One of the first items you learn as a professional engineer is about the presence [and over presence] of logs. On your personal laptop, you might have come across logs in a crash report your operating system vendor collects. Though as engineers as we enter the professional world, the console output of our university projects need to be captured somewhere; enter logs.

Logs are certainly big business and there is a bloom of logging frameworks out there. In the days gone by logs have not been the most fun non-functional requirement to work on. As our systems become more distributed, understanding the health and performance of our systems require systems themselves. Logs can be seen as a foundation into the insight of events in our systems.

What is log?

Not to be confused with the wood variety or a logarithmic function, logs are pieces of event output that our systems produce. Since code is literally a set of instructions, we have the opportunity to log the interactions of all of those instructions. 

Certain interactions are more important to capture than others. Verbosity or granularity of your logging statements is an engineering decision point. Typically in lower environments when you are creating something new, you have a more verbose set of logs meaning you capture more to validate and debug. As we move towards production, we tend to have less verbose logs to only capture critical events. If there is a problem, logging verbosity is typically is increased again in an environment for a short period of time e.g production until a resolution. 

As systematic records, logs can be processed by humans or more often than not other systems. There are purpose-built tools for log aggregation such as Splunk and we can forward our logs to other systems such as an ELK [Elastic, Logstash, Kabana] stack. The simple log file has grown more complex as our systems have grown more complex. 

Why are logs hard?

Logs come in all shapes and sizes. Usually up to the engineer to decide what and when log. Most likely as part of a development standard, logging standards and coverage are set. Though when building an application or a piece of infrastructure, the actual implements of the logging is up to the engineer.  

We generate so many events, logs can also be seen as a bottleneck. Imaging a high value and low latency messaging system, if you were to log the content of each message, the system needed to maintain the logs at velocity would require as much computing power than the messaging system itself.  

Each application, platform, system, etc generate different logs in different places. For example, your application might have application specific events that write to web server logs that interact with a database that also has logs/records which reside on an operating system writing to systemd. Most likely there are more logs than that in your application in your infrastructure
stack. You can see how easily the logs can add up. 

Log content can also be taxing for the system needing to generate the logs. For example, if you include a full stack trace [the linage of the calls being made], it requires more overhead than a simple message. Over one of two events, not a big deal but for multiple users in the system that is distributed, the overhead will certainly add up. 

With so much firepower needed to capture every event properly, the math starts to creep in with statistical significance. Modern applications and platforms are designed to be elastic and scale up and down depending on the workload. Because of scale, does it make sense to fully monitor every running container? Perhaps we can take a small set which is still statistically significant as a sample e.g one out of three.  

Depending on the language there might be other methods to instrument e.g in JAVA you can instrument the byte code thus how Application Performance Management [APM] vendors were born. APM vendors also have sophisticated ways of sampling other than looking at log data. Logs do take time to get written and aggregated which are part of the logging layers. 

Logging Layers

Logging methodology and architecture certainly take consideration. Unlike my belief as a young engineer that logs just magically appear, they are part of the system and have design considerations. In this excellent Medium Post, the author goes through logging in a modern application stack and what to do with the logs once you have them. 

We can classify logging systems into three layers. Functionally you need to intercept, route/forward, and aggregate your logs.  

The job of a logging interceptor is to decide what events or instructions need to be captured and at what verbosity. Should items that are fatal be captured or should items that are more informational be captured. This is usually configured in an interceptor to look out for log statements in the codebase. 

Your logs have to go somewhere even if that is a text file on your local system. The job of a logging router is to do just that, to route where the logs get written to and ultimately delivered to. More often than not, logs are headed to another system for processing and retention. Potentially a big data system to look out for anomalies quicker or a database for compliance and retention. 

In life, there are a few absolutes with death, taxes, and you will have more than one log. Aggregators are common now as our systems that our users transverse are distributed. The job of a log aggregator is to find relationships between the dozens of potential logs and present them in a concise or conclusion based way. 

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