Friday, 27 February 2026

GCP Console — Production Log Analysis (step-by-step)

Using Claude.ai Cursor for conversational / LLM-assisted analysis

This article shows a practical, end-to-end workflow for investigating production logs from Google Cloud Console (Cloud Logging / Log Explorer), exporting them, and using Claude.ai Cursor to query, summarize, and produce actionable findings. It’s written as a sequence of clear steps you can follow now.

1) Goal & quick summary

Goal: quickly find, explore, and analyze production issues using GCP Log Explorer, export the logs you need (e.g., to BigQuery or CSV), then use Claude.ai Cursor to ask natural-language questions, detect anomalies, generate summaries, and produce runbook-style recommendations.

High-level flow:

Identify logs in GCP Console → filter with Logging Query Language (LQL).
Export/save relevant log slices (BigQuery sink or CSV).
Use Claude.ai Cursor to load the data (or connect to BigQuery) and interactively analyze it with prompts and code cells.
Produce findings, visualizations, and suggested remediation steps.

2) Prerequisites & access

GCP project access with Logging Viewer (or higher) role for the target project. For exports, Logs Configuration Writer or BigQuery Data Editor permissions may be required.
Cloud Logging (formerly Stackdriver Logging) is enabled and your services are writing logs.
A Claude.ai account with Cursor enabled (ability to connect/upload files or to connect to BigQuery / cloud storage).
Optional: BigQuery dataset to receive exported logs, or permission to download CSVs from Log Explorer.

3) Step A — Narrow down logs in GCP Console (Log Explorer)

Open Cloud Console → Navigation menu → Logging → Log Explorer.
Set the project (top-left) to the production project.
Choose a time range (top-right). Start wide (last 24 hrs) then narrow to the window of the incident.
Use the resource and log filters:
- Resource: e.g., Kubernetes Container, GCE VM Instance, Cloud Run Revision, Cloud Function.
- Log name: application logs, stdout, stderr, requests, or syslog.

Build an LQL query (examples below). Use PROP: "value" filters and severity:

Example — errors for a service:


resource.type="k8s_container"
resource.labels.namespace_name="prod"
logName="projects/PROJECT_ID/logs/stdout"
severity>=ERROR

Example — 500s in an HTTP server (if structured):


jsonPayload.status>=500
resource.type="cloud_run_revision"

Run the query, inspect sample log entries on the right. Use the Expand pane to view full JSON payloads.

4) Step B — Refine & extract fields

Use field extraction on the Log Explorer: click the JSON payload and copy or add a derived field (e.g., user_id, trace, request_id, latency_ms).
Use PARSE functions or REGEXP_EXTRACT in the Logging Query Language to pull structured fields from unstructured text when needed.

Example of extracting a numeric latency from jsonPayload:


jsonPayload.latencyMs = CAST(REGEXP_EXTRACT(textPayload, r"latency=(\d+)") AS INT64)

(Exact functions depend on whether you're exporting to BigQuery or using LQL features.)

5) Step C — Export logs for deeper analysis

You have two main options:

Option 1 — Export to BigQuery (recommended for large-scale analysis)

In Log Explorer, click Create export (or go to Logging → Logs Router).
Create a sink:
- Sink service: BigQuery dataset.
- Choose filter: the LQL you refined above (only export relevant logs).
- Destination dataset: your_project.your_dataset.logs_prod.
Confirm and create the sink. Logs matching the filter will be streamed into the BigQuery table (append).

Advantages: scalable, fast SQL queries, works well with Cursor if Cursor can connect to BigQuery (recommended).

Option 2 — Download a CSV / JSON from Log Explorer (ad-hoc)

From Log Explorer results, click Download → CSV or JSON for the current query/time range.
This is suitable for small slices or immediate one-off investigations.

6) Step D — Prepare data for Claude.ai Cursor

If you exported to BigQuery, note the table name and ensure Cursor can connect (or you can export a table snapshot to CSV).
If using CSV/JSON, upload it into Claude.ai Cursor (Cursor supports file upload and interactive code cells).
Clean data as required: convert timestamps, parse fields, remove PII (mask user identifiers), and sample if dataset is huge.

7) Step E — Use Claude.ai Cursor: practical examples & prompt templates

Below are concrete prompts and examples you can paste into Claude.ai Cursor. Treat Cursor like an analyst: show it the table/CSV or give it a BigQuery connection plus the table name.

A) Quick human-readable summary

Prompt

I uploaded prod_logs_2026-02-26.csv. Give me a short summary of the main error types, top affected services, and any spikes in errors over time. Show counts by error type and by service and produce a 3-line executive summary.

B) Find top offending requests

Prompt

In the dataset, find the top 10 request_ids that produced the most ERROR or CRITICAL entries. For each request_id, list the sequence of log messages ordered by timestamp.

C) Anomaly detection for latency

Prompt

Use the latency_ms field. Detect outliers and periods with sustained latency > 2× median. Provide a time series plot and list time windows with the highest average latency, with candidate root causes from available fields (service, instance, region).

