There are few processes as important to ongoing API operations as API logging. For teams that look to get better visibility and awareness of their systems, there are few better options. Today, we’re going to dive into API logging and discuss what it is, what it does, and what you should consider before implementing it.

What is API Logging?

APIs are communication mediums connecting different systems together. They’re best thought of as a collection of interconnected systems, transmitting data, status, and more over direct and indirect channels. API logging is the process of recording these transactions, typically by capturing all of the requests and responses at a specific endpoint in the chain.

Logging effectively allows you to create a record of the sum total of interactions in a given system. This has some pretty obvious benefits in terms of observability, as logging opens up a whole world of metacontext to your data and transactions.

For example, logging can unlock visibility into the flow of data from one point to another for a given request type, isolating the points of friction in the system as well as highlighting the typical flow of such requests from system to system with respect to authorization.

This kind of information can help you improve the specific traffic flow, and can also help to directly highlight faults in data transit, authorization, inefficiencies in caching, and so forth. API logging is the most effective thing you can do to boost your base visibility and observability, but it also has significant benefits in the form of debugging and auditing.

With ample API logging, you can take a failed data exchange and go from point to point, digging into each stage of the interaction to debug the problem itself. Using tools such as traffic capture and replay, you can even use this logged data to do A-B testing on potential fixes and implementations that would mitigate the problem. You can then use this data to audit the entire system, testing your implementation across the board to ensure that you haven’t logged this issue elsewhere.

What to Collect: And How Long to Keep It

When we discuss logging, one piece of the conversation that often gets secondary consideration is the question of what should actually be collected, and how long it should be held. It’s quite alluring to view logging as a central collection point and just set it up to collect everything, but doing this can be a risk in and of itself.

Firstly, there is the very real risk of data collection in terms of potential exposure. Data collection necessarily creates a highly attractive and valuable centralized data source that, if breached, could lead to everything from reputational harm to punitive financial measures.

This is not a core argument against the collection, but is instead a very good argument for encrypting your data. With proper encryption, this risk is somewhat mitigated in the sense that encrypted data, if exfiltrated, is less likely to be used by criminals due to the difficulty in decrypting it.

There’s also a very real privacy argument to be made. Collecting anything and everything coming into the systems could catch a wide variety of requests and flows that could expose private information, ranging from the innocuous (such as requesting IP) to the more serious (such as accessing data or secrets).

Your best bet, then, is to collect only the data that will actually be useful for your analysis. Leveraging a tool for collection that allows you to set filters and change what is actually being collected can be extremely helpful, and can help you isolate certain kinds of traffic or interactions that are best left ephemeral.

All of this said, providers should consider not just what they collect, but how long they keep that data. You should only collect that data which is necessary to collect, but you should also only keep it for as long as it is useful. Having data sitting in a collection point for months or years without secure, encrypted storage can create a goldmine for malicious actors and can lead to ever-mounting risks for your service that generate minimal benefit in return.

Common Approaches for Logging

Now that we have a firm grasp of why we would want to do logging, let’s look at some common approaches to implementing this in a production service.

Middleware Logging

Middleware logging is one of the more common implementations of logging because of how simple it is to deploy. In essence, you develop a middleware client that sits directly between your users and your API or application code. The service then collects all data that flows through it like a man-in-the-middle, allowing you to log everything that passes through.

Benefits

• This approach gives you significantly fine-grained controls over what gets logged, as you can implement code in the middleware to filter, allow, and disallow everything from headers to payloads.
• Middleware tends to be the easiest to implement, typically offering a “drop-in” solution.
• Because it’s so simple, you typically don’t need any additional infrastructure.

Drawbacks

• Since middleware necessarily adds more to the processing time, it can introduce a performance overhead if not optimized.
• Middleware requires consistent implementation across all services to be universally helpful and comparable between endpoints.
• Middleware solutions can be harder to centralize in distributed environments.

Common Tools

• Express.js Middleware (morgan, winston)
• Django Middleware
• Spring Boot Interceptors

API Gateway Logging

An API gateway is essentially a universal middleware. Instead of having a middleware implementation for each service or endpoint, the API gateway collects all interactions and all data with all services through a single unified endpoint. This highly centralizes the logging process, but does introduce some concerns around specificity.

