Why ELK stack provides observability in Elasticsearch - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create an index in Elasticsearch.

Elasticsearch

PUT /[1]-logs
{
  "mappings": {
    "properties": {
      "timestamp": { "type": "date" },
      "message": { "type": "text" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Auser

Bapp

Csystem

Dnetwork

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated index names like 'user' or 'network' for app logs.

The index app-logs is commonly used to store application logs in ELK, which helps in observability.

2fill in blank

medium

Complete the code to query logs with a specific keyword.

Elasticsearch

GET /app-logs/_search
{
  "query": {
    "match": {
      "message": "[1]"
    }
  }
}

Drag options to blanks, or click blank then click option'

Aerror

Binfo

Cdebug

Dtrace

Attempts:

3 left

💡 Hint

Common Mistakes

Using less severe log levels like 'info' or 'debug' when searching for errors.

Searching for the keyword error helps find error logs, which is key for observability.

3fill in blank

hard

Fix the error in the aggregation to count logs by severity level.

Elasticsearch

GET /app-logs/_search
{
  "size": 0,
  "aggs": {
    "levels": {
      "terms": { "field": "[1]" }
    }
  }
}

Drag options to blanks, or click blank then click option'

Ahost

Bmessage

Cseverity.keyword

Dtimestamp

Attempts:

3 left

💡 Hint

Common Mistakes

Using analyzed text fields like 'message' or 'timestamp' for aggregation.

The field severity.keyword is used to aggregate logs by their severity level in ELK.

4fill in blank

hard

Fill both blanks to create a filter for logs between two timestamps.

Elasticsearch

GET /app-logs/_search
{
  "query": {
    "range": {
      "timestamp": {
        "gte": "[1]",
        "lte": "[2]"
      }
    }
  }
}

Drag options to blanks, or click blank then click option'

A2024-01-01T00:00:00Z

B2023-12-31T23:59:59Z

C2024-01-31T23:59:59Z

D2023-01-01T00:00:00Z

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping start and end dates or using invalid date formats.

Filtering logs from 2024-01-01T00:00:00Z to 2024-01-31T23:59:59Z helps observe logs in January 2024.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps log levels to counts for levels above 'warning'.

Elasticsearch

counts = {level: count for level, count in [1].items() if level [2] 'warning' and count [3] 0}

Drag options to blanks, or click blank then click option'

Alog_counts

B>

Dlog_data

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong variable names or incorrect comparison operators.

This comprehension filters log_counts dictionary to include only levels greater than 'warning' with counts above zero.

Practice

1. What is the main reason the ELK stack provides observability in systems?
ELK = Elasticsearch + Logstash + Kibana

easy

A. It collects, stores, and visualizes data to understand system behavior

B. It only stores data without visualization

C. It only visualizes data without collecting it

D. It replaces all system monitoring tools automatically

Solution

1. Step 1: Understand ELK components roles

   Elasticsearch stores data, Logstash collects and processes data, Kibana visualizes data.

2. Step 2: Connect roles to observability

   Combining these lets you see and understand system behavior clearly.

3. Final Answer:

   It collects, stores, and visualizes data to understand system behavior -> Option A

4. Quick Check:

   Observability = Collect + Store + Visualize [OK]

Hint: Remember ELK = Collect + Store + Visualize for observability [OK]

Common Mistakes:

• Thinking ELK only stores data
• Assuming ELK only visualizes data
• Believing ELK replaces all monitoring tools automatically

2. Which syntax correctly shows the ELK stack components working together for observability?

easy

A. Logstash -> Elasticsearch -> Kibana

B. Kibana -> Logstash -> Elasticsearch

C. Elasticsearch -> Kibana -> Logstash

D. Logstash -> Kibana -> Elasticsearch

Solution

1. Step 1: Identify data flow in ELK

   Logstash collects and processes data first, then sends it to Elasticsearch for storage.

2. Step 2: Visualize data with Kibana

   Kibana reads data from Elasticsearch to create visual dashboards.

3. Final Answer:

   Logstash -> Elasticsearch -> Kibana -> Option A

4. Quick Check:

   Data flow = Logstash to Elasticsearch to Kibana [OK]

Hint: Data flows Logstash -> Elasticsearch -> Kibana [OK]

Common Mistakes:

• Mixing order of components
• Thinking Kibana collects data
• Assuming Elasticsearch visualizes data

3. Given the ELK stack setup, what will Kibana display if Logstash collects logs and Elasticsearch stores them correctly?

medium

A. Only error messages without context

B. Raw logs without any visualization

C. Visual dashboards showing system logs and metrics

D. No data because Kibana cannot access Elasticsearch

Solution

1. Step 1: Understand Kibana's role

   Kibana reads data from Elasticsearch and creates visual dashboards.

2. Step 2: Consider data flow correctness

   If Logstash collects logs and Elasticsearch stores them, Kibana can visualize them properly.

3. Final Answer:

   Visual dashboards showing system logs and metrics -> Option C

4. Quick Check:

   Kibana visualizes stored data [OK]

Hint: Kibana shows dashboards if data is stored correctly [OK]

Common Mistakes:

• Thinking Kibana shows raw logs only
• Assuming Kibana cannot access Elasticsearch
• Believing Kibana shows only errors

4. You set up ELK stack but Kibana shows no data. What is the most likely error in your setup?

medium

A. Elasticsearch is visualizing data incorrectly

B. Kibana is collecting data instead of visualizing

C. Logstash is visualizing data directly

D. Logstash is not sending data to Elasticsearch

Solution

1. Step 1: Identify data flow problem

   If Kibana shows no data, likely Elasticsearch has no data to show.

2. Step 2: Check Logstash role

   Logstash must send data to Elasticsearch; if it doesn't, Elasticsearch stays empty.

3. Final Answer:

   Logstash is not sending data to Elasticsearch -> Option D

4. Quick Check:

   No data in Kibana means no data in Elasticsearch [OK]

Hint: Check Logstash to Elasticsearch connection first [OK]

Common Mistakes:

• Thinking Kibana collects data
• Assuming Elasticsearch visualizes data
• Believing Logstash visualizes data

5. How does the ELK stack help a team quickly find and fix issues in a complex system?

hard

A. By automatically fixing bugs without human input

B. By collecting logs, storing them centrally, and visualizing patterns and errors

C. By replacing all system components with ELK tools

D. By only storing data without any analysis or visualization

Solution

1. Step 1: Understand ELK's observability role

   ELK collects logs, stores them centrally, and visualizes data to reveal system behavior.

2. Step 2: Connect observability to issue resolution

   Visualizing patterns and errors helps teams quickly spot and fix problems.

3. Final Answer:

   By collecting logs, storing them centrally, and visualizing patterns and errors -> Option B

4. Quick Check:

   Observability = Collect + Store + Visualize for quick fixes [OK]

Hint: Observability helps find and fix issues fast [OK]

Common Mistakes:

• Thinking ELK fixes bugs automatically
• Assuming ELK replaces all system parts
• Believing storing data alone solves issues