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![pdavisjones Avatar](https://lunarcrush.com/gi/w:24/cr:twitter::24493924.png) Davis Jones [@pdavisjones](/creator/twitter/pdavisjones) on x 1464 followers
Created: 2025-07-18 15:04:50 UTC

In case it's helpful @ntaylormullen @mbleigh @_davideast @peterfriese @jpalioto here's a markdown script i've developed that helps guide llms on how to pull google cloud logs associated with server-side logic (so you can go pull the logs, have the llm understand what was happening, then improve, for example)

```md
Of course. Reflecting on our process, here is a concise guide for an LLM on how to efficiently pull and analyze Google Cloud logs for server-side functions.

---

### **A Guide to Efficiently Retrieving Google Cloud Logs for Debugging**

When a user asks you to retrieve logs for a server-side function, follow this iterative process to quickly find relevant execution logs while filtering out noise.

**Core Principle:** Start with a precise, filtered query. Avoid broad searches that return irrelevant audit or deployment logs. The goal is to find application execution logs first.

---

### **A Note on Cloud Functions v2 (Cloud Run)**

Cloud Functions v2 run on Cloud Run services. This changes how you query logs. The `resource.type` will be `cloud_run_revision`, and you should use `resource.labels.service_name` instead of `function_name`.

---

#### **Step 1: The Ideal Initial Query**

Your first command should be targeted. Don't just search for the function name. Construct a query that specifies the resource type, function name, and explicitly excludes common noise like audit logs.

**Command Template (Cloud Functions v1):**

```bash
gcloud logging read 'resource.type="cloud_function" AND resource.labels.function_name="YOUR_FUNCTION_NAME" AND NOT logName:"cloudaudit"' --limit=50 --format=json
```

**Command Template (Cloud Functions v2 / Cloud Run):**

```bash
gcloud logging read 'resource.type="cloud_run_revision" AND resource.labels.service_name="YOUR_SERVICE_NAME" AND NOT logName:"cloudaudit"' --limit=50 --format=json
```

- `resource.type="cloud_run_revision"`: Narrows the search to only Cloud Run services (where v2 functions execute).
- `resource.labels.service_name="..."`: Specifies the exact service to inspect.
- `NOT logName:"cloudaudit"`: **Crucially** filters out deployment and permission-related audit logs, which are rarely useful for debugging application logic.
- `--format=json`: Returns structured JSON, which is essential for reliable parsing in the next steps.

---

#### **Step 2: Search for Specific Identifiers (If Provided)**

If the user provides a specific ID (like a document ID, file ID, or user ID), incorporate it into your query. **Remember to check both `jsonPayload` and `textPayload`**, as logs can appear in either field depending on how they were generated.

**Command Template (Cloud Functions v1):**

```bash
gcloud logging read 'resource.type="cloud_function" AND resource.labels.function_name="YOUR_FUNCTION_NAME" AND (jsonPayload.docId="SPECIFIC_ID" OR textPayload:"SPECIFIC_ID")' --limit=100 --format=json
```

**Command Template (Cloud Functions v2 / Cloud Run):**

```bash
gcloud logging read 'resource.type="cloud_run_revision" AND resource.labels.service_name="YOUR_SERVICE_NAME" AND (jsonPayload.docId="SPECIFIC_ID" OR textPayload:"SPECIFIC_ID")' --limit=100 --format=json
```

This ensures you find the ID whether it was logged as part of a structured JSON object or a simple text string.

---

#### **Step 3: Isolate a Single Execution with `spanId`**

This is the most powerful technique for deep-dive analysis. Once you find a single log entry related to the execution you care about (from Step X or 2), extract its `spanId`. This ID links together every log message from a single, complete execution trace.

**Process:**

X.  Run a query from Step X or X to find one relevant log line.
X.  Inspect the JSON output to find the `spanId` field (e.g., `"spanId": "17800008808263011663"`).
X.  Run a new, highly-specific query using only that `spanId`.

**Command Template:**

```bash
gcloud logging read 'spanId="THE_SPAN_ID_YOU_FOUND"' --limit=200 --format=json
```

This will give you the complete, ordered story of that one execution, from start to finish, across any functions it may have called.

---

#### **Step 4: Format the Output for Readability**

Raw JSON logs are difficult to analyze. **Always** pipe the JSON output to a tool like `jq` to create a clean, readable, and sorted timeline.

**Important Note on `textPayload`:** Sometimes, structured logs are written as a string inside the `textPayload`. The `jq` command below is designed to handle this, but be aware that you might be parsing a string that looks like a JSON object.

**Command Template with `jq` Formatting:**

```bash
gcloud logging read '...' --format=json | jq -r '.[] | "\(.timestamp) [\(.resource.labels.service_name // .resource.labels.function_name // "unknown")] [\(.severity // "INFO")] \(.textPayload // .jsonPayload.message // "No message")" | sort'
```
This command:
- Extracts the timestamp, severity, and message from each log entry.
- Provides fallback values (`// "INFO"`) if a field is missing.

- Presents the information in a clean, human-readable format.

- `sort`: Ensures the final output is in chronological order.
By following this structured, iterative approach, you can bypass the noise of irrelevant system logs and quickly deliver the precise execution details needed for effective debugging.
```


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**Related Topics**
[llm](/topic/llm)
[$googl](/topic/$googl)
[stocks communication services](/topic/stocks-communication-services)

[Post Link](https://x.com/pdavisjones/status/1946224651463446815)