FB Flurry: The Ultimate Guide for Beginners

Alright folks, lemme tell you about my little adventure with “f b flurry”. So, I was tasked with, well, basically bombarding a server with requests using Flurry, after seeing some chatter about it online. Seemed simple enough, right?

First things first, I downloaded the Flurry SDK. Dug around for a bit, trying to figure out how to actually send custom events. The documentation wasn’t exactly crystal clear, I’ll be honest. I ended up piecing together snippets from different forum posts. Annoying, but hey, that’s how it goes sometimes.

Next up was the actual coding. I decided to use Python – seemed like the quickest way to whip something up. I used the `requests` library to send POST requests to a dummy endpoint. Just wanted to see if I could actually trigger events and if Flurry was catching them. The initial code looked something like this:

import requests
url = 'your_test_endpoint_here'
data = {'event_name': 'test_event', 'param1': 'value1'}

for i in range(100):
response = *(url, data=data)
print(f"Request {i+1}: {*_code}")

Simple, right? But this was just the beginning. I needed to integrate this with the Flurry SDK to send legit Flurry events.

Here’s where things got a bit hairy. Flurry uses a specific format for their events. I had to craft the request body to match their expected schema. Lots of trial and error here, tweaking the headers and the payload until Flurry finally started registering events in the dashboard.

I ended up creating a function to build the Flurry-compatible payload. Something like this:

import json
import uuid
def create_flurry_event(event_name, params):
event_id = str(*4())

timestamp = int(*()  1000)  # Flurry wants milliseconds
event = {
"apiKey": "YOUR_FLURRY_API_KEY",
"sessionId": str(*4()),
"events": [


"eventName": event_name,
"eventId": event_id,
"eventTime": timestamp,
"parameters": params



return *(event)

Now, for the “flurry” part. I wanted to simulate a high volume of events. So, I wrapped the event-sending code in a multithreaded loop. Figured I could crank up the concurrency and really stress-test things. Used Python’s `threading` module. Looked something like this:

import threading
def send_flurry_event(event_name, params):
payload = create_flurry_event(event_name, params)
headers = {'Content-Type': 'application/json'}

try:
response = *("YOUR_FLURRY_ENDPOINT", data=payload, headers=headers)
print(f"Event sent: {event_name}, Status: {*_code}")
except Exception as e:
print(f"Error sending event: {e}")

def flurry_bombardment(event_name, params, num_threads, events_per_thread):
threads = []
for i in range(num_threads):
thread = *(target=lambda: [send_flurry_event(event_name, params) for _ in range(events_per_thread)])
*(thread)


for thread in threads:

# Example usage:
flurry_bombardment("my_event", {"param1": "value1"}, 10, 100)

The results? Well, the server definitely felt it. Saw a spike in CPU usage. And Flurry did register the events, although there was a noticeable delay. I also noticed some events getting dropped, which meant I needed to implement some kind of retry mechanism. That’s a problem for another day though.

Lessons learned? Flurry’s documentation could be better. Multithreading in Python is tricky. And stress-testing is never as simple as you think it’s going to be. But hey, I got “f b flurry” working, at least in a rudimentary way. On to the next challenge!