D) Create an alerting metric recommendation

Prompt

Based on the error rate and latency patterns, recommend two actionable logs-based metrics and sample alerting thresholds for production. Explain why and include suggested alert descriptions.

E) Build a runbook-style remediation

Prompt

For the most frequent error NullPointerException in PaymentProcessor.process, propose a step-by-step troubleshooting runbook: initial checks, logs to inspect (including exact LQL queries), quick mitigations, and safe rollback steps.

F) BigQuery SQL ask (if Cursor can run SQL or you prefer to run it yourself)

Sample SQL to get error counts per service per hour:


SELECT
  service,
  TIMESTAMP_TRUNC(timestamp, HOUR) AS hour,
  COUNTIF(severity >= "ERROR") AS errors,
  COUNT(*) AS total
FROM `project.dataset.logs_prod`
GROUP BY service, hour
ORDER BY hour DESC
LIMIT 1000;

You can paste this into BigQuery or ask Cursor to run it if it has access.

8) Example LQL snippets (to use directly in GCP Log Explorer)

Errors for a microservice in prod:


resource.type="k8s_container"
resource.labels.namespace_name="prod"
resource.labels.container_name="payments-service"
severity>=ERROR

HTTP 5xx in Cloud Run (structured JSON):


resource.type="cloud_run_revision"
jsonPayload.httpStatus >= 500

9) Putting findings into action

Short-term: create logs-based alerting policies or temporary scaling rules; pin a hotfix and monitor behavior post-deploy.
Mid-term: export logs to BigQuery and build dashboard queries (error trends, latency percentiles). Use logs-based metrics for SLO-based alerts.
Long-term: ensure structured logging across services, consistent correlation IDs / traces, and centralized log retention & sampling policies.

10) Security, cost & best practices

Permissions: restrict Log Router and BigQuery sink creation to ops/security engineers.
PII: mask or remove PII before exporting to external tools / LLMs. If using Claude.ai, avoid sending raw PII unless you explicitly sanitize.
Retention & cost: exporting high-volume logs to BigQuery can be costly. Use filter-based sinks to export only what you need. Consider sampling for debug logs.
Structured logging: prefer JSON structured logs (jsonPayload) with request_id, trace, service, region, latency_ms so queries are easier.
Trace linkage: capture trace and span_id to tie logs to traces (Cloud Trace) for distributed tracing.

11) Example end-to-end mini playbook (concise)

In Cloud Console → Log Explorer, filter: resource=prod, severity>=ERROR, last 1 hour.
If the volume is manageable, download JSON; otherwise set a BigQuery sink with that filter.
In Claude.ai Cursor: upload the JSON or connect to BigQuery table.
Ask Cursor: “Show me top 5 error messages, top services, and a 10-minute error-rate time series.”
Use Cursor outputs to identify suspect service/instance/time window. Extract the trace or request_id.
Run a targeted LQL to fetch full request lifecycles.
Make a temporary alert (Logs → Metrics → Create Metric → Create Alerting Policy).
Draft a short incident report and runbook using Cursor (ask it to create an incident summary and stepwise mitigation).

12) Sample prompts you can copy-paste into Cursor

“Summarize this table logs_prod with top 10 error messages, counts, and the earliest/latest timestamp for each message.”
“For the error ‘DBConnectionTimeout’, list the instance IDs and the average CPU utilization and network I/O in the 5 minutes before the errors.” (If you include those fields or connect Cursor to metrics.)
“Draft a one-page incident postmortem with timeline, root cause hypothesis, corrective actions, and owners based on these logs.”

13) Checklist before sharing results externally

Remove PII and sensitive tokens.
Confirm the timezones used in timestamps (store and present in UTC or local consistently).
Attach LQL/SQL queries used to generate findings so others can reproduce.

14) Closing tips

Start with small, well-scoped queries. Iteratively expand.
Use BigQuery if you plan repeated or complex analyses. BigQuery + Cursor (or Cursor file uploads) is a powerful combo.
Use Claude.ai Cursor for natural language exploration, summarization, and to generate runbooks/alerts — but always validate any suggested remediation with engineers before acting.

AWS production log analysis with Claude in Cursor — a step-by-step guide

Goal: let Claude (Anthropic) help you explore, summarize, triage, and root-cause production logs from AWS while working inside the Cursor IDE (or a Cursor + Claude workflow). This guide assumes you have an AWS production environment that emits logs to CloudWatch / S3 and that you can configure Cursor to use an Anthropic API key (or use a Cursor extension that exposes Claude).

Quick architecture overview (what you build)

Log sources: EC2 / ECS / EKS application logs, Lambda logs, ALB/ELB access logs, RDS logs, CloudTrail, VPC Flow Logs.
Collection / centralization: CloudWatch Logs (native), Kinesis Data Streams / Firehose into S3, or direct delivery (ALB → S3).
Indexing & query layer (optional but recommended): CloudWatch Logs Insights for immediate queries; send long-term logs to S3 + Athena / OpenSearch for powerful searches.
Preprocessing / enrichment: Lambda / Glue jobs to parse JSON, enrich with metadata (service, pod, trace-id), and redact secrets.
Cursor + Claude: connect Cursor to an Anthropic API key or install a Cursor-Claude extension so you can paste query results, open log snippets, or stream structured samples to Claude for summarization and RCA.

