Log-based metrics in GCP - Cheat Sheet & Quick Revision

Recall & Review

beginner

What are log-based metrics in Google Cloud?

Log-based metrics are measurements created from logs data. They help track and count specific events or patterns found in logs to monitor system behavior.

beginner

How do log-based metrics differ from regular metrics?

Regular metrics come from system data like CPU or memory usage. Log-based metrics come from analyzing log entries to count or measure specific events.

intermediate

What are the two types of log-based metrics in Google Cloud?

There are two types: Counter metrics, which count how many times something happens, and Distribution metrics, which measure values like latency or size from logs.

beginner

Why use log-based metrics instead of just reading logs?

Log-based metrics summarize log data into numbers you can graph and alert on. This makes it easier to spot trends and problems quickly without reading all logs.

beginner

What is a common use case for log-based metrics?

A common use is counting error messages in logs to alert when errors increase, helping teams fix issues faster.

What does a counter log-based metric do?

A Measures the size of log files

B Counts how many times a specific event occurs

C Tracks CPU usage over time

D Stores raw log data

Counter metrics count occurrences of events found in logs.

Which Google Cloud service is used to create log-based metrics?

A Cloud Logging

B Cloud Storage

C Cloud Functions

D Cloud SQL

Cloud Logging collects logs and allows creation of log-based metrics.

What type of log-based metric would you use to measure request latency from logs?

A Counter metric

B Gauge metric

C Boolean metric

D Distribution metric

Distribution metrics measure values like latency or size from logs.

Why are log-based metrics useful for monitoring?

A They store logs in a database

B They replace logs completely

C They convert log data into numbers for easy tracking and alerting

D They encrypt logs for security

Log-based metrics summarize logs into measurable data for monitoring.

Which of the following is NOT a feature of log-based metrics?

A Automatically fixing errors found in logs

B Measuring values from logs

C Triggering alerts based on log data

D Counting specific log events

Log-based metrics do not fix errors; they help detect and alert on them.

Explain what log-based metrics are and why they are useful in cloud monitoring.

Think about how logs can be turned into numbers to watch system health.

Describe the two types of log-based metrics and give an example use case for each.

One counts events, the other measures values like latency.

Practice

1. What is the main purpose of a log-based metric in Google Cloud?

easy

A. To delete old logs automatically

B. To count specific log entries and turn them into numbers you can monitor

C. To store logs permanently without any processing

D. To encrypt logs for security

Solution

1. Step 1: Understand what log-based metrics do

   Log-based metrics count log entries that match a filter and convert them into numerical data.

2. Step 2: Compare options to this definition

   Only To count specific log entries and turn them into numbers you can monitor describes counting logs and turning them into numbers for monitoring.

3. Final Answer:

   To count specific log entries and turn them into numbers you can monitor -> Option B

4. Quick Check:

   Log-based metrics = counting logs for monitoring [OK]

Hint: Log-based metrics count logs to create numbers you watch [OK]

Common Mistakes:

• Confusing log storage with metrics
• Thinking metrics delete or encrypt logs
• Assuming metrics store logs permanently

2. Which of the following is the correct way to create a log-based metric filter to count error logs with severity ERROR?

easy

A. severity != "ERROR"

B. severity = "ERROR"

C. severity:ERROR

D. severity == "ERROR"

Solution

1. Step 1: Recall the correct filter syntax for log-based metrics

   Google Cloud uses the syntax severity == "ERROR" to filter logs with severity ERROR.

2. Step 2: Check each option

   Double equals (==) is the correct equality operator; single equals (=) is invalid. Colon (:) is for substring matching, not exact equality. != excludes the value.

3. Final Answer:

   severity == "ERROR" -> Option D

4. Quick Check:

   Filter syntax uses == for equality [OK]

Hint: Use double equals sign for filter equality [OK]

Common Mistakes:

• Using single equals instead of double
• Using colon instead of equals
• Using not equals when filtering for ERROR

3. Given this log-based metric filter: resource.type = "gce_instance" AND severity >= "ERROR", what logs will this metric count?

medium

A. All logs from GCE instances with severity ERROR or higher

B. Only logs with severity exactly ERROR from any resource

C. All logs from any resource with severity ERROR or higher

D. Only logs from GCE instances with severity lower than ERROR

Solution

1. Step 1: Analyze the filter conditions

   The filter requires resource.type to be "gce_instance" and severity to be ERROR or higher (like CRITICAL).

2. Step 2: Match options to filter meaning

   All logs from GCE instances with severity ERROR or higher correctly states logs from GCE instances with severity ERROR or higher. Others either ignore resource type or severity condition.

3. Final Answer:

   All logs from GCE instances with severity ERROR or higher -> Option A

4. Quick Check:

   Filter = GCE instance + severity >= ERROR [OK]

Hint: AND means both conditions must be true [OK]

Common Mistakes:

• Ignoring resource.type condition
• Confusing severity >= ERROR with exact match
• Assuming severity lower than ERROR is included

4. You created a log-based metric with filter severity = "ERROR" OR severity = "CRITICAL", but the metric shows zero counts. What is the likely problem?

medium

A. The filter syntax is invalid; OR is not supported in log-based metric filters

B. Severity levels ERROR and CRITICAL do not exist

C. The logs do not have severity fields

D. The metric needs at least 24 hours to show data

Solution

1. Step 1: Check if the filter syntax is valid

   The syntax severity = "ERROR" OR severity = "CRITICAL" is valid; OR is supported in log-based metric filters.

2. Step 2: Understand impact of zero counts

   A valid filter with zero counts indicates no matching logs, likely because the logs do not have severity fields or lack ERROR/CRITICAL values.

3. Final Answer:

   The logs do not have severity fields -> Option C

4. Quick Check:

   Valid filter + zero = no matching logs [OK]

Hint: Zero metric despite valid filter usually means no matching log entries [OK]

Common Mistakes:

• Assuming filter syntax invalid
• Thinking OR unsupported
• Waiting 24 hours unnecessarily

5. You want to create a log-based metric that counts how many times a specific error message appears in logs from Cloud Functions. The error message is in the field jsonPayload.message. Which filter correctly counts logs where the message contains the word "timeout"?

hard

A. resource.type = "cloud_function" AND jsonPayload.message : "timeout"

B. resource.type = "cloud_function" AND jsonPayload.message = "timeout"

C. resource.type = "cloud_function" AND jsonPayload.message >= "timeout"

D. resource.type = "cloud_function" AND jsonPayload.message != "timeout"

Solution

1. Step 1: Understand how to filter logs containing a word

   The colon operator (:) is used to check if a field contains a substring in log filters.

2. Step 2: Check resource type and filter syntax

   resource.type = "cloud_function" AND jsonPayload.message : "timeout" correctly filters logs from Cloud Functions where jsonPayload.message contains "timeout".

3. Final Answer:

   resource.type = "cloud_function" AND jsonPayload.message : "timeout" -> Option A

4. Quick Check:

   Use : to check substring in log filters [OK]

Hint: Use colon : to find substring in log fields [OK]

Common Mistakes:

• Using equals instead of colon for substring
• Using comparison operators like >= incorrectly
• Filtering for not equals when looking for matches