How can I simulate 1000 user logins with Puppeteer using minimal resources? Should I launch one headless browser with distinct contexts? Example:
async executeTest(){ const browserInst = await pbStarter.launch(); }
hey, i reccommend one broswer with incognito contexts. though its a bit of fiddling, its lighter than spawning multple browsers. also, check puppeteer-cluser if needed, but monitor mem usage as setups vary
In my experience, using a single headless browser and creating separate incognito contexts for each simulated user yields the best resource utilization. This method avoids the overhead of launching multiple browser instances and keeps the environment isolated per session. It is important, however, to implement robust cleanup routines to free resources between tests, especially during prolonged sessions. Adjusting parameters such as timeouts and concurrent operation limits further refines the process, ensuring load tests are both efficient and reliable under high concurrency.
In my personal testing, I found that while a single headless browser with separate contexts is resource friendly, combining it with careful session management directly impacts performance during large-scale tests. I frequently encountered memory leaks if cleanup wasn’t rigorous. Tracking resource consumption with external monitoring tools allowed me to fine-tune concurrency settings and avoid plateauing issues when simulating more than 500 users simultaneously. Experimentation with context reuse and nuanced delay handling between session initiations often resulted in a more reliable and scalable setup, though it sometimes required balancing resource consumption with test precision.
hey i used a mix - a couple of headless broswers each w incog sessions. it seems to ease memory risks & keep things isolated. test scaling slowly to find the sweet spot for cleanup frequency and delay btw logins.
I have experimented with splitting the workload into groups and reusing browser contexts within each group. In one of my setups, I created a pool of browser instances and used a job scheduler to assign login tasks sequentially. This approach helped manage memory usage while still testing with a high concurrency rate. I took measures to stagger login attempts and implemented thorough cleanup routines after each session. Modifying the scheduler’s throttling parameters also allowed me to reduce resource contention and achieve a more stable load test environment.