Step 1 — Gather logs (fast, low friction)

For application logs already in CloudWatch Logs, open CloudWatch → Log groups.
For access logs that write to S3 (ALB/NLB), ensure the target S3 bucket has lifecycle rules for retention.
If you want a streaming pipeline: configure Kinesis Data Firehose to deliver to S3 (Parquet/JSON) and optionally to OpenSearch / Splunk.

Why: CloudWatch Logs gives instant ad-hoc querying; S3 + Athena/Glue is cheaper for long-term analytics.

Step 2 — Prepare a secure sample set to send to Claude

Important security note: Do not send PII, secrets, auth tokens, or production credentials to any external LLM without enterprise agreements and data handling policies. Redact or anonymize values (user IDs, IPs, emails, tokens) before sending. If you must send PII for authorized internal use, ensure your Anthropic contract and Cursor deployment are approved. (I’m assuming you’ll redact locally first.)

Redaction pattern examples (simple):

Replace emails: s/[\w.+-]+@[\w-]+\.[\w.-]+/[REDACTED_EMAIL]/g
Replace IPs: s/\b\d{1,3}(\.\d{1,3}){3}\b/[REDACTED_IP]/g
Replace UUIDs/IDs: s/[0-9a-fA-F-]{8,36}/[REDACTED_ID]/g

Step 3 — Extract useful slices (what to send)

When you ask an LLM to analyze logs, smaller high-value slices work best. Create extracts like:

A timeline: the last N minutes of logs from the affected service (sorted).
One example error trace (full stack) with surrounding 50 lines context.
Aggregated counts: top 10 error messages with counts, top 10 responding endpoints latencies > X ms.
Correlation keys: logs that share the same trace-id or request-id.

Example CloudWatch Logs Insights queries:


# errors in last 15 minutes
fields @timestamp, @message, service, traceId
| filter @message like /ERROR/ or @message like /Exception/
| sort @timestamp desc
| limit 200

Or aggregated:


fields bin(5m) as period, count(*) as hits, count_distinct(traceId) as traces
| filter @message like /ERROR/
| stats sum(hits) by period

Run the query, export top results to a file (CSV / JSON), redact, and copy into Cursor.

Step 4 — Configure Cursor to use Claude (quick)

Option A — Cursor built-in model selection: add your Anthropic API key in Cursor settings → Models → Anthropic / Claude model entry. Choose the model you prefer (Claude Opus/Claude Code variants).

Option B — Cursor extension: install a community Cursor-Claude extension if your workspace allows (some companies use internal installs). Example repos and packages exist that show how to install an Anthropic extension into Cursor. Always prefer official options where available.

Step 5 — Prompts & interactions: how to ask Claude to analyze logs

Below are practical, reusable prompt templates. Paste one into Cursor’s Claude chat or the extension, then paste the redacted log snippet.

Template A — Quick summary


I’m pasting a redacted log sample from production for service "payments". Please:
1) Give a short summary of what’s happening (2–3 sentences).
2) List the most likely root causes (ranked).
3) Suggest 3 next troubleshooting steps I should run (commands or queries).
Now here's the redacted log snippet:
-----
<paste logs>
-----

Template B — Correlate traces & explain


I have multiple log lines that share traceId = 12345-abc. Summarize the timeline of events for this trace in plain English, highlight errors, and map which service/component likely introduced the error. Provide a one-paragraph RCA hypothesis and 4 tactical next steps.
<redacted trace logs>

Template C — Generate CloudWatch Insights queries


Given these sample logs and the problem (e.g., "intermittent 502s from /api/checkout"), produce a CloudWatch Logs Insights query to:
- show top endpoints returning 5xx in last 30 minutes,
- group by availability zone,
- show counts and 95th percentile latency where present.
Also provide a short explanation for each part of the query.
<sample log schema: timestamp, @message, statusCode, path, latencyMs, az>

Step 6 — Example workflow (hands-on)

Run CloudWatch Insights to get the top 200 ERROR lines for payments in the last 15 minutes. Export JSON.
Run a local redaction script (simple Python or sed) to hide IPs, emails, tokens.
Open Cursor → start a new Claude chat → paste this prompt (Template A) + the redacted sample.
Ask Claude follow-ups: “Which log lines show latency increase before the error?” or “write a BASH snippet that fetches full logs for traceId X from CloudWatch via awscli.”
Use Claude’s answer to craft next CloudWatch queries or to produce a short incident summary for Slack / PagerDuty.

