From 9ee3dce1bfaaa8ba42788016a8f9f756cf3ae0de Mon Sep 17 00:00:00 2001
From: Simon Ehrenstein <simon.ehrenstein@gmail.com>
Date: Thu, 17 Sep 2020 13:35:59 +0200
Subject: [PATCH] Introduce new version of benchmarking strategies
---
execution/lag_analysis.py | 10 +---------
1 file changed, 1 insertion(+), 9 deletions(-)
diff --git a/execution/lag_analysis.py b/execution/lag_analysis.py
index ce2e0f7f6..66abf05c3 100644
--- a/execution/lag_analysis.py
+++ b/execution/lag_analysis.py
@@ -13,7 +13,7 @@ instances = sys.argv[4]
execution_minutes = int(sys.argv[5])
time_diff_ms = int(os.getenv('CLOCK_DIFF_MS', 0))
-prometheus_query_path = 'http://kube1.internal:32529/api/v1/query_range'
+prometheus_query_path = 'http://localhost:9090/api/v1/query_range'
#http://localhost:9090/api/v1/query_range?query=sum%20by(job,topic)(kafka_consumer_consumer_fetch_manager_metrics_records_lag)&start=2015-07-01T20:10:30.781Z&end=2020-07-01T20:11:00.781Z&step=15s
@@ -51,14 +51,6 @@ for result in results:
df = pd.DataFrame(d)
-# save whether the subexperiment was successful or not, meaning whether the consumer lag was above some threshhold or not
-# Assumption: Due to fluctuations within the record lag measurements, it is sufficient to analyze the second half of measurements.
-second_half = list(map(lambda x: x['value'], filter(lambda x: x['topic'] == 'input', d[len(d)//2:])))
-avg_lag = sum(second_half) / len(second_half)
-with open(r"last_exp_result.txt", "w+") as file:
- success = 0 if avg_lag > 1000 else 1
- file.write(str(success))
-
# Do some analysis
input = df.loc[df['topic'] == "input"]
--
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