集群运行nfsiostat收集信息并分析
工作中偶遇到NFS Server中IO hang，这里提供一个在集群中批量运行nfsiostat命令，并收集信息保存至excel文件，按nfs server分sheet，锁定第一行，并对每一列汇总。
也可以进一步定制，定期将数据推送至ElasticSearch，记录时序数据以做进一步分析。
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# wanlinwang
# 2024-03-17
import re
import paramiko
import pandas as pd
from io import StringIO
from concurrent.futures import ThreadPoolExecutor
# fs to mountpoint
fs2mp = {
"192.168.10.1:/nfs001": "home",
"192.168.10.1:/nfs002": "tools",
"192.168.10.2:/nfs003": "proj",
# Server information list
servers = [
"192.168.10.1",
"192.168.10.2",
]
ops_pattern = r'\b[\d.]+(?: \([\d.]+\))?|\d+ \(.*?\)'
# Execute nfsiostat and get the results
def get_nfsiostat(host, username="root"):
client = paramiko.SSHClient()
client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
client.connect(hostname=host, username=username, port=36000)
stdin, stdout, stderr = client.exec_command('nfsiostat 2 2')
# 等待第一次报告的输出并忽略
while not stdout.channel.recv_ready():
time.sleep(1) # 轮询等待输出
first_output = stdout.channel.recv(1000000) # 读取并丢弃第一次输出
print(f"{host} 接收到第一次数据。")
#print(first_output.decode())
# 等待并读取第二次结果
time.sleep(2)
# 接收第二次
output = b""
while not stdout.channel.eof_received:
try:
chunk = stdout.channel.recv(4096)
if not chunk:
break
output += chunk
except Eception as e:
print(f"接收第二次时发生错误{e}")
break
print(f"{host} 第二次结果")
output = output.decode()
#print(f"output {output}")
client.close()
return output
# Parse nfsiostat output
def parse_nfsiostat_output(server, output):
data = []
output_io = StringIO(output)
lines = output_io.readlines()
lines = [line for line in lines if line.strip() != ""]
for i in range(0, len(lines), 7):
if i + 5 < len(lines):
#mount_point = "/".join(lines[i].strip().split("/")[0:2])
mount_point = "/".join(re.split('/| ', lines[i].strip())[0:2])
ops_rpc = lines[i+2].strip().split()
matches = re.findall(ops_pattern, lines[i+4])
read_ops = [float(num) if '.' in num else int(num) for num in matches]
matches = re.findall(ops_pattern, lines[i+6])
write_ops = [float(num) if '.' in num else int(num) for num in matches]
data.append({
"server": server,
"mount_point": mount_point,
"op/s": ops_rpc[0],
"rpc bklog": ops_rpc[1],
"read_ops/s": read_ops[0], "read_kB/s": read_ops[1], "read_kB/op": read_ops[2],
"read_retrans": read_ops[3], "read_retrans%": str(read_ops[4]) + "%", "read_avg RTT(ms)": read_ops[5], "read_avg exe(ms)": read_ops[6],
"write_ops/s": write_ops[0], "write_kB/s": write_ops[1], "write_kB/op": write_ops[2],
"write_retrans": write_ops[3], "write_retrans%": str(write_ops[4]) + "%", "write_avg RTT(ms)": write_ops[5], "write_avg exe(ms)": write_ops[6]
})
return data
# Function to fetch and parse nfsiostat output for a single server
def fetch_and_parse_nfsiostat(server):
output = get_nfsiostat(server)
return parse_nfsiostat_output(server, output)
# Main program using ThreadPoolExecutor for concurrency
def main():
all_data = []
with ThreadPoolExecutor(max_workers=len(servers)) as executor:
futures = [executor.submit(fetch_and_parse_nfsiostat, server) for server in servers]
for future in futures:
all_data.extend(future.result())
# Convert data to DataFrame for easier processing
df = pd.DataFrame(all_data)
# Removing duplicates, keeping only unique records
df_unique = df.drop_duplicates()
# Output to Excel file with different mount_points on different sheets
# Generating the Excel file name with a timestamp
current_time = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
excel_filename = f"nfsiostat_data_{current_time}.xlsx"
with pd.ExcelWriter(excel_filename, engine='xlsxwriter') as writer:
workbook = writer.book
for mount_point in df_unique['mount_point'].unique():
df_mp = df_unique[df_unique['mount_point'] == mount_point]
# 对每个server保留op/s最高的记录
df_mp = df_mp.sort_values(by='op/s', ascending=False).drop_duplicates(subset=['server'], keep='first')
# 对指定列进行降序排序
if 'write_kB/s' in df_mp.columns and 'read_kB/s' in df_mp.columns:
df_mp = df_mp.sort_values(by=['write_kB/s', 'read_kB/s'], ascending=[False, False])
# 创建工作表并写入数据
if mount_point in fs2mp:
sheet_name = fs2mp[mount_point]
else:
sheet_name = mount_point.replace(":/", "_")[0:31]
df_mp.to_excel(writer, sheet_name=sheet_name, index=False)
# 获取工作表对象
worksheet = writer.sheets[sheet_name]
# 锁定第一行
worksheet.freeze_panes(1, 0)
# 自动调整列宽
for column in df_mp:
column_length = max(df_mp[column].astype(str).map(len).max(), len(column))
col_idx = df_mp.columns.get_loc(column)
worksheet.set_column(col_idx, col_idx, column_length)
# Identify numeric columns and exclude specific ones from sum calculation
numeric_cols = df_mp.select_dtypes(include=['number']).columns
numeric_cols = numeric_cols.drop(['read_avg RTT(ms)', 'read_avg exe(ms)', 'write_avg RTT(ms)', 'write_avg exe(ms)'], errors='ignore') # Exclude specific columns
sum_row = df_mp[numeric_cols].sum().to_dict()
sum_row = {col: sum_row.get(col, '') for col in df_mp.columns} # Include empty values for non-numeric columns
df_sum = pd.DataFrame([sum_row])
startrow = len(df_mp) + 1
df_sum.to_excel(writer, sheet_name=sheet_name, startrow=startrow, index=False, header=False)
# Format the sum row
sum_format = workbook.add_format({'bold': True})
for col_num, value in enumerate(df_sum.columns):
worksheet.write(startrow, col_num, sum_row[value], sum_format)
print(f"Data has been outputted to Excel file at {excel_filename}")
if __name__ == "__main__":
main()
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