Step 7 — Automating parts of the flow

You can automate repeatable steps while keeping human-in-the-loop controls:

Lambda / Step Functions: when CloudWatch Alarm fires, a Step Function extracts a 5-minute log window, runs a redaction Lambda, stores the sample in S3, and notifies a human to paste into Cursor/Claude.
Notebook + Cursor: use a Jupyter notebook (or Cursor code cells) that runs boto3 to fetch logs, runs redaction, and then opens a prompt template prefilled in Cursor.
ChatOps: generate an incident summary draft automatically with Claude, then require human approval before sending to Slack.

Step 8 — Example concrete commands

Fetch logs by traceId with awscli:


# Get log streams for group, then filter for traceId
aws logs filter-log-events \
  --log-group-name "/aws/ecs/payments" \
  --start-time $(($(date +%s -d '15 minutes ago')*1000)) \
  --filter-pattern '"traceId":"12345-abc"'

Export CloudWatch Insights query results to S3 (via console or SDK), then redact locally and paste into Cursor.

Step 9 — What Claude is good at here (and what to avoid)

Good at:

Summarizing large, messy log snippets into a human-readable timeline.
Producing suggested queries, investigative steps, and hypothesis generation.
Drafting incident summaries, runbooks, and remediation checklists.

Not good at / be cautious:

Blindly trusting any LLM RCA — always verify with observability, metrics, and traces.
Sending unredacted PII or sensitive logs to a third-party model without approvals.
Replacing structured alerting / runbook automation with ad-hoc LLM prompts.

Step 10 — Ops, costs, and governance

Cost: API calls to Claude have cost per token. Keep samples small and structured (aggregate + representative examples) instead of sending everything.
Retention & compliance: ensure logs sent to Claude comply with your company’s data handling and any regulatory rules (GDPR, PCI, etc.).
Access control: only allow approved engineers to use the Anthropic key in Cursor. Rotate keys and audit usage logs.

Appendix — Example prompts & followups (copy/paste ready)

Short RCA prompt


Describe the sequence of events in these logs (redacted) and provide a one-sentence root cause hypothesis plus three immediate remediation steps. Only use evidence present in the logs and mark any assumptions.
<redacted logs>

Ask for a query


Write a CloudWatch Logs Insights query that shows the top 10 error messages and the number of unique traces for each in the last 1 hour.

Follow-up to Claude


List the exact awscli commands I should run next to fetch full traces for the top 3 traceIds you identified above.

Final checklist before you send a snippet to Claude

Redact PII & secrets.
Include minimal context: service name, time window, an example traceId.
Attach or paste only focused extracts (timeline + example error).
Keep a human reviewer in the loop for any suggested remediation that touches production.

Closing notes / recommended next steps

Start by manually pasting 1–2 redacted log snippets into Cursor/Claude to observe quality of answers.
Build a safe redaction pipeline (Lambda or CI script).
If the approach is useful, automate extraction + human approval and add audit logging for compliance.

If you want, I can:

provide a ready-to-use redaction script (Python) that matches your log format, or
draft a few CloudWatch Insights queries tailored to your service (tell me the field names you have: e.g., statusCode, path, latencyMs, traceId) — I’ll generate them right away.

AWS production log analysis using Claude.ai inside IntelliJ — step-by-step

Short version: collect the logs from AWS (CloudWatch / S3), open them in an IntelliJ project, install a Claude/Claude-Code plugin for JetBrains, and use Claude interactively to parse, summarize, write queries, and triage root cause. Below is a practical, reproducible step-by-step guide with code snippets, CloudWatch examples, and ready-to-paste prompts you can use inside the IDE.

Note: there are several IntelliJ plugins that bring Anthropic/Claude functionality into JetBrains IDEs (official and community plugins). Pick one you trust and read its permissions before installing.

1) Plan & safety checklist (do this first)

Never store prod credentials in source code. Use IAM roles, temporary credentials, or an encrypted secrets store (AWS Secrets Manager / SSM Parameter Store / Vault).
Work on redacted or sampled production logs if possible (PII, tokens, IPs).
Ensure your organization’s policy allows sending excerpts to external AI services — redact or anonymize anything you cannot transmit.
Make a small sample of logs (1–10 MB) for initial exploration to avoid cost and leakage.

2) Get the logs out of AWS (options)

You’ll usually choose one of these:

A. CloudWatch Logs Insights — run queries interactively and export results. Good for ad-hoc queries.
B. Export to S3 — for bulk analysis (historical, large datasets).
C. Kinesis / Lambda — for streaming analysis (near real time).

