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1 year ago - resolve symbols before sending stack
- display stack type and color accordingly
- python native stack winding
Open 0b01 opened 1 year ago
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1 year ago 0b01
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1 year ago A grid of boxes with different time frames. seconds in an hour, minutes
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1 year ago One thing to consider is the layout of the interface. For a tool that displays heatmaps and flamegraphs, a logical layout might be to have a menu on the left-hand side of the screen that allows users to select the specific application or time period they want to view. The main part of the screen could then display the heatmap or flamegraph for the selected application or time period.
Another important aspect of the design will be the visual presentation of the heatmaps and flamegraphs themselves. The key here is to make the information easy to understand at a glance, while also providing enough detail and context for users to delve deeper if they need to. For example, you could use colors to indicate different levels of activity or performance, with warmer colors (e.g. red, orange) indicating high activity and cooler colors (e.g. blue, green) indicating low activity. You could also use labels and annotations to provide more information about specific parts of the heatmap or flamegraph.
Overall, the key to designing a professional and logical UI for your startup will be to focus on the needs and goals of your users, and to create a design that clearly and effectively presents the information they need to understand and manage their applications. I hope these suggestions are helpful!
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1 year ago A time selector that allows users to specify the time period for which they want to view stack trace data. This could be implemented as a date range picker, or as a set of controls that allow users to navigate forward and backward in time. A heatmap view that displays the number of stacks collected during a given time period. This could be implemented using a graphical library, such as D3.js, to generate a heatmap that shows the density of stack trace data over time. A flamegraph view that displays the stack trace data as a tree, with the root node at the top and the leaves at the bottom. This could be implemented using a library, such as Flamegraph.pl, to generate a hierarchical representation of the stack trace data. A call tree view that allows users to drill down into the stack trace data and view the individual calls that make up a stack. This could be implemented using a tree visualization library, such as d3-hierarchy, to display the call tree in an interactive and intuitive way. Filtering controls that allow users to filter the stack trace data by process and stack type. This could be implemented using a combination of front-end and back-end technologies, such as React for the user interface and MongoDB for the data storage and querying. These are just some examples of potential features you could build for your dashboard for continuous profiling. Ultimately, the specific features you choose to implement will depend on your specific requirements and the needs of your users. By carefully considering the features and functionality that will be most useful for your users, you can create a dashboard that is both effective and user-friendly.
Ricky Han Thanks. What are other features you think would be useful?
In addition to the features I mentioned earlier, there are many other potential features you could consider implementing for your dashboard for continuous profiling. Some other ideas you might want to consider include:
Customizable display options, such as the ability to adjust the color scheme, font size, and other visual elements of the dashboard. This could be implemented using a configuration interface, where users can adjust the display settings to suit their preferences. Data export capabilities, such as the ability to download the stack trace data as a CSV or other file format. This could be implemented using a server-side library, such as Pandas, to generate the export file and make it available for download. An alert system, where users can specify conditions that trigger notifications when certain thresholds are reached. For example, a user could set an alert to notify them when a particular process generates a large number of stack traces over a given time period. A search interface, where users can search for specific stack traces or processes using keywords or other criteria. This could be implemented using a search engine, such as Elasticsearch, to index the stack trace data and provide fast and accurate search results. These are just a few examples of additional features that you could consider implementing for your dashboard for continuous profiling. By adding these types of features, you can make the dashboard more versatile and user-friendly, and provide users with more powerful tools for analyzing and understanding the stack trace data.
Ricky Han What about differential flamegraphs?
A differential flamegraph is a type of visual representation of stack trace data that shows the differences between two sets of data. This can be useful for comparing the performance of a system over time, or for identifying changes in the behavior of a particular process or stack.
To generate a differential flamegraph, you would first need to collect two sets of stack trace data, such as by running a performance test at different times or under different conditions. You could then use a tool, such as Flamegraph.pl, to generate the flamegraphs for each set of data. Finally, you could use a tool, such as DiffFlame, to compare the two flamegraphs and generate the differential flamegraph, which shows the differences between the two sets of data.
Incorporating differential flamegraphs into your dashboard for continuous profiling could be a useful feature, as it would allow users to quickly and easily compare the performance of a system over time or under different conditions. To implement this feature, you would need to implement the necessary data collection and processing logic, as well as a front-end component for displaying the differential flamegraph.
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1 year ago