What is Log Visualization? Getting Started and Best Practices

Logging aims to collect penitent details about your application environment so that you can understand how it behaves and interacts with other components in your environment.

Once your systems are instrumented to emit all kinds of useful information into its logs, they are transformed from mysterious black boxes into a transparent, comprehensible system.

That said, it's still your job to make sense of the information contained in the logs. In today's computing environments, where vast amounts of data are generated, simply sifting through text files or similar methods is inefficient and time-consuming.

This is where log visualization comes in to help you extract insights from your logs that might be difficult, if not impossible, to obtain through conventional methods.

This article aims to provide a comprehensive overview of log visualization and how it can help you enhance your log management process.

What is log visualization?

Log visualization is the process of graphically representing log data to make it easier to analyze and understand.

It's not just about having aesthetically pleasing graphs and charts but feeding your raw log data into operational dashboards, making it possible to see and understand how your application is performing at a glance.

Visualization also works hand in hand with alarms, which trigger and notify you when the system is behaving unexpectedly. You can subsequently dig into the details of your logging instrumentation to quickly diagnose why things went wrong.

From there, you can solve the problem and put measures in place to prevent it from happening again. Without visualizing the information in your logs, you'll end up wasting precious time diagnosing problems.

Why is log visualization important?

At a high level, log visualization enables the aggregation of log data, which are usually dense and packed with information into key metrics that can be graphically represented, making spotting patterns, trends, and anomalies easy.

These aggregated metrics provide a comprehensive overview, allowing for monitoring of key aspects such as the error rate, latency of API requests, query performance, and more.

Plotting these metrics makes it easy to spot anomalies in system behavior, and triggering alarms alert you to notable issues or changes that should be investigated. For instance, a spike in a graph can represent a surge in server error rates or increased latency.

Additionally, this high-level overview facilitates efficient communication regarding the application's state and performance to teammates and upper management since graphical representations of log data are more accessible to a wider audience, including those without a technical background.

Since trends and patterns that are not obvious in raw log data become clearer through visualization, your problem-spotting and decision-making process will be greatly accelerated enabling timely responses to changes in application behavior.

How to visualize your log data in four steps

So, how do you get started with log visualization?

First, you need to ensure that your systems are generating useful and well-formatted logs, and that they're being aggregated and centralized in a log management system equipped for log data visualization.

For example, the Elastic Stack provides Kibana for visualization, while many cloud-based systems have such functionality built-in.

Once your data is being stored in your chosen system, you can begin to visualize it immediately. But you need to follow some guidelines to do it effectively. In simple terms, you need to:

1. Identify which data you want to visualize.
2. Choose the chart type that best fits your data.
3. Design and set up your dashboard.
4. Set thresholds and add alerting.

Now, let's dive into each step above so you can learn how to effectively visualize your production log data.

1. Choose the data to visualize

The initial step in the log visualization process is to define your objectives and determine the insights you aim to derive from the data. This step involves exploring and querying the data to pinpoint key metrics that align with your business goals. Examples of frequently visualized data include:

• Error rates and their types.
• Failed login attempts and security incidents.
• Response times and performance metrics for API calls.
• Traffic patterns and user engagement statistics.
• System resource utilization, like CPU and memory usage.
• Frequency and types of user actions within an application.

2. Select the most appropriate chart

After identifying the essential metrics to visualize, the next step involves selecting the most suitable graphing technique.

This choice should complement the nature of your data and the story you wish to tell. For instance, line graphs are ideal for showing trends over time, bar charts for comparing quantities across different categories, and pie charts for illustrating proportional distributions.

The goal is to use a visualization method that not only represents your data accurately but also communicates the information effectively to your audience.

Some guidelines to help you choose include the following:

1. Displaying a prominent key metric

In many cases, the goal is to highlight a key metric prominently, like the total log count, the volume of requests, or the tally of errors.

When presenting such figures, it's necessary to provide context by comparing the figure with a previous period's data or setting it against another relevant metric.

Such contextualization helps viewers quickly grasp the significance of the number, enabling them to respond accordingly with either satisfaction or concern.

