Module-6: Logging and Monitoring Assignment

What is Logging and What is monitoring? Explain with examples and different types of tools being used.

Logging:

Logging involves recording events or messages that occur within a system. These events are typically stored in log files for later analysis, troubleshooting, and auditing.

Examples:
1. Error Logging: Recording errors that occur during the execution of a program or system.
  
  Example: If a web application encounters an unexpected error, it can log details about the error, including the timestamp, error message, and relevant context.
2. Audit Logging: Capturing information about user activities and system events for security and compliance purposes.
  
  Example: Logging user logins, file access, and other security-related events to trace potential security breaches.
3. Performance Logging: Recording performance metrics to analyze and optimize system performance.
  
  Example: Logging response times, resource utilization, and other performance metrics to identify bottlenecks.

Types of Logging Tools:

ELK Stack (Elasticsearch, Logstash, Kibana): A popular open-source stack used for searching, analyzing, and visualizing log data.
Splunk: A platform for searching, monitoring, and analyzing machine-generated data, including log files.
Graylog: An open-source log management platform that collects, indexes, and analyzes log data.

Monitoring:

Monitoring involves observing the state and performance of a system in real-time to detect issues, ensure optimal performance, and trigger alerts when predefined thresholds are breached.

Examples:

Server Monitoring: Tracking the health and performance of servers, including CPU usage, memory usage, and disk space.

Example: A monitoring tool can alert administrators if a server's CPU usage exceeds a certain threshold.
Application Monitoring: Observing the behavior and performance of software applications.

Example: Monitoring the response time of a web application and alerting if it takes longer than expected.
Network Monitoring: Monitoring network infrastructure for performance, availability, and security.

Example: Detecting network outages or unusual network activity that may indicate a security threat.

Types of Monitoring Tools:

Nagios: An open-source monitoring system that can monitor hosts, services, and network devices.
Prometheus: An open-source monitoring and alerting toolkit designed for reliability and scalability.
New Relic: A SaaS-based application performance monitoring (APM) tool that provides real-time insights into application performance.

Set up AWS Custom metric like Memory metric and push it to Amazon Cloudwatch. Also, push Ubuntu EC2 logs like /var/log/syslog to Cloudwatch logs.

Launch an ec2 instance(ubuntu) and attached the EC2CloudWatchAgentRole to the ec2 instance as shown below.

Download the appropriate agent installation file:

In my case it’s ubuntu. I am downloading the latest Ubuntu package and installing it.

 wget https://s3.amazonaws.com/amazoncloudwatch-agent/ubuntu/amd64/latest/amazon-cloudwatch-agent.deb
 sudo dpkg -i -E ./amazon-cloudwatch-agent.deb

The final config files get stored in the following location

 sudo nano /opt/aws/amazon-cloudwatch-agent/bin/config.json

If you want to collect the system metrics, install collected on your server.

 sudo apt-get update -y
 sudo apt-get install collectd

Here is the final cloudwatch agent config. Update the file as follows.

 sudo nano /opt/aws/amazon-cloudwatch-agent/bin/config.json

 {
     "agent": {
       "metrics_collection_interval": 10,
       "run_as_user": "root"
     },
     "logs": {
       "logs_collected": {
         "files": {
           "collect_list": [
             {
               "file_path": "/var/log/syslog",
               "log_group_name": "/apps/system/syslog",
               "log_stream_name": "{ip_address}_{instance_id}",
               "timestamp_format": "%b %d %H:%M:%S",
               "timezone": "Local"
             }
           ]
         }
       }
     },
     "metrics": {
       "aggregation_dimensions": [
         [
           "InstanceId"
         ]
       ],
       "append_dimensions": {
         "AutoScalingGroupName": "${aws:AutoScalingGroupName}",
         "ImageId": "${aws:ImageId}",
         "InstanceId": "${aws:InstanceId}",
         "InstanceType": "${aws:InstanceType}"
       },
       "metrics_collected": {
         "collectd": {
           "metrics_aggregation_interval": 60
         },
         "disk": {
           "measurement": [
             "used_percent"
           ],
           "metrics_collection_interval": 10,
           "resources": [
             "*"
           ]
         },
         "mem": {
           "measurement": [
             "mem_used_percent"
           ],
           "metrics_collection_interval": 10
         },
         "statsd": {
           "metrics_aggregation_interval": 60,
           "metrics_collection_interval": 10,
           "service_address": ":8125"
         }
       }
     }
   }

