Beyond Line Charts: Why Some Diversity in Telemetry Visualization Is Long Overdue

Beyond Line Charts: Why Some Diversity in Telemetry Visualization Is Long Overdue

The presentation by Yao Yue advocates for diversifying telemetry visualization techniques beyond the traditional line charts, which have dominated the industry due to their simplicity in visualizing service metrics. Yue, a seasoned platform engineer, argues that while line charts are effective for certain data types, they fall short when dealing with complex or noisy datasets, which are crucial for decision-making in real-time telemetry systems.

Key Points Discussed:

• Limitations of Line Charts: Line charts are criticized for their inadequacy in handling busy or noisy data. They often rely on extrapolation, which can obscure the true variability of the data, creating misleading conclusions.
• Importance of Data Shape: Understanding the data shape is crucial for choosing the right visualization. Busy datasets necessitate techniques to reduce noise, such as window operations, which can transform data into more comprehensible formats.
• Telemetry Visualization Diversity: Yue emphasizes the need for trying different visualization styles tailored to the data's properties rather than defaulting to line charts, which often do not provide clear insights for operational questions.
• Telemetry Data Typing: The presentation outlines three main types of telemetry data—counters, gauges, and histograms—and their respective characteristics that influence visualization choices.

Solution Approach:

• Visualization Flexibility: Encourages experimenting with multiple visualization forms to find ones that best highlight the underlying data structure and trends.
• Spotlight on Data Rawness: Suggests using plots like dot plots that emphasize raw data points over extrapolated data to provide a more honest representation of datasets.

The session highlights a broad systemic issue in telemetry visualization where reliance on line charts can lead to critical misinterpretations, proposing a shift towards a more varied and data-sensitive approach.

This is the end of the AI-generated content.

For decades, visualization of service metrics overwhelmingly converges to line charts. The time-centric nature of real-time telemetry further cemented this phenomenon via storage layouts and domain-specific query languages.

But what questions do we, as service operators, ask or should ask? Can we find all the answers in line charts? Obviously my answer to that last question is no. I want to show you how some of the most valuable operational questions are much better served by presenting, and perhaps processing, telemetry data and metadata in alternative formats. 

It is easy for software developers to treat operations as an afterthought. But running software well is a serious engineering challenge in its own right. By taking a fresh and principled look at service telemetry, we can better appreciate both the breadth and depth of this fascinating problem that transcends programming languages, software/hardware abstractions, and application domains.