Various types of log data can be effectively communicated this way, including but not limited to:

• The number of logs being ingested every day. It's highly likely that this number directly affects your logging spend.
• Number of API requests made to an endpoint or the entire service.
• Number of security incidents detected.
• Percentage uptime over a given period.

2. Illustrating the proportional makeup of data

Pie and doughnut charts are useful for comparing the relationship between different key metrics within your log data. But take care not to visualize too many categories to avoid making the chart hard to read and interpret.

Some examples of log data that could be effectively visualized with a pie chart or similar include the following:

• The distribution of HTTP status codes returned by your service.
• Traffic source by operating system.
• Number of logs produced by each service in your environment.
• The percentage of your logs that are structured vs unstructured to monitor your progress when transitioning to a structured approach.

3. Displaying changes over time

Showing how various metrics change over time is a frequent and fundamental aspect of dashboard design. For example, graphing your web service response codes lets you easily see when there's a spike in 5xx errors and correlate them to other happenings in your system.

Line, area, and column charts are popular for illustrating changes over time in log data. Line and area charts excel in depicting data that fluctuates frequently, like daily error rates in a system or hourly network traffic. Column charts, in contrast, are ideal for showcasing data that resets each period, such as daily API request counts or weekly new user registrations.

4. Plotting geographical data

When your log data includes geographical information, visualizing it on a map can be beneficial for understanding aspects like the distribution of user access locations, regional variations in server load, or the geographical spread of cybersecurity threats.

For instance, mapping the source of web traffic can offer insights into market reach and user behavior across different regions. Similarly, visualizing the locations of server outages or security incidents can help pinpoint areas that may require infrastructure improvements or heightened security measures.

5. Correlating metrics

Metric correlation helps you understand how different log data metrics influence and relate to each other. For example, you can analyze the correlation between server load and response times to understand the median latency.

If you ever need to answer questions like the following examples, a scatter plot or bubble chart could be ideal:

• How does the number of API requests correlate with server response times across different times of the day?
• What is the relationship between the amount of traffic an endpoint receives and the number of errors logged?
• How does the users' geographic location affect the latency of service calls?
• What's the relationship between database query execution times and the volume of data being queried?
• What's the relationship between the number of concurrent users and the system's memory usage?

6. The good ol' table

Tables, while not always visually striking, can be quite useful in logging dashboards. They excel in presenting detailed, itemized information and are invaluable when interactive features like sorting and drilling down are needed.

After summary charts and overviews, a table can list more detailed log entries, such as error details or user-session information.

3. Design and set up your dashboard

Once you've decided the metrics to plot and what representations to use, the next step is to create the dashboard and start adding graphs to it. The exact approach to achieve this depends on your tool of choice.

With Better Stack, you can select your log source and filter on the properties or use an SQL query to fetch and display data precisely how you want it.

You can then choose from the available chart types and preview the results before saving it to the dashboard. You'll then repeat this step for each metric that needs to be visualized.

It's best to keep each dashboard focused on a single overall theme to make interpreting the data more straightforward.

4. Set up thresholds and alerting

Log visualization goes hand in hand with alerting. After charting the key metrics you're tracking, it's crucial to integrate real-time alerting mechanisms.

This setup ensures that you're promptly notified when specific thresholds are surpassed, allowing for immediate and appropriate responses to potential issues in your system.

Final thoughts

In most cases, logs are treated like an abandoned warehouse or dusty archive: store now and sort later (if ever). However, they can reveal their true potential when viewed as valuable data and visualized effectively.

This post isn't an exhaustive guide to every log visualization use case but I hope it's helped you understand how you can use it to enhance your log management process and make better, faster decisions about your software.

Thanks for reading, and happy logging!

Article by

Ayooluwa Isaiah

Ayo is the Head of Content at Better Stack. His passion is simplifying and communicating complex technical ideas effectively. His work was featured on several esteemed publications including LWN.net, Digital Ocean, and CSS-Tricks. When he’s not writing or coding, he loves to travel, bike, and play tennis.

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