In this next series, I am going to enumerate a series of cross-cutting concerns amongst APIs and Microservices that require the attention of developers and specific capabilities to be managed within the API Microservices ecosystem either via a gateway platform or through other methods. There are two main areas of concern in APIs and Microservices that need a set of audit and logging capabilities.

These allow the user to get both a horizontal end to end audit of a request as well as a vertical audit of event handling within a set of containers servicing a specific API or microservice in the ecosystem. These are generic audit handlers that dump the most important info on a per request basis as defined in the configuration of the API or Microservice.

As an example, an audit module will typically log all the following information by default:. Usually working with tools like Splunk. I wanted to cover this cross-cutting concern first as this is a major area of concern for performance — especially when building monolithic API gateway solutions. Reporting and logging functions can generate high levels of chatter between APIs and Microservices with the Gateway if the gateway is being relied on to gather and aggregate these details on an ongoing basis.

Better to work on solutions that integrate the logging and audit mechanisms within the API Microservice itself to dump all the logging to a central storage facility and then let other out-of-band tools do the reporting and analytics. Relying on API Gateways for these audit and logging functions can severely limit the transaction volumes that can be processed for incoming requests on the gateway due to the excessive chatter required between the APIs and Microservices back to the gateway to keep these details flowing.

By Craig Borysowich.

Tools and Techniques for Logging Microservices

Craig Borysowich Principal Architect. Follow Tech Sign In Page. Forgot Password? Don't have an account? Sign up. Monday July 23, Favorites Tech Sign In Page. Bookmark Tech Sign In Page. Some name. Tech Sign In Page. Craig Borysowich. More by this Author. Artificial Intelligence Development Hardware.

Supporting Your Artificial Intelligence Solution. Be the first to comment Write Comment. Sign In to Post a Comment. Sign In. Something went wrong on our end. Please try again later. Sage Web API.An audit log is the simplest, yet also one of the most effective forms of tracking temporal information. The idea is that any time something significant happens you write some record indicating what happened and when it happened.

An audit log can take many physical forms. The most common form is a file. However a database table also makes a fine audit log. If you use a file you need a format. If it's a simple tabular structure, then tab delimited text is simple and effective. More complex structures can be handled nicely by XML.

Audit Log is easy to write but harder to read, especially as it grows large. Occasional ad hoc reads can be done by eye and simple text processing tools. More complicated or repetitive tasks can be automated with scripts. Many scripting languages are well suited to churning though text files. If you use a database table you can save SQL scripts to get at the information.

When you use Audit Log you should always consider writing out both the actual and record dates. As you do this remember that the record date is always the current processing date. The glory of Audit Log is its simplicity. As you compare Audit Log to other patterns such as Temporal Property and Temporal Object you quickly realize that these alternatives add a lot of complexity to an object model, although these are both often better at hiding that complexity than using Effectivity everywhere.

But it's the difficulty of processing Audit Log that is it's limitation. If you are producing bills every week based on combinations of historic data, then all the code to churn through the logs will be slow and difficult to maintain. So it all depends how tightly the accessing of temporal information is integrated into your regular software process.

The tighter the integration, the less useful is Audit Log. Remember that you can use Audit Log in some parts of the model and other patterns elsewhere. You can also use Audit Log for one dimension of time and a different pattern for another dimension. So you might handle actual time history of a property with Temporal Property and use Audit Log to handle the record history. Notice that even though the setting method only uses the actual time, I've also added the record date MfDate.

I think it's always wise to add both dates as it's easy to do and if you don't add it you can't reconstitute it later.

I'll leave the script for finding out my phone number on some arbitrary date as an exercise for the reader. Clearly it's too trivial for me to write out hereComment 1. In this article, we will examine some best practices to follow while logging microservices and the architecture to handle distributed logging in the microservices world.

As we all, know microservices run on multiple hosts. To fulfill a single business requirement, we might need to talk to multiple services running on different machines. So, the log messages generated by microservices are distributed across multiple hosts. As a developer or administrator, if you want to troubleshoot any issue, that leaves you clueless. Even if you know which host s served your request, going to different hosts and grepping the logs and correlating them across all the microservices requests is a cumbersome process.

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If your environment is auto-scaled, then troubleshooting an issue is unimaginable. Here are some practices that will make life easier when troubleshooting issues in the microservices world. As microservices are running on multiple hosts, you should send all the generated logs across the hosts to an external, centralized place.

From there, you can easily get the log information from one place. It might be another physical system that is highly available or an S3 bucket or any other storage option. If you are hosting your environment on AWS, you can leverage CloudWatch, and other cloud providers generally offer similarly appropriate services.

