it roles/logging.bucketWriter on the central project.

    # Get the writer identity of the central sink
    gcloud logging sinks describe {central_sink_name} \
        --project={central_project_id} \
        --format="value(writerIdentity)"
    

    The output is the {central_writer_identity} value for the following command:

    # Grant Bucket Writer permissions on the central project
    gcloud projects add-iam-policy-binding {central_project_id} \
        --member={central_writer_identity} \
        --role=roles/logging.bucketWriter
    

5. Create custom Log Views on the central bucket (Tier M)

To partition logs or restrict access by log ID or project (since all logs were routed to the same central bucket), create custom Log Views on the central bucket.

  • Filter by log ID:

    gcloud logging views create {view_id} \
        --bucket={bucket_id} \
        --location={region} \
        --project={central_project_id} \
        --log-filter='LOG_ID("{log_id}")'
    
  • Filter by source project:

    gcloud logging views create {view_id} \
        --bucket={bucket_id} \
        --location={region} \
        --project={central_project_id} \
        --log-filter='project_id="{source_project_id}"'
    

Verify Centralized Log Routing (Tier R)

To verify that logs are being routed from the source projects to the central regional log bucket:

  1. Write a test log in the source project:

    gcloud logging write {test_log_id} "Test log entry for verification" \
        --severity=WARNING \
        --project={source_project_id}
    
  2. Read the test log from the central log bucket: For regional log buckets, you must specify the --view flag. Because the default view _Default only contains logs matching the default filter, you should query the _AllLogs view or your custom log view on the central bucket:

    gcloud logging read 'logName:"projects/{source_project_id}/logs/{test_log_id}"' \
        --bucket={bucket_id} \
        --location={region} \
        --view=_AllLogs \
        --project={central_project_id}
    

Setup Steps: Read-Time Aggregation (Log Scopes)

Create or select a Google Cloud project that you will use for querying your log data. This is the scoping project. Use these steps to configure a log scope. Log scopes let you issue a query for log data that is stored in multiple projects.

1. Create a custom Log View (Optional but recommended) (Tier M)

Create a Log View in the source project to restrict which logs are accessible.

[!IMPORTANT] Gotcha: Log View filters can only contain specific restrictions. Refer to https://docs.cloud.google.com/logging/docs/logs-views.md.txt#view-filter

gcloud logging views create {view_id} \
    --bucket={bucket_id} \
    --location={region} \
    --project={source_project_id} \
    --log-filter='LOG_ID("{log_id}")'
  • {bucket_id}: for example, _Default
  • {region}: for example, global
  • {view_id}: for example, app-logs-view
  • If you want to allow access to all logs in the log bucket, omit the --log-filter flag.

2. Create a log scope in the scoping project (Tier M)

Create a log scope in the scoping project listing the source resources, which may be projects or specific log views.

gcloud logging scopes create {log_scope_id} \
    --project={scoping_project_id} \
    --resource-names={resource_names}
  • {log_scope_id}: for example, central-query-scope
  • {resource_names}: Comma-separated list of log views. For example, projects/source-project-1/locations/global/buckets/_Default/views/app-logs-view,projects/source-project-2/locations/global/buckets/_Default/views/app-logs-view. You can include up to 100 views in the scope.

3. Update Default Observability Scope (Optional) (Tier M)

Link the log scope to the project's default observability scope so it is used by default in Logs Explorer.

gcloud observability scopes update _Default \
    --project={scoping_project_id} \
    --location=global \
    --log-scope=//logging.googleapis.com/projects/{scoping_project_id}/locations/global/logScopes/{log_scope_id}

4. Grant IAM permissions to users (Tier B)

Unlike centralized routing, permissions are checked at query time on all source projects. Users running queries must have:

  • roles/logging.viewAccessor granted on the specific Log View (with IAM conditions) or roles/logging.viewer on the source projects.
  • Access to the scoping project.

Troubleshooting Cross-Project Log Routing and Sink Permission Failures

If logs are not appearing in a central project's bucket after configuring centralized logging:

1. Verify Log Router Sink Filters in Source Resource

  • Ensure that the log sink's filter matches the logs you expect to route.

    [!IMPORTANT] Gotcha: Standard filter expressions like logName:abc or logName="projects/{project_id}/logs/abc" can fail to match in log sinks. Always use LOG_ID("abc") for precise matching in log sink filters.

  • Verify that you have not configured an exclusion filter that accidentally discards these logs.

2. Verify and Grant Writer Identity Permissions (Most Common Cause)

The log sink's writer identity service account in the source project must be explicitly granted the necessary permissions on the central resource.

  • Step A: Get the Writer Identity

    gcloud logging sinks describe {sink_name} \
        --project={source_project_id} \
        --format="value(writerIdentity)"
    
  • Step B: Grant the Permission in the Central Project (Tier B)

    [!IMPORTANT] Security Action (Tier B): Granting IAM permissions changes access control policy and must be explicitly confirmed by the user before execution.

    • For Cloud Logging Buckets (standard): Grant roles/logging.bucketWriter:

      gcloud projects add-iam-policy-binding {central_project_id} \
          --member={writer_identity} \
          --role=roles/logging.bucketWriter
      
    • For GCS Buckets: Grant roles/storage.objectCreator on the GCS bucket.

    • For Pub/Sub Topics: Grant roles/pubsub.publisher on the Pub/Sub topic.

    • For BigQuery Datasets: Grant roles/bigquery.dataEditor on the BigQuery dataset.


References and Supporting Links

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Create Terraform files that define alerts for AI agent latency, errors, token use, safety, security, and response quality. Check telemetry and existing policies first, then validate the generated configuration. Quality alerts require Vertex AI.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Configure alerts for agent latency and error rates
  • Monitor rapid increases in token usage
  • Monitor safety triggers and permission denials
google/skills2.1만2026년 10월 17일 업데이트

Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Choose a deployable model and supported hardware
  • Estimate hourly deployment costs before approval
  • Check deployment progress and test predictions
google/skills2.1만2026년 10월 17일 업데이트

Manages Agent Platform endpoints, the stable URLs used to serve online model predictions. It supports listing, inspecting, creating, updating, and deleting endpoints, plus troubleshooting permission errors, quota limits, and resources that are busy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Listing endpoints and checking their configuration
  • Creating an endpoint before model deployment
  • Updating endpoint display names or labels
google/skills2.1만2026년 10월 17일 업데이트

Evaluate AI models and agents on Google Cloud using test datasets and scoring criteria. Generate simulated user scenarios, analyze failures, and compare results across changes to guide improvements to prompts and code.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Build evaluation datasets from conversation logs
  • Generate simulated user scenarios
  • Choose scoring criteria for responses and tool use
google/skills2.1만2026년 10월 17일 업데이트

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Send a test prompt to Gemini
  • Call Llama or DeepSeek with the OpenAI SDK
  • Write code to call an existing custom endpoint
google/skills2.1만2026년 10월 17일 업데이트

Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Moving a Gemini application to Google Cloud
  • Using Google Cloud credits for Gemini API calls
  • Unifying agent authentication and billing
google/skills2.1만2026년 10월 17일 업데이트

google의 스킬 모두 보기

이 스킬의 문제 신고