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mirror of https://github.com/ltcptgeneral/cs239-caching.git synced 2025-04-01 12:33:25 +00:00

Revert "fix tiered results"

This reverts commit dbaf99af9e22b25fb0ab61552e696e5f9c8cb25a.
This commit is contained in:
Xuanzhe Han 2025-03-10 23:24:00 +00:00
parent dbaf99af9e
commit 44dd3592b0
2 changed files with 44 additions and 44 deletions

@ -40,33 +40,33 @@ cache throughput (requests / s) : 107.27478709180934
real throughput (requests / s) : 49.79781897651969
--- weighted_friend_readonly 0.25 Results ---
hits: 2163 misses: 7837 ratio: 0.2163
average response time (ms) : 10.19118001461029
average cache hit response time (ms) : 0.08772910881307022
average cache miss response time (ms): 12.97971699422486
cache throughput (requests / s) : 98.12406400106552
real throughput (requests / s) : 88.291946486926
hits: 635 misses: 9365 ratio: 0.0635
average response time (ms) : 12.026190519332886
average cache hit response time (ms) : 0.0014263813889871433
average cache miss response time (ms): 12.841537580474837
cache throughput (requests / s) : 83.15185082029382
real throughput (requests / s) : 75.84771942953694
--- weighted_friend_readonly 0.50 Results ---
hits: 2796 misses: 7204 ratio: 0.2796
average response time (ms) : 9.38545286655426
average cache hit response time (ms) : 0.07548079811281742
average cache miss response time (ms): 12.998817928098163
cache throughput (requests / s) : 106.54786873029562
real throughput (requests / s) : 95.1474855850778
hits: 1072 misses: 8928 ratio: 0.1072
average response time (ms) : 11.465663266181945
average cache hit response time (ms) : 0.001328649805552924
average cache miss response time (ms): 12.842205236248645
cache throughput (requests / s) : 87.21693431809628
real throughput (requests / s) : 79.27228928100207
--- weighted_friend_readonly 0.75 Results ---
hits: 4106 misses: 5894 ratio: 0.4106
average response time (ms) : 7.740231680870056
average cache hit response time (ms) : 0.05565987431944298
average cache miss response time (ms): 12.992649447636973
cache throughput (requests / s) : 129.19509922054337
real throughput (requests / s) : 113.09939433219387
hits: 2253 misses: 7747 ratio: 0.2253
average response time (ms) : 9.959305834770202
average cache hit response time (ms) : 0.0012218290786980208
average cache miss response time (ms): 12.855338268592709
cache throughput (requests / s) : 100.40860443393278
real throughput (requests / s) : 90.2328882960655
--- weighted_friend_readonly 1 Results ---
hits: 9998 misses: 2 ratio: 0.9998
average response time (ms) : 0.003992676734924316
average cache hit response time (ms) : 0.0014036124790876145
average cache miss response time (ms): 12.946724891662598
cache throughput (requests / s) : 250458.54357626967
real throughput (requests / s) : 1042.746318159068
hits: 9992 misses: 8 ratio: 0.9992
average response time (ms) : 0.01112067699432373
average cache hit response time (ms) : 0.0008874601894039646
average cache miss response time (ms): 12.792408466339111
cache throughput (requests / s) : 89922.582996559
real throughput (requests / s) : 1052.9166753169109

@ -9,6 +9,8 @@ from utils import print_report
baseurl = "http://localhost:8000"
chance_of_selecting_friend = 1
user_friends = json.loads(requests.get(baseurl + "/users_and_friends").content)
user_ids = json.loads(requests.get(baseurl + "/users").content)["ids"]
@ -25,25 +27,23 @@ def generate_random_friend(user):
next_user = str(random.choice(user_friends[user]))
return next_user
for chance_of_selecting_friend in [0.25, 0.5, 0.75, 1.0]:
times = []
hits = []
times = []
hits = []
start = time.time()
curr_user = generate_random()
last_user = curr_user
for i in tqdm(range(10000)):
url = baseurl + "/user/" + curr_user
response = requests.get(url)
content = json.loads(response.content)
times.append(content["time_ms"])
hits.append(content["source"] == "cache")
if fetch_friend(chance_of_selecting_friend):
curr_user = generate_random_friend(last_user)
else:
curr_user = generate_random()
last_user = curr_user
end = time.time()
start = time.time()
curr_user = generate_random()
last_user = curr_user
for i in tqdm(range(10000)):
url = baseurl + "/user/" + curr_user
response = requests.get(url)
content = json.loads(response.content)
times.append(content["time_ms"])
hits.append(content["source"] == "cache")
if fetch_friend(chance_of_selecting_friend):
curr_user = generate_random_friend(last_user)
else:
curr_user = generate_random()
last_user = curr_user
end = time.time()
print_report(hits, times, end - start)
print_report(hits, times, end - start)