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小編給大家分享一下Kubernetes節點之間的ping監控怎么實現，希望大家閱讀完這篇文章之后都有所收獲，下面讓我們一起去探討吧！
腳本和配置
我們解決方案的主要組件是一個腳本，該腳本監視每個節點的.status.addresses值。如果某個節點的該值已更改(例如添加了新節點)，則我們的腳本使用Helm value方式將節點列表以ConfigMap的形式傳遞給Helm圖表：
apiVersion: v1 kind: ConfigMap metadata: name: ping-exporter-config namespace: d8-system data: nodes.json: > {{ .Values.pingExporter.targets | toJson }} .Values.pingExporter.targets類似以下： "cluster_targets":[{"ipAddress":"192.168.191.11","name":"kube-a-3"},{"ipAddress":"192.168.191.12","name":"kube-a-2"},{"ipAddress":"192.168.191.22","name":"kube-a-1"},{"ipAddress":"192.168.191.23","name":"kube-db-1"},{"ipAddress":"192.168.191.9","name":"kube-db-2"},{"ipAddress":"51.75.130.47","name":"kube-a-4"}],"external_targets":[{"host":"8.8.8.8","name":"google-dns"},{"host":"youtube.com"}]}下面是Python腳本：
#!/usr/bin/env python3
import subprocess
import prometheus_client
import re
import statistics
import os
import json
import glob
import better_exchook
import datetime
better_exchook.install()
FPING_CMDLINE = "/usr/sbin/fping -p 1000 -C 30 -B 1 -q -r 1".split(" ")
FPING_REGEX = re.compile(r"^(\S*)\s*: (.*)$", re.MULTILINE)
CONFIG_PATH = "/config/targets.json"
registry = prometheus_client.CollectorRegistry()
prometheus_exceptions_counter = \
prometheus_client.Counter('kube_node_ping_exceptions', 'Total number of exceptions', [], registry=registry)
prom_metrics_cluster = {"sent": prometheus_client.Counter('kube_node_ping_packets_sent_total',
'ICMP packets sent',
['destination_node', 'destination_node_ip_address'],
registry=registry),
"received": prometheus_client.Counter('kube_node_ping_packets_received_total',
'ICMP packets received',
['destination_node', 'destination_node_ip_address'],
registry=registry),
"rtt": prometheus_client.Counter('kube_node_ping_rtt_milliseconds_total',
'round-trip time',
['destination_node', 'destination_node_ip_address'],
registry=registry),
"min": prometheus_client.Gauge('kube_node_ping_rtt_min', 'minimum round-trip time',
['destination_node', 'destination_node_ip_address'],
registry=registry),
"max": prometheus_client.Gauge('kube_node_ping_rtt_max', 'maximum round-trip time',
['destination_node', 'destination_node_ip_address'],
registry=registry),
"mdev": prometheus_client.Gauge('kube_node_ping_rtt_mdev',
'mean deviation of round-trip times',
['destination_node', 'destination_node_ip_address'],
registry=registry)}
prom_metrics_external = {"sent": prometheus_client.Counter('external_ping_packets_sent_total',
'ICMP packets sent',
['destination_name', 'destination_host'],
registry=registry),
"received": prometheus_client.Counter('external_ping_packets_received_total',
'ICMP packets received',
['destination_name', 'destination_host'],
registry=registry),
"rtt": prometheus_client.Counter('external_ping_rtt_milliseconds_total',
'round-trip time',
['destination_name', 'destination_host'],
registry=registry),
"min": prometheus_client.Gauge('external_ping_rtt_min', 'minimum round-trip time',
['destination_name', 'destination_host'],
registry=registry),
"max": prometheus_client.Gauge('external_ping_rtt_max', 'maximum round-trip time',
['destination_name', 'destination_host'],
registry=registry),
"mdev": prometheus_client.Gauge('external_ping_rtt_mdev',
'mean deviation of round-trip times',
['destination_name', 'destination_host'],
registry=registry)}
def validate_envs():
envs = {"MY_NODE_NAME": os.getenv("MY_NODE_NAME"), "PROMETHEUS_TEXTFILE_DIR": os.getenv("PROMETHEUS_TEXTFILE_DIR"),
"PROMETHEUS_TEXTFILE_PREFIX": os.getenv("PROMETHEUS_TEXTFILE_PREFIX")}
