Logging and monitoring were important with monoliths, but they’re far more critical with Microservices. That’s because debugging with Microservices is difficult since services are deployed independently.
With a robust tool for logging and monitoring, we can quickly know what happened by checking what occurred with a specific user. Also, we can measure metrics from services such as latency, errors, performance, etc.
Logging
Logging is the process of recording events or messages that happen when a software application or system is running. It’s crucial because it helps developers and administrators understand how the application behaves, find and fix issues or errors, and learn about system performance and usage.
For example, if a user purchased a product but didn’t receive the product, how could we figure this out without logs? By logging in to the system, we can quickly check the time the customer purchased the product to see if the purchase was executed, and then we can deliver the product.
Another scenario is that a bug might appear in one cloud service for unknown reasons. The quickest action we can take to troubleshoot this issue is to check the logs. Sometimes, we can discover the problem immediately by looking at the logs.
By logging events, they can review the recorded information to investigate problems, track the sequence of events that caused an issue, and find ways to make the application better.
Let’s see the key components of logging:
Log Messages: Log messages are the information that is recorded during the logging process. They typically include timestamps, severity levels (e.g., debug, info, warning, error, etc.), source or module names, and the actual content or description of the event or message.
Logging Framework/Library: A logging framework or library is a software component that provides the necessary tools, APIs, and utilities to facilitate logging in an application. Examples of popular logging frameworks include Log4j, Logback, and Python’s logging module. These frameworks offer features like log-level configuration, formatting, rotation, and output destinations (e.g., console, file, database, etc.).
Log Outputs: Log outputs are the destinations where log messages are written or stored. Common log outputs include console output (displaying log messages in the console or terminal), log files (writing logs to files on disk), and remote log servers or databases (sending logs to a centralized logging system). The choice of log outputs depends on the specific requirements of the application or system.
Log Levels: Log levels are used to categorize log messages based on their severity or importance. Common log levels include:
DEBUG: Detailed information for debugging purposes.
INFO: General information about the application’s execution.
WARN: Indication of potential issues or warnings that may require attention.
ERROR: Signifies errors or exceptional conditions that may impact the application’s functionality.
FATAL/CRITICAL: Critical errors that may lead to the termination of the application.
Developers can set the desired log levels to control which messages are recorded based on their importance. For example, in a production environment, the log level may be set to ERROR or higher to focus on critical issues. In contrast, in a development or testing environment, a lower log level like DEBUG or INFO may be used to capture more detailed information.
Logging best practices include
Using meaningful log messages: Log messages should provide enough information to understand the context of the event or error and aid in troubleshooting.
Proper log levels: Assigning appropriate log levels to messages ensures we don’t miss important events and that the logs are not cluttered with excessive information.
Contextual information: Including relevant contextual information in log messages, such as user IDs, request IDs, or error codes, can assist in tracing the flow of execution and identifying specific instances of an issue.
Log rotation and archival: Managing log files by implementing log rotation strategies helps prevent log files from growing indefinitely and consuming excessive disk space. Archiving old logs is helpful for compliance, auditing, or historical analysis.
Security and privacy considerations: Care should be taken to avoid logging sensitive information, such as passwords or personally identifiable data, to prevent potential security breaches or privacy violations.
Logging is a crucial aspect of software development and system administration that enables monitoring, troubleshooting, and analysis of applications and systems. By capturing and reviewing log messages, developers and administrators can gain insights into the behavior of their software and identify and resolve issues efficiently.
Monitoring
Monitoring means keeping a close watch on a system or application to ensure it works well. It involves checking and analyzing data in real-time or at set times to find unusual things happening and understand how the system is doing.
To accomplish that, we usually use software with specific dashboards and data to quickly understand what is happening in the system.
Monitoring ensures software, servers, networks, and other system parts work correctly. It helps find and fix problems before they become significant issues, reduces downtime, and best uses resources.
Let’s see some key aspects of monitoring:
Metrics and Data Collection: Monitoring involves collecting relevant metrics and data points from different sources, such as system logs, performance counters, network traffic, and application-specific indicators. These metrics include CPU usage, memory utilization, network latency, response times, error rates, etc.
Monitoring Tools and Platforms: Various monitoring tools and platforms are available to facilitate data collection, visualization, and analysis. These tools range from simple command-line utilities to sophisticated monitoring systems with dashboards, alerts, and advanced analytics capabilities. Examples include Prometheus, Grafana, Nagios, and Datadog.