Now, let’s start the Cloudwatch agent using the following.

 sudo /opt/aws/amazon-cloudwatch-agent/bin/amazon-cloudwatch-agent-ctl -a fetch-config -m ec2 -c file:/opt/aws/amazon-cloudwatch-agent/bin/config.json -s

Verify Metrics in CloudWatch Console

Open the CloudWatch console.
Navigate to the "Metrics" section and look for the custom metric "mem_used_percent."

Validating Custom Logs in Cloudwatch Dashboard

Once the setup is done, you can view all the configured logs under the cloudwatch dashboard (under the logs option)

Go to Logs –> Log Groups and you will see the log group you mentioned in the agent configuration.
Select the log group and you should see the instance identified you mentioned in the config.
If you click the instance identifier, it shows all the logs. You can use the cloud watch filter option to filter and query required logs.

Now check the above logs in the terminal.

sudo nano /var/log/syslog

Explain Splunk and its advantages.

Splunk is a powerful platform designed for searching, monitoring, and analyzing machine-generated data. It excels in processing and visualizing large volumes of data from diverse sources, helping organizations gain insights, troubleshoot issues, and make informed decisions. Splunk is widely used for log management, security information and event management (SIEM), and operational intelligence.

Advantages of Splunk:
1. Data Aggregation and Indexing:
  - Splunk can collect and index data from a wide range of sources, including logs, events, and metrics.
  - It supports the aggregation of data from different systems and applications, providing a unified view.
2. Search and Analysis Capabilities:
  - Splunk's strength lies in its powerful search and query language, which enables users to quickly search, analyze, and correlate data.
  - The platform provides real-time search capabilities, allowing users to monitor events as they occur.
3. Visualization and Reporting:
  - Splunk offers robust visualization tools to create dashboards and reports, making it easier to interpret and communicate insights.
  - Users can create custom charts, graphs, and tables to represent data in a meaningful way.
4. Alerting and Monitoring:
  - Splunk allows users to set up alerts based on predefined conditions or thresholds.
  - Alerts can be triggered in real-time, notifying administrators of issues or potential security incidents.
5. Scalability:
  - Splunk is designed to scale horizontally and vertically to handle growing data volumes.
  - It can distribute search and indexing tasks across multiple nodes in a clustered environment for improved performance.
6. Integration Capabilities:
  - Splunk provides a wide range of integrations with third-party tools, allowing users to connect and correlate data from various sources.
  - Integration with other security tools enhances its capabilities for threat detection and incident response.
7. Security and Compliance:
  - Splunk offers features to help organizations meet security and compliance requirements.
  - It can assist in monitoring user activities, detecting security incidents, and generating reports for audits.
8. Community and Ecosystem:
  - Splunk has a vibrant community and a rich ecosystem of apps and add-ons developed by both Splunk and the community.
  - Users can leverage pre-built apps to extend Splunk's functionality for specific use cases.
9. Flexibility and Customization:
  - Splunk is highly customizable, allowing users to tailor searches, dashboards, and reports to their specific needs.
  - It supports the creation of custom apps and dashboards to address unique requirements.
10. Machine Learning and Analytics:
  - Splunk integrates machine learning capabilities to detect anomalies, predict trends, and provide predictive analytics.
  - Advanced analytics features enhance the platform's ability to identify patterns and outliers in data.

Splunk's versatility and broad feature set make it a popular choice for organizations seeking a comprehensive solution for log management, monitoring, and analysis of machine-generated data.