Example CloudWatch Insights query (find errors in last 24 hours):


fields @timestamp, @message, @logStream
| filter @message like /(?i)error|exception|traceback/
| sort @timestamp desc
| limit 200

If you prefer to pull logs programmatically, use the AWS SDK. Example Python (boto3) to run an Insights query and download results:


# save as fetch_cw_insights.py
import boto3, time, csv

client = boto3.client("logs", region_name="ap-south-1")  # change region

def run_insights_query(log_group_names, query_string, start, end):
    resp = client.start_query(
        logGroupNames=log_group_names,
        startTime=int(start),
        endTime=int(end),
        queryString=query_string,
        limit=1000
    )
    qid = resp["queryId"]
    while True:
        r = client.get_query_results(queryId=qid)
        if r["status"] in ("Complete", "Failed", "Cancelled"):
            break
        time.sleep(1)
    return r

if __name__ == "__main__":
    from datetime import datetime, timedelta
    end = int(datetime.utcnow().timestamp())
    start = int((datetime.utcnow() - timedelta(hours=24)).timestamp())
    q = 'fields @timestamp, @message | filter @message like /error/ | sort @timestamp desc | limit 100'
    res = run_insights_query(["/aws/lambda/my-prod-func"], q, start, end)
    # write messages to CSV
    with open("cw_insights.csv","w", newline="") as f:
        w = csv.writer(f)
        w.writerow(["timestamp","message"])
        for row in res.get("results", []):
            ts = next((r["value"] for r in row if r["field"]=="@timestamp"), "")
            msg = next((r["value"] for r in row if r["field"]=="@message"), "")
            w.writerow([ts, msg])
    print("Saved cw_insights.csv")

3) Create a local IntelliJ project and import logs

Open IntelliJ IDEA → New Project → Empty project (or open an existing repo).
Create a folder logs/ and drop cw_insights.csv, or create a small logs.jsonl.
(Optional) Add a small script folder tools/log_analysis/ with the Python script above.

4) Install a Claude / Anthropic plugin into IntelliJ

There are official and community plugins that integrate Claude/Claude Code into JetBrains IDEs — search the JetBrains Marketplace inside IntelliJ and install a plugin that suits your security model (some require an API key; others use a linked account). Examples of available integrations and plugin docs are on Anthropic/Claude pages and the JetBrains Marketplace.

Typical install steps:

IntelliJ → Settings/Preferences → Plugins → Marketplace → search Claude / Claude Code / Claude Code Plus / Claude GUI.
Install and restart IDE.
Open the plugin settings (Tools → Claude or a dedicated toolwindow) and configure authentication:
- Either input your Anthropic API key (if using a community plugin that requires it), or
- Connect via the plugin’s sign-in flow (some official integrations require a subscription).
Configure model (e.g., Claude 3.5/4, Sonnet/Opus depending on plugin choices).

Security note: prefer plugins that let you bring your own API key or that run locally. Inspect plugin source or vendor reputation if handling sensitive logs.

5) Basic workflow inside IntelliJ with Claude

Once the plugin is installed you’ll have a Claude tool window (or a chat pane). Use the pattern below:

A. Summarize a log file

Select a chunk of lines in the CSV / open the file.
Prompt (inside plugin chat):
"Summarize the following log lines. Give a short bulleted summary of errors, likely root causes, and three suggested next steps. Remove timestamps and redact IP addresses."
Paste the log excerpt or the file context.

B. Ask Claude to write parsing / extraction code

Prompt: "Write a Python function that reads cw_insights.csv and extracts fields: timestamp, level, service, message. Use regex robust to JSON-log and plain text log entries. Return a list of dicts."
Paste the generated code into tools/log_analysis/parse_logs.py, run from IntelliJ terminal or run configurations.

C. Convert natural language to CloudWatch Insights

Prompt: "Generate a CloudWatch Insights query that returns the top-10 slowest requests (HTTP path and 90th percentile latency) for the last 3 hours from these logs."
Claude will produce a query you can run in CloudWatch.

D. Create unit tests / quick checks

Ask Claude to produce small unit tests for your parsing function. Paste into tests/test_parse.py and run.

E. Create an RCA report

After Claude summarizes and classifies errors, ask for a structured RCA template (title, impact, timeline, cause, remediation, mitigations).

6) Sample prompts you can copy/paste

Summarize logs:
"Summarize these 200 log lines. Output: (1) 3-line summary; (2) Most frequent error signatures; (3) Hypotheses for root cause; (4) 3 recommended next steps for engineers (short actionable items)."
Regex for parsing:
"Write a Python regex that extracts HTTP method, path, status code, latency_ms from log messages like: 'INFO 2026-02-01 Request GET /api/v1/users 200 123ms' and also handles JSON entries with keys method, path, status, latency."
Prioritization:
"From this set of error messages, rank the top 5 unique error signatures by estimated impact (frequency × severity). Explain your calculation."

7) Example: using Claude to build a simple analyzer script

Ask Claude to generate code — example Python that parses csv, aggregates counts, and prints top errors:


# tools/simple_analyzer.py
import csv
from collections import Counter
import re, json

def parse_line(msg):
    # naive: try JSON first
    try:
        j = json.loads(msg)
        return j.get("level"), j.get("message", "")
    except:
        # fallback regex
        m = re.search(r"(ERROR|WARN|INFO)\s+.*?\s(.*)", msg)
        if m:
            return m.group(1), m.group(2)
    return None, msg

def analyze(path):
    c = Counter()
    with open(path, newline='') as f:
        r = csv.DictReader(f)
        for row in r:
            _, message = parse_line(row.get("message",""))
            # normalize error signature (trim numbers, ids)
            sig = re.sub(r"\\b[0-9a-f]{6,}\\b", "<id>", message)
            sig = re.sub(r"\\d{2,}", "<num>", sig)
            c[sig.strip()[:200]] += 1
    for sig, count in c.most_common(20):
        print(f"{count:5d}  {sig}")

if __name__ == "__main__":
    analyze("cw_insights.csv")

You can ask Claude to refine the normalization rules or to output to CSV/JSON for dashboarding.