Real-time Monitoring: Real-time monitoring focuses on capturing and analyzing data as it happens, providing immediate insights into the system’s current state. It enables quick identification and response to critical issues, such as service outages or performance bottlenecks.
Historical Monitoring: Historical monitoring involves analyzing collected data to identify trends, patterns, and long-term performance characteristics. It helps in capacity planning, trend analysis, and predicting future resource requirements.
Alerting and Notifications: Monitoring systems often include alerting mechanisms that notify designated individuals or teams when predefined thresholds or conditions are met or exceeded. We can send alerts via email or SMS or integrate them with collaboration tools like Slack, allowing quick response and resolution of issues.
Visualization and Dashboards: Monitoring tools visually represent collected data through dashboards and charts. These visualizations make understanding and interpreting complex data sets easier, enabling stakeholders to assess the system’s health and performance quickly.
Performance Optimization: Monitoring data can identify performance bottlenecks, resource inefficiencies, or areas for optimization. By analyzing the collected metrics, administrators and developers can make informed decisions and implement improvements to enhance the system’s overall performance and efficiency.
In general, monitoring is essential in ensuring the proper functioning, performance, and availability of software applications and systems. It involves collecting and analyzing data in real-time or over a period to detect issues, identify trends, and optimize performance. Organizations can proactively address problems, maintain stability, and make informed decisions to improve their IT infrastructure by monitoring systems.
Metrics
Metrics refer to the specific measurements or data points that are collected and analyzed to assess the performance, health, and behavior of a system or application. These metrics provide quantitative information about various aspects of the system and are used to track its performance, detect anomalies, and make informed decisions.
Metrics can include a wide range of data, depending on the specific system or application being monitored. Some common examples of metrics in monitoring include:
Performance Metrics: These metrics measure a system or application’s speed, efficiency, and responsiveness. Examples include response time, throughput, CPU usage, memory utilization, and network latency.
Availability Metrics: These metrics indicate the uptime and availability of a system or service. For example, we can have metrics such as uptime percentage, downtime duration, or the number of service outages.
Error and Exception Metrics: These metrics track the occurrence and frequency of system errors, exceptions, or failures. Examples include error rates, exception counts, or specific error codes.
Resource Utilization Metrics: These metrics measure the usage and allocation of system resources such as CPU, memory, disk space, and network bandwidth. They provide insights into resource consumption patterns and can help optimize resource allocation.
User Experience Metrics: These metrics measure the quality and satisfaction of user interactions with a system or application. Examples include page load time, click-through rates, conversion rates, and user feedback ratings.
Security and Compliance Metrics: These metrics assess a system or application’s security posture and compliance adherence. They can include metrics related to security incidents, vulnerability scans, access controls, or regulatory compliance checks.
Monitoring tools and platforms collect and analyze these metrics over time to generate reports, visualizations, and alerts. They help stakeholders understand the system’s performance, identify issues, and make data-driven decisions to improve its reliability, efficiency, and user experience.
Tracing
Tracing refers to capturing and analyzing the flow of execution and interactions within a system or application. It involves tracking the path of requests or transactions as they traverse various components or services.
It allows for detailed visibility into how they are processed and where potential issues or bottlenecks may arise.
For example, if a Microservice creates a request for another Microservice, we will have the tracing from the request trace. So, if there is a request for 5 Microservices, we will get the tracing of all of those Microservices.
Tracing provides a comprehensive view of the end-to-end journey of a request or transaction, including the different services or modules it traverses, the time taken at each step, and any errors or delays encountered along the way. This visibility helps identify performance bottlenecks, diagnose issues, and optimize system behavior.
Here are key aspects of tracing in monitoring:
Distributed Tracing: Distributed tracing focuses on capturing and correlating information across multiple components or services involved in processing a request. It allows for tracking the path of a request as it moves through different systems, enabling the identification of performance issues or dependencies between services.
Tracing Instrumentation: Tracing requires adding instrumentation to the code of an application or system to capture relevant information at different stages of execution. Tracing can involve adding trace statements or using specialized tracing libraries or frameworks that automatically capture essential data points, such as timestamps, method invocations, or network calls.
