EventsSpeedTests emit objects

100%

EventsSpeedTests emit objects

✓ Published0🌍 Public

Last edited May 17, 2016

Created on May 17, 2016

The visualization compares JavaScript event emitter libraries by plotting operations-per-second benchmarks from Chrome 50 and Node.js 6.1.0, using a horizontal bar chart. Each bar’s length represents throughput, with a dashed line marking the theoretical maximum. The code uses a custom rendering approach with the Canvas API and `requestAnimationFrame` to draw bars, while data is parsed from the gist’s markdown tables. This highlights MiniSignals as the fastest emitter in both environments, outperforming EventEmitter3 and ReactiveProperty.

AI-generated description

Similar vizzes

Loading thumbnail…

Gist d51a566a307b428d98eaf3aa8d2a59a3

This example benchmarks JavaScript event emitter libraries across multiple test scenarios, including initialization, emitting with context, and emitting single or multiple values to one or more listeners. The code runs benchmark suites using `benchmark.js`, comparing libraries such as `EventEmitter3`, `MiniSignals`, `push-stream`, and `signal-lite`, and reports throughput in operations per second. It highlights the fastest performers per scenario, such as `push-stream` leading in the "emit with context" test. The visualization shows raw performance data from Node.js 6.2.1, rendered as plain-text tables with pass/fail assertions and timing summaries.

HHypercubed

79% match

Loading thumbnail…

Gist e92adbf79e3256a0d3e222f140a0433b

This example visualizes the relative performance of 16 JavaScript event-emitter libraries across two benchmark scenarios, “emit objects” and “emit one parameter,” using the raw output of the benchmark runner. The data, drawn from a gist by Hypercubed, is rendered as a grouped horizontal bar chart where each bar’s length corresponds to operations per second (ops/sec), with libraries sorted by speed and the fastest highlighted. The chart uses a simple SVG-based custom layout (likely with d3-scale for axes and d3-array for sorting) rather than a high-level charting library, directly mapping the numeric values to pixel widths. The visualization does not show error margins or statistical significance, only the raw ops/sec values and the benchmark’s “Fastest is” annotation for each test.

HHypercubed

77% match

Loading thumbnail…

D3 Example

A horizontal bar chart compares the operations-per-second benchmark results of 10 JavaScript event-emitting libraries, sorted from fastest to slowest. Rendered as static SVG, the visualization uses D3.js (version 3.5.5) for scales, axes, and data binding, and d3-tip for tooltips that display exact values. The data comes from a CSV file. The x-axis uses SI-prefix formatting (replacing G with B), and the visualization is implemented via `d3.scale.linear`, `d3.scale.ordinal`, and `d3.svg.axis`.

CCurran Kelleher

67% match

Loading thumbnail…

EventsSpeedTests

This example demonstrates how a single property added to one function—but not all functions—can trigger V8’s “failed to optimize dispatch” warning in a hot loop. It shows the impact of monomorphic versus polymorphic call-sites for `Function.prototype.apply` when dispatching to multiple callbacks. The code runs 2 million iterations of a `dispatch` function that invokes two callbacks, comparing an unoptimized case, a bound-function variant, and a fixed version using `.bind(0)` to enforce a monomorphic apply site. The included HTML interface and Node command-line tests let users measure execution time differences across the three approaches.

HHypercubed

62% match

Loading thumbnail…

Gist 518525

This example visualizes the JavaScript events bound to elements on a page, showing their frequency and the element types they target. It uses jQuery’s `.data(‘events’)` to inspect registered handlers, then binds a global listener to all those event types via `.bind()` on the `html` element. As events fire, the code increments per-event and aggregate counts, logging each target’s `nodeName` into arrays for later analysis. The underlying data comes from live user interactions on the rendered DOM, providing a real-time snapshot of event activity without any external dataset.

DDylanFM

60% match

Loading thumbnail…

PowerArray benchmarks

This visualization benchmarks the performance of different `forEach` implementations across arrays and typed arrays, showing that native methods on standard arrays are slower than optimized libraries like lodash, underscore, and fast.js, while PowerArray’s custom method rivals them. The bar chart compares operations per second (ops/sec) for each tested scenario, sourced from a benchmark suite generated by Node.js’s `Benchmark` module. The code uses `Benchmark.Suite` to run trials on plain arrays, `Uint16Array`, and PowerArray instances, with libraries like `fast.js`, `underscore`, `lodash`, and native `Array.prototype.forEach` called directly or via `.call`. Data comes from the gist’s `Results.txt` output, and the visualization likely plots these numeric results to highlight performance gaps.

HHypercubed

59% 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

58% match

Loading thumbnail…

Gist 24579e225fd76065b0ac0e72b36ea31b

This example demonstrates a performance profiling visualization for a force-directed graph simulation. It shows how long the `returnLast` function takes to execute on a dataset of star objects, displaying the elapsed time in milliseconds alongside console output. The visualization uses D3.js’s `d3.forceSimulation` to animate the graph, while custom timing logic captures start and end timestamps via `Date.now()` to measure iteration performance. The code logs the star count and elapsed time to the console, iterating through multiple frames to track timing variations. The rendering approach relies on D3’s simulation engine, with the data source being a static array of star objects defined in the script.

WWill Carter

57% match