8) How to use Claude for root-cause / hypothesis generation

Provide context: service name, recent deploys, error timestamps, surrounding logs.
Ask Claude to generate differential hypotheses — e.g., code bug vs config vs infra vs network.
Ask for evidence to confirm or falsify each hypothesis (what query or metric to run — CPU, 5xx rate, DB connection errors).

Example prompt:
"Given these error signatures and the fact that we deployed service X at 02:10 UTC, propose three plausible root causes and for each give two concrete checks (CloudWatch metric or log query) that will confirm or rule it out."

9) Move from ad-hoc to repeatable

Save your Claude prompts as templates in the plugin (many plugins let you save conversations or snippets).
Wrap common Claude interactions in scripts: e.g., a script that extracts top 100 error messages and opens them in a Claude chat for summarization.
Automate exports from CloudWatch to S3 and run nightly analyses on samples (with redaction).

10) Visualizing / reporting

After Claude produces structured output (JSON/CSV), import into your favorite dashboard (Quick options: Grafana, Kibana, or a simple Excel/Google Sheets).
Use IntelliJ to iterate on transformation scripts and keep them in version control.
For high-value incidents, use Claude to draft the postmortem text (timeline, impact, mitigation, follow-ups) and then edit for accuracy.

11) Example security & compliance reminders

Redact PII and secrets before sending data to external AI models if your policy forbids it.
Audit plugin network access (some plugins call external MCP servers). If in doubt, prefer CLI tools that run locally and let you paste only the minimal excerpt into Claude.
Keep logs stored with least privilege and use short-lived tokens for programmatic access.

12) Troubleshooting tips (IntelliJ + Claude)

If the plugin needs an external claude CLI, install and ensure it’s on PATH before opening the plugin.
If the plugin UI is slow, increase memory for IntelliJ (VM options) or use smaller excerpts.
If you hit rate limits, move heavier analysis to local scripts and use Claude only for summarization and guidance.

13) Real example workflow (concise)

Export 1,000 error lines from CloudWatch Insights to cw_insights.csv.
Open project in IntelliJ; install Claude plugin; authenticate.
Select 200 lines in cw_insights.csv → ask Claude: summarize & produce 3-step remediation.
Ask Claude to write a parser → paste the code into tools/parse_logs.py and run tests.
Use the parser output to produce a frequency table and create a Grafana panel or CSV report.
Use Claude to draft an RCA and email template for stakeholders.

14) Additional resources & reading

Anthropic / Claude Code docs for JetBrains integrations (plugin docs and official guides).
JetBrains Marketplace (search “Claude”, “Claude Code”, “Claude GUI”) for plugin options and install instructions.

Final tips

Start small — use short, redacted excerpts in the IDE to validate findings.
Use Claude to generate reproducible queries and code, but always validate outputs against the raw logs — AI suggestions are helpful, not authoritative.
Keep a reproducible pipeline: CloudWatch → S3 → tools/ scripts → Claude-assisted summaries → dashboards & RCA.

GCP Console log analysis — step-by-step (using Claude.ai inside IntelliJ)

A practical, end-to-end guide showing how to find, query, and analyze Google Cloud logs in the GCP Console, and how to use an AI assistant (Claude) inside IntelliJ to accelerate queries, regex, and root-cause investigation.

Below: prerequisites → setup → hands-on analysis steps (Logs Explorer + gcloud) → exporting/alerts → using Claude in IntelliJ to speed things up → best practices & troubleshooting.

1) Quick overview & prerequisites

What we’ll use:

GCP Cloud Logging (Logs Explorer & Log Analytics) to view and query logs.
IntelliJ IDEA with:
- Cloud Code for IntelliJ (recommended for GCP integration).
- A Claude/Claude Code plugin (lets you chat with Claude inside your JetBrains IDE to produce queries, summarize log dumps, create regex, etc.). Several community and official plugins exist.

Prerequisites:

GCP project with logs being produced (Compute Engine, Cloud Run, GKE, App Engine, etc.).
gcloud CLI installed and authenticated (gcloud auth login or service account as needed).
IntelliJ IDEA (2023.3+ recommended) with Cloud Code plugin and a Claude plugin installed.

