High Response Time: Heroku/spatialmmchat

100%

High Response Time: Heroku/spatialmmchat

✓ Published0🌍 Public

Last edited Oct 21, 2015

Created on Oct 21, 2015

This example visualizes a single Heroku router log entry that triggered a “High Response Time” alarm, showing a WebSocket request with a 306-second service time alongside the surrounding log context. Built with D3.js, it uses `d3.forceSimulation` to position log tokens and `d3.select` with `transition.attrTween` to animate the entry’s timeline. The data comes from a raw gist of Logentries output, with the visualization highlighting the unusually slow `/sockjs/419/fhpzwv6e/websocket` request against faster preceding events.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist 180756

This visualization demonstrates a web application’s backend activity by rendering a chronological log of server requests and database queries as an interactive streamgraph. The example uses Ruby on Rails middleware to capture and format the log data, then relies on the `delayed_job` plugin to manage asynchronous background processing. The graph visualizes the sequence of SQL operations, controller actions, and job executions, with each event represented as a colored band along a timeline. It reveals the progression from user-facing HTTP requests to background file-processing jobs, including the point where a `Delayed::Job` fails due to a missing directory. The visualization highlights how Rails requests and background workers interleave.

DDylanFM

65% match

Loading thumbnail…

Hotel messenger traffic

This visualization shows hotel messenger traffic across five service categories (Superhost, Restaurant, Bar, Beach, Room Service) over one week, with lines and circles representing daily message volumes. The chart uses D3.js v5 classes including `d3.scaleTime`, `d3.scaleLinear`, `d3.axisBottom`, and `d3.axisLeft` to build axes, while `d3.line` with `d3.curveMonotoneX` renders the paths. Interactive tooltips appear on mouseover, and circles are colored red or green based on incident versus resolve counts. The legend uses `d3.nest` to group categories, and the data is procedurally generated in `useData.js` for this React-based example.

MMarcelo

63% match

Loading thumbnail…

buoys scheduler error

This example visualizes the timing and error events from a Heroku scheduler run for the “buoys” app, which fetches ocean wave data from New South Wales government GIF graphs. A timeline chart shows the sequence of log lines, highlighting when specific location fetches (Byron Bay, Sydney, Eden) succeed or fail. The code uses D3’s `d3.forceSimulation` to position nodes representing each log entry, with colors distinguishing app messages, scheduler state changes, and the socket hang-up error. Data comes from the gist’s plain-text log file, parsed and rendered with SVG paths and D3 scales.

DDylanFM

63% match

Loading thumbnail…

2018: Reactietijden horizontal bars

This visualization shows the 2018 response times for different priority categories using animated horizontal bars. The data is loaded from a local JSON file and rendered using D3.js v3 with SVG. Each bar is a bullet chart representing response times for critical, high, medium, low, and total categories. The code uses D3’s bullet chart plugin, which includes ranges, markers, and measures, and features a transition effect when the "Update" button is clicked, randomizing the displayed values with `d3.transition` and a linear scale.

MMisunoKitara

62% match

Loading thumbnail…

Gist 182075

This visualization uses a network graph to show a chronological sequence of error messages from a Phusion Passenger application server log. It demonstrates how a single failing dependency—a missing Ruby interpreter path—triggers cascading system events. The rendering relies on a custom force-directed layout, likely built with D3.js’s `d3.forceSimulation`, where each node represents a distinct log entry and edges encode the propagation of errors across time and process IDs. The color scale maps error severity, while the spatial arrangement clusters related failures, making the cascade pattern visually explicit. Data comes directly from the pasted Gist text file, parsed for timestamps, PIDs, and stack-trace lines to drive the layout.

DDylanFM

62% match

Loading thumbnail…

Gist 8a97689739c217f28474d80c89013628

This visualization uses a force-directed graph to show relationships between entities, with nodes positioned by `d3.forceSimulation` and links drawn as curved paths. The code computes node sizes and colors from quantitative attributes, while a canvas overlay via `d3.geoPath` and `getImageData` samples pixel density to modulate link opacity based on local clustering. It employs a custom `transition.attrTween` to animate node drift when the simulation restarts, and uses the `d3.hierarchy` API to flatten nested data from a JSON file. The visualization reads credentials from an `env.list` file for a private data source, but the rendering logic is independent of that authentication step.

CConorAspell

61% match

Loading thumbnail…

Gist 660bf021ec250dfb10016d384d524bd7

This example shows a real-time chat application where users exchange messages through a Node.js server. The visualization tracks message flow and user activity using a force-directed graph rendered with D3.js’s `d3.forceSimulation` for node positioning and link physics. Each user appears as a node, with edges connecting them to messages they send, while color and size encode online status and message frequency. The code uses Socket.IO for live data updates and D3’s `transition.attrTween` to animate new nodes and links smoothly as events arrive, making the conversation pattern immediately visible.

FFrankieFabuloso

60% match

Loading thumbnail…

Seismograph - almost

Four high-speed network lines—Partner, PCL, GOLAN, and DriveU—animate as a scrolling seismograph of throughput values in Mbps, moving leftward over a two-minute window. Built with d3.v6, the SVG paths use `line()` generators, a time-scaled x-axis, and a linear y-axis capped at 16, while a `transition` with `easeLinear` translates the clipped chart continuously. Data are parsed from the CSV and colored via an ordinal scale with `schemeCategory10`.

PPiotr Paszek

60% match