Explain monitoring tools like Nagios, Dynatrace, and AppDynamics.

Nagios:

Nagios is an open-source monitoring system used to monitor the availability and performance of IT infrastructure components. It supports monitoring of hosts, services, and network devices, providing real-time insights into the health of systems.

Key Features:
1. Host and Service Monitoring:
  - Monitors hosts and services such as CPU, memory, disk usage, and network connectivity.
2. Alerting:
  - Sends alerts when predefined thresholds are exceeded, helping administrators respond to issues promptly.
3. Plugins:
  - Supports plugins for extending monitoring capabilities to various applications and devices.
4. Dashboard and Reporting:
  - Provides a web-based interface for viewing dashboards and generating reports on system status.
5. Scalability:
  - Scalable architecture allows the deployment of multiple Nagios instances for distributed monitoring.

Use Case Example:

Nagios can monitor a company's servers and services, alerting administrators if a critical service (e.g., web server) becomes unavailable or if resource utilization exceeds predefined levels.

Dynatrace:

Dynatrace is an application performance monitoring (APM) tool that provides insights into the performance of applications and services. It offers end-to-end visibility, from the user experience to the underlying infrastructure.

Key Features:

Real User Monitoring (RUM):
- Monitors and analyzes real user interactions with applications to identify performance issues.
Code-level Visibility:
- Provides deep insights into application code execution, helping developers pinpoint and resolve issues.
AI-Powered Root Cause Analysis:
- Uses artificial intelligence to automatically identify the root causes of performance problems.
Cloud and Microservices Support:
- Supports monitoring of cloud-native applications and microservices architectures.
Automation:
- Offers automation features for deployment, scaling, and optimization of applications.

Use Case Example:

Dynatrace can help an e-commerce website identify and resolve performance bottlenecks by analyzing user interactions, code execution, and infrastructure metrics.

AppDynamics:

AppDynamics is an APM solution that focuses on providing visibility into application performance, user experience, and business impact. It helps organizations optimize the performance of their applications to enhance user satisfaction.

Key Features:

Application Performance Monitoring:
- Monitors application performance in real-time, including response times and transaction details.
User Experience Monitoring:
- Tracks user interactions with applications to ensure a positive user experience.
Business Transaction Monitoring:
- Correlates application performance with business transactions to prioritize issue resolution.
Dynamic Baselining:
- Establishes performance baselines for applications and detects anomalies that may indicate issues.
Integration with DevOps Tools:
- Integrates with DevOps tools for seamless collaboration between development and operations teams.

Use Case Example:

AppDynamics can assist a financial institution in ensuring the smooth operation of its online banking application by monitoring transaction times, detecting errors, and correlating application performance with business transactions.

Summary:

Nagios: Open-source, versatile, and widely used for IT infrastructure monitoring.
Dynatrace: Focuses on end-to-end application performance monitoring with AI-driven insights.
AppDynamics: APM solution emphasizing user experience, business impact, and seamless DevOps integration.

Each tool caters to specific monitoring needs, ranging from infrastructure monitoring (Nagios) to in-depth application performance insights (Dynatrace and AppDynamics). Organizations choose based on their requirements for scalability, depth of analysis, and integration capabilities.

Explain Logging tools like Cloudwatch logs, Loggly, and Elastic.

CloudWatch Logs:

CloudWatch Logs is a log management and monitoring service provided by Amazon Web Services (AWS). It allows users to collect, store, and monitor log data from AWS resources and custom applications.

Key Features:
1. Log Collection:
  - Collects log data from various AWS services, applications, and custom sources.
2. Search and Query:
  - Provides a query language for searching and filtering log data.
3. Metrics and Alarms:
  - Allows users to create custom metrics and set up alarms based on log data.
4. Integration with AWS Services:
  - Integrates seamlessly with other AWS services, facilitating centralized log management.
5. Retention and Archiving:
  - Supports configurable log retention periods and archival of log data to Amazon S3.