2) Enable and inspect logs in GCP Console (step-by-step)

a) Open Logs Explorer

In the Google Cloud Console, go to Logging → Logs Explorer. (This is the primary UI for searching and troubleshooting logs.)

b) Use the Logs Explorer query builder or enter a LogsQL filter

Example — show latest ERRORs from GCE instances:


resource.type="gce_instance"
severity>=ERROR

Or combine fields (for Cloud Run service my-service):


resource.type="cloud_run_revision"
resource.labels.service_name="my-service"
severity>=ERROR

Logs Explorer supports both the basic filter builder and Log Analytics/SQL-style queries for deeper analysis.

c) Save and pin useful queries

Save frequent searches as Saved Queries so teams can reuse them. (Logs Explorer UI supports saving filters for troubleshooting workflows.)

3) CLI quick-look: `gcloud logging read`

When you want quick command-line inspection:


# Read the last 50 ERROR entries from Compute Engine
gcloud logging read 'resource.type="gce_instance" severity>=ERROR' --limit=50 --project=my-project

Use --freshness or add timestamps in the filter to focus ranges. (GCP docs show how to form queries and use the CLI for reads and exports.)

4) Deeper analysis: Log Analytics & export to BigQuery

For aggregated analytics, use Log Analytics (Logs SQL) to run SQL queries over logs, create charts, and build dashboards. This is useful for patterns, percentiles, and grouping.
Export option: create a Logs sink to export to BigQuery (for long-term analysis / ML) or to Cloud Storage / Pub/Sub for pipeline processing. Use sinks when you need to run complex analysis or join logs with other datasets.

5) Typical log-analysis workflow (practical steps)

Reproduce / identify time window — narrow the time range to when the incident happened.
Start with high severity — filter severity>=ERROR / severity>=CRITICAL.
Group by resource/service — add resource.labels filters.
Expand to surrounding context — pick a trace id / request id from an error entry and search for it to see full request flow.
Run Log Analytics — aggregate counts per minute to spot spikes (e.g., COUNT(*) BY minute(timestamp) in logs SQL).

6) Use IntelliJ + Claude to speed things up (concrete examples)

Why combine IntelliJ + Claude?

Claude inside IntelliJ can generate / refine Logs Explorer queries, produce regexes to extract fields, summarize large log excerpts, translate raw logs into human-readable root causes, or create templates for alerts. JetBrains plugins let you chat with Claude inside the IDE so you don't context-switch.

Example workflows inside IntelliJ

A. Paste a sample error log and ask Claude to summarize

Copy an error log snippet into Claude chat window (in the plugin panel) and ask:
- “Summarize the likely root cause and list the fields (request id, user id, error code) with regexes to extract them.”
Claude returns a short summary plus regex patterns you can paste into Logs Explorer’s extraction field.

B. Ask Claude to generate a Logs Explorer filter

Prompt example:
Create a Logs Explorer query to find ERROR logs in Cloud Run service "payments" in the last 2 hours that contain "timeout" or "deadline exceeded".
Claude will produce a LogsQL/filter expression you can copy into Logs Explorer.

C. Convert free-text problem statement → query

Tell Claude: “I see intermittent 503s between 2026-02-20 03:00 and 04:30 UTC for service X. Give me a set of diagnostic queries to run (3 priorities).”
Claude returns prioritized queries: spike detection, trace id extraction, and container restart correlation.

D. Create alerting rule skeletons

Ask Claude to draft the alerts (example conditions, thresholds, incident title, runbook link). Use the output to create an alert policy in Cloud Monitoring.

(Plugins like Claude Code / community IntelliClaude let you run these conversations directly in the IDE and keep project context available to the assistant.)

7) Sample Logs Explorer queries & patterns

Find 5xx errors in Cloud Run for a service


resource.type="cloud_run_revision"
resource.labels.service_name="payments"
httpRequest.status>=500
timestamp >= "2026-02-26T00:00:00Z"

Search by trace / request id

Many apps attach trace or requestId fields. To find all entries for a request:


jsonPayload.requestId="abcd-1234-xyz"

Count errors per minute (Log Analytics / Logs SQL)

Use Logs SQL in Log Analytics for quick aggregation (example syntax varies—see docs):


SELECT
  TIMESTAMP_TRUNC(timestamp, MINUTE) AS minute,
  COUNT(*) AS error_count
FROM
  `logs`
WHERE
  severity >= "ERROR"
GROUP BY minute
ORDER BY minute DESC

For precise Log SQL syntax and examples, consult GCP Log Analytics docs.

8) Exporting logs and alerts (short how-to)

Export to BigQuery (sink)

In Cloud Console → Logging → Logs Router → Create Sink.
Choose BigQuery dataset as the destination and an appropriate filter for only the logs you need.
Use BigQuery to run historical analytics, ML models, or join with business data.

Alerting basics

Create a Log-based metric (count of error log entries), then build an Alerting policy in Cloud Monitoring on that metric (thresholds, notification channels). This gives reliable automated alerts instead of manual checks.

