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Python Code to get EBS stats for SAP Systems
In this article, we have developed a Python script to simplify the process of retrieving AWS CloudWatch metrics for Elastic Block Store (EBS) volumes. The script takes input from a CSV file, containing metrics such as metric-name, VolumeId, START_TIME, and END_TIME, and uses the boto3 library to interact with AWS services.
By using this script, users can avoid manually executing individual AWS CLI commands for each metric and volume, making the process more efficient and less error-prone. The script iterates through the CSV file, calls AWS CloudWatch using boto3, and collects the required metric statistics, such as the Average value, for each metric and volume within the specified time range.
The output is then written back to a CSV file with the columns metric-name, VolumeId, Timestamp, and Average. This organized output allows users to easily analyze and further process the data for their specific use cases.
Users can customize the input CSV file with desired metrics and volumes, making it adaptable to various AWS environments and monitoring requirements.
SAMPLE - input.csv
metric-name,VolumeId,START_TIME,END_TIME
VolumeReadOps,vol-12345,2023-07-01T00:00:00,2023-07-02T00:00:00
VolumeWriteOps,vol-67890,2023-07-01T00:00:00,2023-07-02T00:00:00
BurstBalance,vol-54321,2023-07-01T00:00:00,2023-07-02T00:00:00
VolumeBytesRead,vol-98765,2023-07-01T00:00:00,2023-07-02T00:00:00
VolumeBytesWrite,vol-24680,2023-07-01T00:00:00,2023-07-02T00:00:00
CODE - sap_get_metric_statistics.py
import csv
import boto3
# Function to get CloudWatch metric statistics
def get_metric_statistics(metric_name, volume_id, start_time, end_time):
cloudwatch = boto3.client('cloudwatch')
response = cloudwatch.get_metric_statistics(
Namespace='AWS/EBS',
MetricName=metric_name,
Dimensions=[
{
'Name': 'VolumeId',
'Value': volume_id
},
],
StartTime=start_time,
EndTime=end_time,
Period=300,
Statistics=['Average']
)
return response['Datapoints']
# Main function
def main():
input_file = 'input.csv'
output_file = 'output.csv'
with open(input_file, 'r') as csvfile:
csvreader = csv.DictReader(csvfile)
next(csvreader) # Skip the header row
data = list(csvreader)
with open(output_file, 'w', newline='') as file:
csvwriter = csv.writer(file)
csvwriter.writerow(['metric-name', 'VolumeId', 'Timestamp', 'Average'])
for entry in data:
metric_name = entry['metric-name']
volume_id = entry['VolumeId']
start_time = entry['START_TIME']
end_time = entry['END_TIME']
datapoints = get_metric_statistics(metric_name, volume_id, start_time, end_time)
for datapoint in datapoints:
csvwriter.writerow([metric_name, volume_id, datapoint['Timestamp'], datapoint['Average']])
if __name__ == "__main__":
main()
SAMPLE - output.csv
metric-name,VolumeId,Timestamp,Average
VolumeReadOps,volume-1,2023-07-20 10:00:00,120.0
VolumeReadOps,volume-1,2023-07-20 10:05:00,130.0
VolumeReadOps,volume-1,2023-07-20 10:10:00,115.0
VolumeWriteOps,volume-1,2023-07-20 10:00:00,50.0
VolumeWriteOps,volume-1,2023-07-20 10:05:00,60.0
VolumeWriteOps,volume-1,2023-07-20 10:10:00,55.0
BurstBalance,volume-1,2023-07-20 10:00:00,75.0
BurstBalance,volume-1,2023-07-20 10:05:00,80.0
BurstBalance,volume-1,2023-07-20 10:10:00,70.0
VolumeBytesRead,volume-1,2023-07-20 10:00:00,2000.0
VolumeBytesRead,volume-1,2023-07-20 10:05:00,2200.0
VolumeBytesRead,volume-1,2023-07-20 10:10:00,1900.0
VolumeBytesWrite,volume-1,2023-07-20 10:00:00,1500.0
VolumeBytesWrite,volume-1,2023-07-20 10:05:00,1700.0
VolumeBytesWrite,volume-1,2023-07-20 10:10:00,1400.0
Python3 AWS CLI script to create machines
Python3 script to create AWS VM using cli, the code reads the input from a file called “input.txt” with a different format, where each line contains the name of the value and the actual value separated by a tab:
import boto3
# Set your AWS profile and region
AWS_PROFILE = "your-profile"
AWS_REGION = "your-region"
# Read input from file
with open("input.txt", "r") as file:
lines = file.readlines()
# Extract instance details from input
INSTANCE_TYPE = ""
IMAGE_ID = ""
SUBNET_ID = ""
SECURITY_GROUP_ID = ""
KEY_NAME = ""
TAG_NAME = ""
TAG_ENVIRONMENT = ""
TAG_OWNER = ""
# Parse input lines
for line in lines:
name, value = line.strip().split("\t")
if name == "INSTANCE_TYPE":
INSTANCE_TYPE = value
elif name == "IMAGE_ID":
IMAGE_ID = value
elif name == "SUBNET_ID":
SUBNET_ID = value
elif name == "SECURITY_GROUP_ID":
SECURITY_GROUP_ID = value
elif name == "KEY_NAME":
KEY_NAME = value
elif name == "TAG_NAME":
TAG_NAME = value
elif name == "TAG_ENVIRONMENT":
TAG_ENVIRONMENT = value
elif name == "TAG_OWNER":
TAG_OWNER = value
# Create an EC2 resource using the specified profile and region
session = boto3.Session(profile_name=AWS_PROFILE, region_name=AWS_REGION)
ec2_resource = session.resource('ec2')
# Launch the EC2 instance
instance = ec2_resource.create_instances(
ImageId=IMAGE_ID,
InstanceType=INSTANCE_TYPE,
SubnetId=SUBNET_ID,
SecurityGroupIds=[SECURITY_GROUP_ID],
KeyName=KEY_NAME,
TagSpecifications=[
{
'ResourceType': 'instance',
'Tags': [
{'Key': 'Name', 'Value': TAG_NAME},
{'Key': 'Environment', 'Value': TAG_ENVIRONMENT},
{'Key': 'Owner', 'Value': TAG_OWNER}
]
}
]
)[0]
# Wait for the instance to be in the "running" state
instance.wait_until_running()
print(f"Instance {instance.id} launched successfully!")
the “input.txt” file, contains the name of the value and the actual value separated by a tab (\t), each line is parsed and the actual value to the corresponding variable based on the name is assigned.
an example of “input.txt” file structure is as below:
INSTANCE_TYPE t2.micro
IMAGE_ID ami-12345678
SUBNET_ID subnet-12345678
SECURITY_GROUP_ID sg-12345678
KEY_NAME your-key-pair-name
TAG_NAME MyInstance
TAG_ENVIRONMENT Production
TAG_OWNER John Doe