Having small specialized services working together to achieve a business goal is better than a giant monolithic service that does everything. That is the core premise of the microservices architecture.
However, monitoring microservices quickly becomes a challenge at scale…in comparison to a monolith where you only have to look in one place.
This article presents an architecture for managing logs that achieves the simplicity of a monolith, without sacrificing the robustness of microservices.
You can find an actual implementation of the concepts discussed here on my Project Horus repository. Oh, and there’s a hidden message in the illustration above, see if you can find it, otherwise keep reading till the end.
Suppose you have an e-commerce system powered by a bunch of microservices like Authorization, Product catalog, and Billing. Now imagine a customer’s checkout process fails, how would you go about determining the cause? You could check the logs of each microservice and eventually find the one with issues. However, this does not scale past a few services.
The more practical approach is to gather the logs from each microservice in a central searchable database. That way when something breaks, you have the complete story easily, hence decreasing your Mean Time To Repair (MTTR).
Although microservices is just an architectural pattern, today the term is almost synonymous with containers. Whereas people have been building microservice systems long before containers became famous, these systems were difficult to deploy…this is where container platforms like Docker came to the rescue.
The rest of this article focuses on a concrete example of microservices implemented with Docker containers. However, the core knowledge applies to alternative platforms like Kubernetes and Mesos.
Let us dive in.
Each setup has its pros and cons, and an extensive treatment of each could make this article too long. So, let us go over some best practices that will form the foundations of our architecture.
So lets now look at an architecture that achieves these goals.
There is quite a bit going on in the diagram above, so let us approach it top-down.
We have a couple of microservices each running in a container. The logs of each service are forwarded to a log-driver which eventually sends them to a dedicated log-shipping container. The shipper can manipulate the logs before persisting them in a store. Finally, the developer can query this datastore to visualize and analyze the logs. That’s the main gist.
Though, I am handwaving a bunch of stuff here: how do the containers ‘know’ to send the logs to a log-driver. In fact, what is a log-driver? Also, aren’t we breaking one of the aforementioned best practices–which is that a microservice should not know where its logs are going? Why are we using a dedicated log-shipping container? What is Fluentd, Elasticsearch, and Kibana?
Let us answer these questions.
docker info, then search for “Logging Driver” and “Plugins”.
Aha! Now you understand why it is a best practice for microservices to log to stdout. It makes them very portable because we delegate the responsibility of log routing to the environment, that is the docker daemon and log-driver.
The critical thing to note here is that we are treating the logs as a stream by channeling it to the final persistent store without storing it intermittently in files.
We use a dedicated log shipping container so we can centralize business logic like obfuscating Personally Identifiable Information (PII) or changing the log format. You want a single source of truth for such logic. Additionally, having everything containerized avoids needing to install a log shipping agent on the host machine.
Finally, we store the logs in Elasticsearch instead of a regular file or database because Elasticsearch is a search engine designed to index and efficiently retrieve large document collections. Kibana is a frontend for Elasticsearch. It can be used to create charts and dashboards from the query results of ES.
We’ve examined the problem of getting insights into the operations of microservices in a containerized environment. Then we proceeded to evaluate a few logging strategies and some best practices. Finally, we brought this all together by looking at an actual implementation that solves most of the problems.
Thanks for reading so far, I hope this helps you with your next microservices project. If you think I missed something, please let me know in the comments.
Oh, about the challenge to find the hidden message in the featured illustration. The image shows the “Observer” in the Situation Room of the 51 Pegasi System. The connected microservices are actually stars forming the constellation Libra and Virgo from left to right. These are the zodiac constellations of September —the month this article was published.
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