Generally, we put log messages with raw text output in log files. There are different log encoders available that will produce JSON log messages. That way, we will have the right data available in the logs to troubleshoot any issues. Below are some useful links to configure JSON appenders. If you are using Logstash as your log aggregation tool, then there are encoders you can configure to output JSON log messages.

Log the correlation ID across all microservice calls. That way, we can use the correlation ID coming from the response to trace out the logs. We should be using different log levels in our code — and have enough logging statements in the code as well. We should have the liberty to change the log level dynamically. That is very helpful for enabling the appropriate log level. We don't need to enable the least logging level to print all the logs during server startup.

We also avoid the overhead of excessive logging that way. Add asynchronous log appenders so that the logger thread will not be blocked by the request thread. If you are using Spring Cloud, then use Spring Boot admin to achieve dynamic log level changes. Make sure all the fields available in the logs are searchable. For example, If you get ahold of the correlation ID, you can search all the logs based on that t to find out the request flow.

Now, let's examine the architecture of log management in the microservices world. This solution uses the ELK stack.Blog Development.

By Andre Newman 04 Nov The microservice architecture is taking the tech world by storm. A growing number of businesses are turning towards microservices as a way of handling large workloads in a distributed and scalable way.

A microservice is an application consisting of multiple small, independent, intercommunicating components. Each component known as a service is modular, reusable, and self-contained and communicates with other services using language-agnostic protocols. Each task becomes a small, self-contained unit that communicates with other units through APIs. The difference between traditional monolithic applications and microservices is that:.

When logging microservices, you have to consider that logs are coming from several different services. Services work together to perform a specific function, but each service can be thought of as its own separate system. This means you can add your logging framework into the service itself, just as you would a regular application. Within each service, we can append information to each log event to help identify where the event took place. For instance, we could append a field to our logs that records the name of the service that generated the event.

This lets us easily group events from the same service.

audit logging in microservices

The downside to this approach is that it requires each service to implement its own logging methods. Not only is this redundant, but it also adds complexity and increases the difficulty of changing logging behavior across multiple services. In this model, services forward their logs to a logging service. Services still need a way to generate logs, but the logging service is responsible for processing, storing, or sending logs to a centralized logging service such as Loggly.

This image creates a container running a rsyslog daemon that listens for incoming syslog messages. You can even have multiple logging containers running simultaneously for load balancing and redundancy.

An alternative to implementing a logging solution for each service is to gather logs from the infrastructure itself. For instance, active Docker containers print log events to stderr and stdout. The Docker logging driver detects output generated on these streams, captures it, and forwards it through the configured driver. You can then configure a syslog driver that forwards log events from your containers directly to Loggly. Tools like logspout also provide a means of collecting and forwarding distributed log events.

Logspout is another Docker-based solution that runs as its own service. It attaches to other containers on the same host and routes events to a destination such as Loggly.

The benefit of tools like logspout is their ease of use: Simply deploy them alongside your other services and they immediately go to work.

This makes it extremely difficult to trace an error back to its source. The very nature of microservices makes it difficult to pinpoint the source of log events. For instance, you might come across a critical event in your log history, but how will you know which microservice generated the event? Without context, tracing logs quickly becomes a game of whodunit. An application using a logging framework stores attributes such as severity, the class and method that generated the event, and relevant content such as stack traces and exception messages.

This is great for a monolithic program, but microservices add another dimension.This article describes best practices for monitoring a microservices application that runs on Azure Kubernetes Service AKS. In any complex application, at some point something will go wrong.

In a microservices application, you need to track what's happening across dozens or even hundreds of services. To make sense of what's happening, you must collect telemetry from the application. Telemetry can be divided into logs and metrics. Logs are text-based records of events that occur while the application is running.

They include things like application logs trace statements or web server logs. Logs are primarily useful for forensics and root cause analysis.

Metrics are numerical values that can be analyzed. You can use them to observe the system in real time or close to real timeor to analyze performance trends over time. To understand the system holistically, you must collect metrics at various levels of the architecture, from the physical infrastructure to the application, including:.

Node-level metrics, including CPU, memory, network, disk, and file system usage. System metrics help you to understand resource allocation for each node in the cluster, and troubleshoot outliers. Container metrics. For containerized applications, you need to collect metrics at the container level, not just at the VM level. Application metrics. This includes any metrics that are relevant to understanding the behavior of a service. Examples include the number of queued inbound HTTP requests, request latency, or message queue length.

Applications can also create custom metrics that are specific to the domain, such as the number of business transactions processed per minute. Dependent service metrics.

audit logging in microservices

Services may call external services or endpoints, such as managed PaaS services or SaaS services. Third-party services may or may not provide any metrics.