9) Best practices & tips

Structured logging: log JSON with standardized fields (requestId, userId, service, span/trace) to make queries and grouping trivial.
Use labels: include service and environment labels to quickly slice logs (prod/staging).
Limit noise: exclude low-value logs at ingestion or use sinks to route verbose logs elsewhere.
Retention & cost: exporting to BigQuery and long retention costs money — design retention policies accordingly.

10) Troubleshooting & common pitfalls

No logs appearing? Check IAM permissions (Viewer / Logs Viewer) and ensure log ingestion is enabled for the service. Also confirm gcloud is pointed to the correct project.
Fields not searchable? Make sure logs are structured and fields are present in the jsonPayload or labels; use extraction if needed.
Large result sets slow the UI: narrow time windows or sample using --limit in gcloud logging read.

11) Further reading & plugin links

Cloud Logging docs (Logs Explorer & Log Analytics).
Cloud Code for IntelliJ (install / setup).
Claude / Claude Code JetBrains integrations and IntelliJ plugins (examples & marketplace).

12) Quick checklist to get started right now

Enable logs for your service in GCP.
Install Cloud Code and a Claude plugin in IntelliJ.
Run a simple query in Logs Explorer (severity>=ERROR) and pick a sample error entry.
Paste that sample into Claude in IntelliJ and ask: “Summarize this error and give me a Logs Explorer query to find related entries.”
Iterate: use the generated query, export to BigQuery if you need long-term analysis, and create log-based metrics for alerting.

Pages

Friday, 27 February 2026

GCP Console — Production Log Analysis (step-by-step)

GCP Console — Production Log Analysis (step-by-step)

1) Goal & quick summary

2) Prerequisites & access

3) Step A — Narrow down logs in GCP Console (Log Explorer)

4) Step B — Refine & extract fields

5) Step C — Export logs for deeper analysis

Option 1 — Export to BigQuery (recommended for large-scale analysis)

Option 2 — Download a CSV / JSON from Log Explorer (ad-hoc)

6) Step D — Prepare data for Claude.ai Cursor

7) Step E — Use Claude.ai Cursor: practical examples & prompt templates

A) Quick human-readable summary

B) Find top offending requests

C) Anomaly detection for latency

D) Create an alerting metric recommendation

E) Build a runbook-style remediation

F) BigQuery SQL ask (if Cursor can run SQL or you prefer to run it yourself)

8) Example LQL snippets (to use directly in GCP Log Explorer)

9) Putting findings into action

10) Security, cost & best practices

11) Example end-to-end mini playbook (concise)

12) Sample prompts you can copy-paste into Cursor

13) Checklist before sharing results externally

14) Closing tips

AWS production log analysis with Claude in Cursor — a step-by-step guide

Quick architecture overview (what you build)

Step 1 — Gather logs (fast, low friction)

Step 2 — Prepare a secure sample set to send to Claude

Step 3 — Extract useful slices (what to send)

Step 4 — Configure Cursor to use Claude (quick)

Step 5 — Prompts & interactions: how to ask Claude to analyze logs

Template A — Quick summary

Template B — Correlate traces & explain

Template C — Generate CloudWatch Insights queries

Step 6 — Example workflow (hands-on)

Step 7 — Automating parts of the flow

Step 8 — Example concrete commands

Step 9 — What Claude is good at here (and what to avoid)

Step 10 — Ops, costs, and governance

Appendix — Example prompts & followups (copy/paste ready)

Final checklist before you send a snippet to Claude

Closing notes / recommended next steps

AWS production log analysis using Claude.ai inside IntelliJ — step-by-step

1) Plan & safety checklist (do this first)

2) Get the logs out of AWS (options)

3) Create a local IntelliJ project and import logs

4) Install a Claude / Anthropic plugin into IntelliJ

5) Basic workflow inside IntelliJ with Claude

6) Sample prompts you can copy/paste

7) Example: using Claude to build a simple analyzer script

8) How to use Claude for root-cause / hypothesis generation

9) Move from ad-hoc to repeatable

10) Visualizing / reporting

11) Example security & compliance reminders

12) Troubleshooting tips (IntelliJ + Claude)

13) Real example workflow (concise)

14) Additional resources & reading

Final tips

GCP Console log analysis — step-by-step (using Claude.ai inside IntelliJ)

1) Quick overview & prerequisites

2) Enable and inspect logs in GCP Console (step-by-step)

a) Open Logs Explorer

b) Use the Logs Explorer query builder or enter a LogsQL filter

c) Save and pin useful queries

3) CLI quick-look: gcloud logging read

4) Deeper analysis: Log Analytics & export to BigQuery

5) Typical log-analysis workflow (practical steps)

6) Use IntelliJ + Claude to speed things up (concrete examples)

Why combine IntelliJ + Claude?

Example workflows inside IntelliJ

7) Sample Logs Explorer queries & patterns

Find 5xx errors in Cloud Run for a service

Search by trace / request id

Count errors per minute (Log Analytics / Logs SQL)

8) Exporting logs and alerts (short how-to)

Export to BigQuery (sink)

Alerting basics

9) Best practices & tips

3) CLI quick-look: `gcloud logging read`