Updated: Jul 1
In a microservices-based application, there are multiple services running and interacting with each other. However, things can become complex when one or more services encounter issues, and it becomes crucial to identify the failed service(s) and understand the reason behind the failure. Additionally, it is important to comprehend the entire request flow, including which services were invoked, how many times, and in what sequence.
Logging in a single, unified application (monolith) is straightforward since you can simply publish data to a single log file and retrieve it later. However, when dealing with microservices-based applications that have logging capabilities, there are unique challenges that need to be addressed.
Microservices Logging Architecture
Here are the steps involved in utilizing Serilog and Azure services for logging:
Applications (Desktop, Web & Mobile), Operating systems, Azure resources (App Services, VMs, Active Directory, Function Apps, and Azure SQL, etc.), and Custom Data sources can utilize the Serilog Client library to write logging information.
The Serilog Client Library offers various links that allow emitting information to different Azure services in a structured and unified manner using templates.
Application Insights is an extensible application performance management service designed for developers and DevOps professionals. It can be used to query short-term (90 days) logging data. Serilog provides an Application Insights sink to send logging information from your applications. It's important to note that logging to Application Insights is asynchronous.
If your application requires sending real-time logging information to SIEM (Security Information and Event Management) tools, you can send Azure resource and operating system event log information to Azure Event Hub. Event Hub serves as a suitable option for forwarding this log information to external solutions.
To achieve real-time logging, you can create a function app trigger on the Event Hub resource to send the logs.
Microsoft Sentinel provides a comprehensive overview of cloud resources and can collect information from on-premise data centers. Sentinel offers a dashboard view of security events such as failed logins, abnormal activities, and relevant connections from these events. LogRhythm provides similar services as a SIEM tool.
Azure Storage Accounts or Data Lake Storage services can be utilized to store logs for long-term use and analysis. These services are cost-effective for log retention. The access tier for a storage account allows you to choose between Hot, Cool, and Archive options.
Analyzing the logging information can be done using Azure Log Analytics workspace queries using K-SQL (Kusto Query Language). These queries help in extracting meaningful insights from the logs.
Finally, a Kibana dashboard view can be created to present the results of Azure Log Analytics workspace queries, providing a visual representation of the data.
Microservices Logging Optionsenges
Logging challenges in applications include:
Multiple Logging Stores and Formats: Components within applications often have their own native log stores, resulting in different storage locations and logging formats. This lack of a common location and format makes it challenging to consolidate and analyze logs effectively.
Access Control: Controlling access to log information is crucial to ensure that only authorized individuals or systems can view and manipulate the logs. Implementing proper access controls and authentication mechanisms helps maintain the confidentiality and integrity of log data.
Retention Policy: Determining the appropriate retention period for log data is important to meet operational and compliance requirements. Different types of logs may have varying retention needs, and defining a consistent and efficient retention policy can be a challenge.
Data Policy Regulation and Security: Logging often involves handling sensitive data, including personally identifiable information (PII) or confidential business information. Compliance with data protection regulations, such as GDPR or HIPAA, adds complexity to logging practices and requires implementing adequate security measures to protect log data from unauthorized access or breaches.
Integration for Analysis and Alerts: Logs provide valuable insights for analysis, troubleshooting, and generating alerts for anomalous or critical events. However, integrating logs from various components and applications to enable comprehensive analysis and timely alerting can be challenging, especially when dealing with diverse logging formats and storage locations.
Microservice Logging Options
Below we have some Microservice logging options:
Unified Logging Library: Utilizing a unified logging library that supports the technology stack used within an organization can facilitate structured logging. Choosing a lightweight log format like JSON can be advantageous. Serilog, for example, offers sinks that enable writing events to various storage locations in different formats.
AZURE Metrics and Logs: Azure provides a wide range of metrics and logs, which can vary depending on the resource type. For instance, the logs stored for a virtual machine (VM) may differ from those stored for Azure SQL. Understanding the specific logging options available for each resource type can help tailor the logging strategy accordingly.
Observability Principle: Following the observability principle involves actively monitoring centralized logging in real-time to proactively address issues. Having a user interface (UI) to visualize log data can aid in identifying and resolving problems promptly. A central monitoring system can also be utilized to schedule alerts when metrics exceed defined service level agreements (SLAs) or deviate from expected tolerances, ensuring effective communication to engineers for timely resolution.
Leveraging Existing Azure Services: It is recommended to leverage existing Azure services as much as possible to achieve unified logging. Azure offers various services that can help consolidate and manage logs effectively, such as Azure Application Insights, Azure Monitor, and Azure Log Analytics. By utilizing these services, organizations can streamline their logging process and benefit from Azure's built-in capabilities.
Microservice Logging Best Practices
A Microservices architecture brings its own set of challenges when it comes to logging. Implementing the following best practices can help ensure effective logging in a microservices-based application:
Best Practice 1. Use a correlation ID
Generate and assign a unique correlation ID to each request as it enters the system. This ID should be propagated throughout the microservices involved in processing the request. By including the correlation ID in logs, troubleshooters can easily trace the flow of a transaction across different services and identify the service responsible for a failure.
Best Practice 2. Structure logs appropriately
Maintain consistency in log structures across different microservices. Adopt a structured logging approach that defines a standardized format for log messages. This ensures that logs can be parsed, searched, and analyzed uniformly, regardless of the service generating them. Structured logs simplify troubleshooting and facilitate automated processing of log data.
Best Practice 3. Provide informative application logs
When an error or exception occurs, logs should capture all relevant information for effective troubleshooting. Essential details to include in logs are the service name, username, IP address, correlation ID, timestamp in UTC, execution time, method name, and call stack. Comprehensive logs expedite problem diagnosis and resolution.
Best Practice 4. Visualize log data
Implement log visualization techniques to gain insights into the application's state, performance, and issues. Creating dashboards that visualize log data helps developers monitor the application's health, identify trends, and detect anomalies. Log visualization tools like Scalyr, Graylog, Kibana, or Sematext can assist in visualizing aggregated log information.
Best Practice 5. Use centralized log storage
Instead of managing individual log storage for each microservice, establish a centralized log storage solution. Sending logs to a single, dedicated location simplifies log retrieval, analysis, and correlation across multiple services. Centralized log storage enables efficient log management, facilitates investigation of problems, and streamlines the configuration of alerts based on log events.
Best Practice 6. Query logs
Ensure the ability to query logs effectively to identify failures that span multiple microservices. Utilize the correlation ID to retrieve the complete request flow within the application, enabling a holistic view of the transaction's journey. Querying log data allows for analyzing the percentage of failed requests, response times, and service call frequencies. Tools like the ELK stack, Splunk, or Sumo Logic can assist in aggregating and querying log data.
Best Practice 7. Handle failures
Implement an automated alert system that monitors logs and triggers alerts when issues arise. Configure alerts based on specific log events or anomalies to promptly identify and address service failures. Consider potential variations in logging component availability and adjust alerting based on different times of the day or expected fluctuations in system behavior.
Effective logging in microservices is important to ensure visibility, traceability, and troubleshooting capabilities across distributed systems. Microservice logging presents unique challenges due to the decentralized nature of services and diverse logging formats. However, by following best practices, organizations can overcome these challenges and establish a robust logging framework.