Trace Data and Context: Tracing generates data that includes unique identifiers for requests or transactions, as well as contextual information about each step, such as timestamps, service names, and metadata. This data is collected and organized to form a trace, representing the complete journey of a request or transaction.
Trace Visualization and Analysis: We typically visualize traces in tools or platforms that provide insights into the behavior and performance of a system. Visualization tools can display the flow of requests, highlighting potential bottlenecks or errors. Analysis features allow drilling down into individual traces to examine specific steps, timings, and dependencies.
Performance Optimization: Tracing data can be used to identify performance issues and bottlenecks within a system. By analyzing trace information, developers and administrators can pinpoint areas contributing to slow response times, high latency, or resource inefficiencies. It also enables them to make targeted improvements and optimizations to enhance overall system performance.
Tracing complements monitoring by providing detailed information about the execution and behavior of specific transactions or requests. It helps in understanding the end-to-end flow of a system, diagnosing issues, and optimizing performance and reliability.
Technologies to Use Logging and Monitoring
Several technologies and tools are available for logging and monitoring in software development and system administration. Here are some commonly used ones:
Logging Technologies:
Log4j: A widely used Java-based logging framework that allows developers to log events and messages at different levels of severity.
Serilog: A versatile logging library for .NET that supports structured logging and allows logs to be stored in various formats and destinations.
Winston: A popular logging library for Node.js that provides flexible logging options and supports various transports, such as console, file, or external services.
Monitoring Technologies:
Prometheus: An open-source monitoring system that collects metrics from targets using a pull model and provides powerful querying, alerting, and visualization capabilities.
Grafana: A widely used open-source platform for visualizing and analyzing time-series data from various data sources, including Prometheus, Elasticsearch, and InfluxDB.
Nagios: A robust open-source monitoring tool that enables monitoring of hosts, services, and network devices, and provides alerting and reporting features.
Datadog: A cloud-based monitoring and analytics platform that offers comprehensive monitoring, alerting, and visualization capabilities for infrastructure, applications, and logs.
Cloud Monitoring Services:
Amazon CloudWatch: A monitoring and observability service provided by Amazon Web Services (AWS) that collects and analyzes metrics, logs, and events from various AWS resources and applications.
Google Cloud Monitoring: A monitoring and observability service offered by Google Cloud Platform (GCP) that provides monitoring, alerting, and visualization capabilities for GCP resources and applications.
Azure Monitor: A comprehensive monitoring solution provided by Microsoft Azure that collects and analyzes telemetry data from Azure resources, applications, and infrastructure.
Logging and Monitoring Aggregation:
ELK Stack (Elasticsearch, Logstash, Kibana): A popular open-source stack for centralized logging and monitoring. It uses Elasticsearch for log storage and searching, Logstash for log processing and ingestion, and Kibana for log visualization and analysis.
Splunk: A powerful log management and analysis platform that allows organizations to collect, index, and analyze data from various sources, including logs, metrics, and events.
These are just a few examples of the many logging and monitoring technologies available. The choice of technologies depends on factors such as the programming language, the infrastructure being used, specific requirements, and the scale of the system.
Conclusion
Logging and monitoring are necessary for every cloud service to give us more control over our applications. As software engineers, we need to know what logging and monitoring technologies we should use and how to use them.
Logging:
- Log4j, Serilog, Winston are popular logging technologies.
- Logging records events and messages during application execution.
- Helps understand application behavior, diagnose issues, and improve performance.
- Captures information for monitoring, debugging, analysis, and auditing.
Monitoring:
- Prometheus, Grafana, Nagios, Datadog are common monitoring technologies.
- Continuous observation and measurement of system/application aspects.
- Detects anomalies, identifies trends, and provides insights into system health.
- Ensures proper functioning, performance, and availability of software and infrastructure.
- Proactively identifies and addresses issues, minimizes downtime, and optimizes resource utilization.
Cloud Monitoring Services:
- Amazon CloudWatch, Google Cloud Monitoring, Azure Monitor.
- Collects and analyzes metrics, logs, and events from cloud resources and applications.
Logging and Monitoring Aggregation:
- ELK Stack (Elasticsearch, Logstash, Kibana) for centralized logging and monitoring.
- Splunk for log management and analysis.
- Choice of technologies depends on programming language, infrastructure, and specific requirements.