If not, you'll have to rely on your own application metrics to track statistics for latency and error rate. Use Azure Monitor to monitor the overall health of your clusters. The following screenshot shows a cluster with critical errors in user-deployed pods. From here, you can drill in further to find the issue. For example, if the pod status is ImagePullBackoffit means that Kubernetes could not pull the container image from the registry.

This could be caused by an invalid container tag or an authentication error trying to pull from the registry. For a typical scenario where a pod is part of a replica set and the retry policy is Alwaysthis won't show as an error in the cluster status. However, you can run queries or set up alerts for this condition. We recommend using Azure Monitor to collect and view metrics for your AKS clusters and any other dependent Azure services. For cluster and container metrics, enable Azure Monitor for containers.

When this feature is enabled, Azure Monitor collects memory and processor metrics from controllers, nodes, and containers via the Kubernetes metrics API. For more information about the metrics that are available through Azure Monitor for containers, see Understand AKS cluster performance with Azure Monitor for containers. Use Application Insights to collect application metrics. To use it, you install an instrumentation package in your application. This package monitors the app and sends telemetry data to the Application Insights service.Comment 0.

The microservice architecture is taking the tech world by storm. A growing number of businesses are turning towards microservices as a way of handling large workloads in a distributed and scalable way. A microservice is an application consisting of multiple small, independent, intercommunicating components.

Each component known as a service is modular, reusable, and self-contained and communicates with other services using language-agnostic protocols. Each task becomes a small, self-contained unit that communicates with other units through APIs. When logging microservices, you have to consider that logs are coming from several different services. Services work together to perform a specific function, but each service can be thought of as its own separate system.

This means you can add your logging framework into the service itself, just as you would a regular application.

Tools and techniques for logging microservices

Within each service, we can append information to each log event to help identify where the event took place. For instance, we could append a field to our logs that records the name of the service that generated the event. This lets us easily group events from the same service.

The downside to this approach is that it requires each service to implement its own logging methods. Not only is this redundant, but it also adds complexity and increases the difficulty of changing logging behavior across multiple services. In this model, services forward their logs to a logging service. Services still need a way to generate logs, but the logging service is responsible for processing, storing, or sending logs to a centralized logging service such as Loggly.

This image creates a container running a rsyslog daemon that listens for incoming syslog messages. You can even have multiple logging containers running simultaneously for load balancing and redundancy.

An alternative to implementing a logging solution for each service is to gather logs from the infrastructure itself. For instance, active Docker containers print log events to stderr and stdout.

The Docker logging driver detects output generated on these streams, captures it, and forwards it through the configured driver. You can then configure a syslog driver that forwards log events from your containers directly to Loggly. Tools like logspout also provide a means of collecting and forwarding distributed log events. Logspout is another Docker-based solution that runs as its own service. It attaches to other containers on the same host and routes events to a destination such as Loggly.

The benefit of tools like logspout is their ease of use: Simply deploy them alongside your other services and they immediately go to work. This makes it extremely difficult to trace an error back to its source.Chris helps clients around the world adopt the microservice architecture through consulting engagements, and training classes and workshops.

Sign up to learn more. Chris teaches comprehensive workshops, training classes and bootcamps for executives, architects and developers to help your organization use microservices effectively.

Avoid the pitfalls of adopting microservices and learn essential topics, such as service decomposition and design and how to refactor a monolith to microservices. Want to see an example?

audit logging in microservices

Check out Chris Richardson's example applications. See code. Engage Chris to create a microservices adoption roadmap and help you define your microservice architecture. Use the Eventuate. Eventuate is Chris's latest startup. It makes it easy to use the Saga pattern to manage transactions and the CQRS pattern to implement queries.

Engage Chris to conduct an architectural assessment. Alternatively, conduct a self-assessment using the Microservices Assessment Platform. Join the microservices google group. Microservice Architecture Supported by Kong. Pattern: Audit logging Context You have applied the Microservice architecture pattern. Problem How to understand the behavior of users and the application and troubleshoot problems?

Forces It is useful to know what actions a user has recently performed: customer support, compliance, security, etc. Solution Record user activity in a database. Examples This pattern is widely used. Resulting Context This pattern has the following benefits: Provides a record of user actions This pattern has the following drawbacks: The auditing code is intertwined with the business logic, which makes the business logic more complicated Related patterns Event Sourcing is a reliable way to implement auditing.

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LEARN about microservices. Chris offers numerous resources for learning the microservice architecture. Training classes Chris teaches comprehensive workshops, training classes and bootcamps for executives, architects and developers to help your organization use microservices effectively. Delivered in-person and remotely. Ready to start using the microservice architecture? Consulting services Engage Chris to create a microservices adoption roadmap and help you define your microservice architecture, The Eventuate platform Use the Eventuate.

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