Use Case Example:

CloudWatch Logs can be used to monitor and analyze logs generated by an AWS EC2 instance, helping identify performance issues, errors, or security events.

Loggly:

Loggly is a cloud-based log management and analytics service that helps organizations collect, search, and analyze log data from various sources, including applications, servers, and cloud platforms.

Key Features:

Centralized Log Collection:
- Aggregates logs from different sources into a centralized platform.
Real-time Search and Analysis:
- Provides real-time search capabilities and analytics for log data.
Dashboards and Visualizations:
- Offers customizable dashboards and visualizations to gain insights from log data.
Alerting:
- Allows users to set up alerts based on log events to detect and respond to issues proactively.
Integration with DevOps Tools:
- Integrates with popular DevOps and collaboration tools for seamless workflow integration.

Use Case Example:

Loggly can assist a software development team in identifying and troubleshooting issues by analyzing logs from distributed microservices.

Elastic (Elasticsearch, Logstash, Kibana - ELK Stack):

The Elastic Stack, also known as ELK Stack, consists of three main components: Elasticsearch, Logstash, and Kibana. Elasticsearch is a search and analytics engine, Logstash is a log processing pipeline, and Kibana is a visualization tool.

Key Features:

Data Ingestion:
- Logstash processes and ingests log data from various sources into Elasticsearch.
Search and Analysis:
- Elasticsearch provides powerful search and analytics capabilities for log data.
Visualization:
- Kibana enables users to create customizable dashboards and visualizations.
Scalability:
- Elasticsearch is designed for horizontal scalability to handle large volumes of data.
Open Source:
- ELK Stack is open source, providing flexibility for customization and community support.

Use Case Example:

An organization can use the ELK Stack to collect, analyze, and visualize logs from multiple servers and applications to gain insights into system performance and troubleshoot issues.

Summary:

CloudWatch Logs: AWS-native log management service with seamless integration into the AWS ecosystem.
Loggly: Cloud-based log management solution offering real-time search, analytics, and integration with DevOps tools.
Elastic (ELK Stack): Open-source stack with Elasticsearch for search and analytics, Logstash for log processing, and Kibana for visualization, providing flexibility and scalability.

Set up the ELK stack and push some of the EC2 metric data into the ELK stack.
Set up the Grafana dashboard to monitor Docker containers. Generate an alert every time a Docker container is Stopped.
Explain the AWS Config service and create events when an S3 bucket is made public.
Use the SSM agent to update the software on your Linux machine.
Understand logging levels in Python and write a boto3 script to list all IAM Roles in your AWS account (import logging module) and also push the logs to Cloudwatch logs. This you can do on an EC2 instance or on Lambda.

Module-6: Logging and Monitoring Assignment

What is Logging and What is monitoring? Explain with examples and different types of tools being used.

Logging:

Monitoring:

Set up AWS Custom metric like Memory metric and push it to Amazon Cloudwatch. Also, push Ubuntu EC2 logs like /var/log/syslog to Cloudwatch logs.

Verify Metrics in CloudWatch Console

Validating Custom Logs in Cloudwatch Dashboard

Explain Splunk and its advantages.

Explain monitoring tools like Nagios, Dynatrace, and AppDynamics.

Nagios:

Dynatrace:

AppDynamics:

Summary:

Explain Logging tools like Cloudwatch logs, Loggly, and Elastic.

CloudWatch Logs:

Loggly:

Elastic (Elasticsearch, Logstash, Kibana - ELK Stack):

Summary:

Set up the ELK stack and push some of the EC2 metric data into the ELK stack.

Set up the Grafana dashboard to monitor Docker containers. Generate an alert every time a Docker container is Stopped.

Explain the AWS Config service and create events when an S3 bucket is made public.

Use the SSM agent to update the software on your Linux machine.

Understand logging levels in Python and write a boto3 script to list all IAM Roles in your AWS account (import logging module) and also push the logs to Cloudwatch logs. This you can do on an EC2 instance or on Lambda.