A log analyzer allows you to quickly search a specific set of data in a haystack of records.
Think of it like bookkeeping. Accounting books tell a story for each transaction made in a period of time. To search through those transactions, you need criteria like the date and amount paid. You find the transaction log and it has more information: payment method, recipient, plus other details.
That’s what a log analyzer does: it helps you find events in a collection so you can study them and fix problems.
Log Analyzer, an Ideal Case:
- An incident report provides data for log analysis.
- Someone reproduces this issue based on your log data.
- Your development team proposes a solution in a new fixed app.
- Someone reproduces the incident again, this time using your new app.
- Log analysis and testing show whether this fix was effective or not.
In this article, I’ll share insights from my experience with log analysis. Read on, draw parallels with your organization, and infer your own conclusions. Learn how a log analyzer helps you.
Collect, Aggregate, and Process Logs
Let’s go back to our bookkeeping analogy, but imagine you’re keeping the books for many people, like a bank. Banks maintain thousands of accounts, recording transactions in ATMs, shops, online banking, etc. All these records are consolidated in a single source of truth.
In the software world, we call this single source of truth a “centralized log,” which allows you to
- collect relevant details from transactions happening in a certain scope;
- put them all in the same place under standard rules for common fields like the date; and
- implement a log analyzer to index, search, sort, filter, and visualize data in meaningful ways.
Start With Standard Services, Save Business Applications for Later
Most log analyzers have sets of plugins that gather data from standard tools like web servers, databases, and operating systems. The plugins require minimal setup time and they’ve been extensively tested. Implement these first so you can see quick results with low effort.
Now it’s time to feed your application logs to your log analyzer.
Review the documentation and choose a supported log format. With Scalyr, this process involves uploading files, providing configuration for each log, and then manipulating your data based on your needs.
Some analyzers allow you to add third-party sources to your analysis. This empowers you to correlate your application logs with other data sources. For example, your business may benefit from knowing how your “active user” count affects your AWS bill over time.
Make Logs Useful, Provide Context
Once you have all the pieces in place, users of your log analyzer will be able to visualize logs for their applications. In some cases, they will be able to read logs for applications they didn’t write. Consider new hires; this tool will be useful only if they have enough context to understand it.
Infrastructure and programs vary among teams, but here’s a list of metadata that you might want to add to your logging:
- Services and API versions
- Source environment
- Docker instance ID
- Kubernetes cluster and pod ID
Additionally, you can ask developers to analyze logs for applications they don’t maintain. This process will provide feedback so that you know what elements are missing from your centralized logging implementation.
Validate With All Your Test Methods
Whether it’s functional, load, unit, regression or any other form of testing, validate with your teams if they are getting valuable information from your log analyzer. Each of these processes has different purposes; it’s reasonable to assume that some of their requirements escaped your initial assessment. Keep this feedback loop open so your logging capacity stays in line with your codebase growth.
Decrease Noise, Allow Dynamic Tuning
Some developers maintain lists of “safe errors” in their application logs, a form of technical debt that adds unnecessary glut.
Maintaining a list of “errors you should ignore” is like hiding dust under the rug. There’s trash there, but you don’t want to put it away. Encourage developers to remove safe error lists because each log line written, sent over the network, and stored somewhere is a cost.
Dynamic tuning is also a useful feature, as it allows you to change application log levels on the fly. You don’t need abundant detail when things are going well, but debug logging is vital while troubleshooting issues that might vanish if you restart a failing service.
Enable Everyone to Log and View
One strategy to increase the usefulness of a log analyzer is to build sets of graphics directly related to your core business KPIs. While logging is inherently technical, it holds valuable information for monitoring your costs and operating expenses.
Talk to your direct manager about this; he’ll probably drool at the possibilities.
Whether your organization has grown under DevOps practices or you’re in a transition phase, visibility and transparency are principles you want to promote. Try to introduce this tool as a means towards better software. You also need to discourage any effort to make it a source of blame; instead present it as an opportunity to look for improvements.
You may propose logging as a requirement that applications need to comply with before going live. Provide development teams with specifications they need to meet earlier in their process. Enforce these specifications with automated testing in your delivery pipeline.
Define How Much Data History You Want
There are a handful of factors to consider when deciding how long you want to keep logs of your applications.
Legal requirements in certain industries and countries will force you to store logs for a minimum amount of time. Hard disk space might be cheap these days, but storing all your logging in the same place for years will make your analysis process slow and more expensive.
Help your business define what it considers to be “old log data” and design an archiving strategy.
This could consist of compressing data or storing it in a cheaper storage tier. Document a process to recover this data when necessary and inform your users of how much time it’ll take to make this data available to them. All things considered, I would make sure not to lose granularity when archiving; you might lose valuable information that could be used as a baseline in the future.
Add Chaos Under Safe Conditions
At the time of writing, chaos engineering—introducing failure into your application infrastructure in a controlled way—is not yet a widespread trend. Chaos engineering helps show developers how their code fails when an unexpected event destroys running instances.
If that sounds scary, implement it in a staging environment modeled to be as similar as possible to your live website. Validate if your log analyzer setup is useful for troubleshooting during induced and real service outages.
Log Analysis Is Never 100% Done
It’s important to create a continuous feedback loop that will help you keep your log strategy sharp as it grows and adapts to new services and metrics. Otherwise, your applications and features will grow and your log scope will be left behind.
No matter what you choose as log analyzer, strive to provide your organization with means to pinpoint their inefficiencies and make their work more efficient. Guide them to contribute their knowledge to your logging strategy, thus increasing the benefits they get from it.
This post was written by Carlos “Kami” R Maldonado, who is an engineer helping his company transition to DevOps. He specializes in Linux automation, and he’s experienced in all layers of infrastructure, from the application layer down to the cable. He’s transitioning from static VM based infrastructure to on-premises Kubernetes deployments.
More action. Less Noise
Get a weekly dose of news from the observability world.Close