Benefits

• Centralized logging for all API calls can reduce logging complexity in microservices, presenting a single endpoint for collection.
• Since you have a single logging service, this integrates really well with external log aggregators, as you only need a single outbound connection.
• Unlike standard middleware, you don’t really need to modify API code as much as route your flow through the endpoint in question.

Drawbacks

• Having a single gateway means you have simpler code structures, but it also means things are more generalized, removing a lot of application-specific logging logic.
• This also introduces a single point of failure that, if it collapses, could take down your entire system.
• API gateway implementations typically bill on a volumetric basis, meaning high-volume traffic might quickly become prohibitively expensive.

Common Tools

• AWS API Gateway + CloudWatch
• NGINX with logging modules
• Apigee Analytics and Logging

Sidecar Logging (Service Mesh)

Sidecar logging creates a service mesh in which logs are captured by a service running alongside each microservice instance. This is somewhat more common in ephemeral services, where the logging process can be packaged with the containerized implementation and deployed or spun down when needed.

Benefits

• This process decouples the logging from the application code, allowing the logging process to stand alone.
• This standalone nature means that sidecar logging works really well in Kubernetes and other containerized environments.

Drawbacks

• This process obviously introduces substantial infrastructure and code overhead, as it requires a large code cost.
• In addition to the code cost, there’s obvious complexity that follows, both in terms of setup and management.

Common Tools

• Linkerd + Prometheus/Grafana
• Consul Connect with integrated logging

Client-Side Logging

In this style of logging, logs are created via the client or via the SDK used by the client making the request. These logs can then be shared for debugging, processing, or other means.

Benefits

• This is very useful for abstracting the cost of logging, as logs are typically only shared when a catastrophic client error requiring them for analysis occurs.
• This is a good complementary process for server logs, as it provides visibility on the client side of the client-server dichotomy.
• Client-side logging also means that no changes are required on the server side, which can be simpler in some applications.

Drawbacks

• Client-side logging is only a half solution focused on what part of the data flow, so it requires server performance and backend tracking to be really useful.
• This approach can introduce security risks as it records a lot locally — beyond individual data exposures, this can also make it easier for malicious actors to use local reports to improve attack vectors.
• Unfortunately, this is only a half solution — and client-side logging can create more issues (such as fragmented logs) unless centrally managed.

Common Tools

• Mobile SDK logging (e.g., Firebase Crashlytics with API tracking)
• Browser dev tools or network monitoring tools

Event-Driven / Async Logging

In this type of logging, API logs are pushed to a queue or an event stream instead of being synchronously monitored. This allows for logging to happen separately from processing, increasing overall speed and making for greater logging flexibility.

Benefits

• Due to the nature of async logging, this doesn’t introduce any substantive blocks and only mildly impacts latency.
• This process can handle quite large volumes of logs without impacting API performance significantly.

Drawbacks

• Event-driven solutions can require a more complex setup between queues and consumers.
• By their very nature, these systems implement delayed visibility, even for those streaming “in real time,” there’s always going to be a slight delay.
• Within this process, you need to set up monitoring of the logging pipeline itself, which is a recursion that can get quite expensive.

Common Tools

• Kafka + Logstash
• AWS Kinesis + CloudWatch
• Google Pub/Sub + BigQuery

Conclusion (and a Word of Caution)

The best logging solution is going to be the one your team uses — but a word of caution is worth sharing here. Too many API developers view the data their systems generate as “their data”. In actuality, this data is owned and generated by your users and the interactions they give to your system. Accordingly, you should consider what regulations — if any — might cover this data.

Regulatory components like GDPR, CCPA, HIPAA, or PCI DSS might require very particular data logging practices and restrictions, and while these considerations are outside the scope of this article, they are very much worth considering. Make sure that you are not shooting yourself in the foot with this process — don’t rush to log and, in the process, forget that regulations may answer what you can collect (and how long you can keep that data) for you.