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set_hpa.py
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set_hpa.py
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import csv
import os
import yaml
import pandas as pd
from yaml.loader import SafeLoader
template_file_name = "manifests/hpa-template.yaml"
df_hpa = pd.read_csv("hpa.csv")
for _, row in df_hpa.iterrows():
service_name = row["Service"]
cpu_value = int(row["CPU Threshold (%)"])
memory_value = row["Memory Threshold (Mb)"]
pods_min = int(row["PodsMin"])
pods_max = int(row["PodsMax"])
with open(template_file_name) as file_template:
data = yaml.load(file_template, Loader=SafeLoader)
data["metadata"]["name"] = service_name
data["spec"]["scaleTargetRef"]["name"] = service_name
data["spec"]["minReplicas"] = pods_min
data["spec"]["maxReplicas"] = pods_max
for jj in range(len(data["spec"]["metrics"])):
metric_resource_name = data["spec"]["metrics"][jj]["resource"]["name"]
if metric_resource_name == "cpu":
data["spec"]["metrics"][jj]["resource"]["target"][
"averageUtilization"
] = cpu_value
if metric_resource_name == "memory":
data["spec"]["metrics"][jj]["resource"]["target"][
"averageValue"
] = memory_value
target_file_name = "manifests/hpa-{service_name}.yaml".format(
service_name=service_name
)
with open(target_file_name, "w") as file_target:
yaml.dump(data, file_target, sort_keys=False, default_flow_style=False)
os.system("kubectl apply -f {file_name}".format(file_name=target_file_name))