
Security News
rv Is a New Rust-Powered Ruby Version Manager Inspired by Python's uv
Ruby maintainers from Bundler and rbenv teams are building rv to bring Python uv's speed and unified tooling approach to Ruby development.
performance-decorators
Advanced tools
Elevate your application's performance with Performance Decorators! This TypeScript library provides powerful tools for seamless performance monitoring and optimization in both Node.js and browser environments. Simplify the task of identifying performance bottlenecks and ensure your applications run efficiently with our easy-to-use decorators.
Easily integrate into your project:
npm install performance-decorators
import { LogExecutionTime } from "performance-decorators/debugging";
class PerformanceExample {
@LogExecutionTime()
quickMethod() {
// Simulated task
}
@LogExecutionTime((time, method) => console.log(`${method} took ${time} ms`))
detailedMethod() {
// More complex task
}
}
import { LogMemoryUsage } from "performance-decorators/debugging";
class PerformanceExample {
@LogMemoryUsage()
standardMemoryMethod() {
// Memory consuming task
}
@LogMemoryUsage((usedMemory, method) =>
console.log(`${method} used ${usedMemory} bytes`),
)
detailedMemoryMethod() {
// Task with detailed memory monitoring
}
}
import { LogMethodError } from "performance-decorators/debugging";
class PerformanceExample {
@LogMethodError()
methodWithError() {
throw new Error("Example error");
}
@LogMethodError(true, (error, method) =>
console.error(`${method} error: ${error.message}`),
)
methodWithCustomErrorHandling() {
throw new Error("Custom error");
}
}
import WarnMemoryLeak from "performance-decorators/debugging";
/**
* Class decorator to monitor and warn about potential memory leaks.
* Works in both Node.js and browser environments.
*
* @param checkIntervalMs - Interval in milliseconds to check memory usage.
* @param thresholdPercent - Percentage increase in memory usage to trigger warning.
* @param logger - Logging function to use for warnings.
* @param enableManualGC - Enables manual garbage collection in Node.js (requires --expose-gc flag).
*/
function MemoryLeakWarning(
checkIntervalMs: number = 30000,
thresholdPercent: number = 20,
logger: (msg: string) => void = console.warn,
enableManualGC: boolean = false,
) {
return WarnMemoryLeak(
checkIntervalMs,
thresholdPercent,
logger,
enableManualGC,
);
}
@MemoryLeakWarning(30000, 20, console.warn, false)
class MyMonitoredClass {
// Your class implementation
}
// Create an instance
const instance = new MyMonitoredClass();
import { WarnPerformanceThreshold } from "performance-decorators/debugging";
class PerformanceExample {
@WarnPerformanceThreshold()
methodWithDefaultThreshold() {
// Task to be monitored
}
@WarnPerformanceThreshold(200, (time, method) =>
console.warn(`${method} exceeded ${time} ms`),
)
methodWithCustomThreshold() {
// Another monitored task
}
}
import LogNetworkRequests from "performance-decorators/debugging";
class PerformanceExample {
@LogNetworkRequests()
async fetchData(url: string): Promise<void> {
const response = await fetch(url);
return response.json();
}
@LogNetworkRequests((log) => {
console.log(
`Custom Logger - ${log.method} request to ${log.url} took ${log.duration.toFixed(2)}ms`,
);
})
async fetchDataWithCustomLogger(url: string): Promise<void> {
const response = await fetch(url);
return response.json();
}
}
import LogReturnValue from "performance-decorators/debugging";
class ExampleService {
@LogReturnValue()
calculateSum(a: number, b: number): number {
return a + b;
}
@LogReturnValue((value, methodName) =>
console.log(`[${methodName}] returned:`, value),
)
async fetchData(url: string): Promise<any> {
const response = await fetch(url);
return response.json();
}
}
const service = new ExampleService();
console.log(service.calculateSum(3, 4)); // Logs and returns 7
service.fetchData("https://api.example.com/data"); // Logs the returned JSON data
import { AutoRetry } from "performance-decorators/optimization";
class DataService {
@AutoRetry(3, 1000) // Retry up to 3 times with a 1-second delay
async fetchData(url: string) {
console.log(`Fetching data from ${url}`);
const response = await fetch(url);
if (!response.ok) {
throw new Error("Network response was not ok.");
}
return response.json();
}
}
const service = new DataService();
service
.fetchData("https://api.example.com/data")
.then((data) => console.log("Data fetched successfully:", data))
.catch((error) => console.error("Failed to fetch data:", error));
import { Debounce } from "performance-decorators/optimization";
class SearchComponent {
@Debounce(300)
async onSearch(term: string) {
console.log(`Searching for: ${term}`);
// Simulate an API call
return fetch(`/api/search?q=${encodeURIComponent(term)}`).then((res) =>
res.json(),
);
}
}
const searchComponent = new SearchComponent();
searchComponent.onSearch("hello");
import { LazyLoad } from "performance-decorators/optimization";
class ExpensiveComputation {
@LazyLoad()
get expensiveData() {
console.log("Computing expensive data");
return Array.from({ length: 1000000 }, (_, i) => Math.sqrt(i));
}
}
const computation = new ExpensiveComputation();
console.log("ExpensiveComputation instance created");
// The first access triggers the computation
console.log(computation.expensiveData[1000]); // Initializes and accesses the data
console.log(computation.expensiveData[2000]); // Accesses cached data
import { Memoize } from "performance-decorators/optimization";
class Calculator {
@Memoize()
fibonacci(n: number): number {
if (n <= 1) return n;
return this.fibonacci(n - 1) + this.fibonacci(n - 2);
}
}
const calculator = new Calculator();
console.log(calculator.fibonacci(10)); // Computed
console.log(calculator.fibonacci(10)); // Cached result
import { Throttle } from "performance-decorators/optimization";
class ScrollHandler {
@Throttle(100)
onScroll(event: Event) {
console.log("Scrolling", event);
}
}
const handler = new ScrollHandler();
window.addEventListener("scroll", handler.onScroll);
Refer to the TypeScript JSDoc comments in the source code for detailed API information. Each decorator is well-documented, providing insights into its usage and configuration.
Contributions are welcome! Please refer to the project's style and contribution guidelines for submitting patches and additions. Ensure to follow best practices and add tests for new features.
This project is licensed under the MIT License - see the LICENSE file for details.
FAQs
A set of decorators to help with performance testing
The npm package performance-decorators receives a total of 4 weekly downloads. As such, performance-decorators popularity was classified as not popular.
We found that performance-decorators demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 0 open source maintainers collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Security News
Ruby maintainers from Bundler and rbenv teams are building rv to bring Python uv's speed and unified tooling approach to Ruby development.
Security News
Following last week’s supply chain attack, Nx published findings on the GitHub Actions exploit and moved npm publishing to Trusted Publishers.
Security News
AGENTS.md is a fast-growing open format giving AI coding agents a shared, predictable way to understand project setup, style, and workflows.