for k, v in envs.items():
if not v:
raise ValueError("{} environment variable is empty".format(k))
return envs
@prometheus_exceptions_counter.count_exceptions()
def compute_results(results):
computed = {}
matches = FPING_REGEX.finditer(results)
for match in matches:
host = match.group(1)
ping_results = match.group(2)
if "duplicate" in ping_results:
continue
splitted = ping_results.split(" ")
if len(splitted) != 30:
raise ValueError("ping returned wrong number of results: \"{}\"".format(splitted))
positive_results = [float(x) for x in splitted if x != "-"]
if len(positive_results) > 0:
computed[host] = {"sent": 30, "received": len(positive_results),
"rtt": sum(positive_results),
"max": max(positive_results), "min": min(positive_results),
"mdev": statistics.pstdev(positive_results)}
else:
computed[host] = {"sent": 30, "received": len(positive_results), "rtt": 0,
"max": 0, "min": 0, "mdev": 0}
if not len(computed):
raise ValueError("regex match\"{}\" found nothing in fping output \"{}\"".format(FPING_REGEX, results))
return computed
@prometheus_exceptions_counter.count_exceptions()
def call_fping(ips):
cmdline = FPING_CMDLINE + ips
process = subprocess.run(cmdline, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT, universal_newlines=True)
if process.returncode == 3:
raise ValueError("invalid arguments: {}".format(cmdline))
if process.returncode == 4:
raise OSError("fping reported syscall error: {}".format(process.stderr))
return process.stdout
envs = validate_envs()
files = glob.glob(envs["PROMETHEUS_TEXTFILE_DIR"] + "*")
for f in files:
os.remove(f)
labeled_prom_metrics = {"cluster_targets": [], "external_targets": []}
while True:
with open(CONFIG_PATH, "r") as f:
config = json.loads(f.read())
config["external_targets"] = [] if config["external_targets"] is None else config["external_targets"]
for target in config["external_targets"]:
target["name"] = target["host"] if "name" not in target.keys() else target["name"]
if labeled_prom_metrics["cluster_targets"]:
for metric in labeled_prom_metrics["cluster_targets"]:
if (metric["node_name"], metric["ip"]) not in [(node["name"], node["ipAddress"]) for node in config['cluster_targets']]:
for k, v in prom_metrics_cluster.items():
v.remove(metric["node_name"], metric["ip"])
if labeled_prom_metrics["external_targets"]:
for metric in labeled_prom_metrics["external_targets"]:
if (metric["target_name"], metric["host"]) not in [(target["name"], target["host"]) for target in config['external_targets']]:
for k, v in prom_metrics_external.items():
v.remove(metric["target_name"], metric["host"])
labeled_prom_metrics = {"cluster_targets": [], "external_targets": []}
for node in config["cluster_targets"]:
metrics = {"node_name": node["name"], "ip": node["ipAddress"], "prom_metrics": {}}
for k, v in prom_metrics_cluster.items():
metrics["prom_metrics"][k] = v.labels(node["name"], node["ipAddress"])
labeled_prom_metrics["cluster_targets"].append(metrics)
for target in config["external_targets"]:
metrics = {"target_name": target["name"], "host": target["host"], "prom_metrics": {}}
for k, v in prom_metrics_external.items():
metrics["prom_metrics"][k] = v.labels(target["name"], target["host"])
labeled_prom_metrics["external_targets"].append(metrics)
out = call_fping([prom_metric["ip"] for prom_metric in labeled_prom_metrics["cluster_targets"]] + \
[prom_metric["host"] for prom_metric in labeled_prom_metrics["external_targets"]])
computed = compute_results(out)
for dimension in labeled_prom_metrics["cluster_targets"]:
result = computed[dimension["ip"]]
dimension["prom_metrics"]["sent"].inc(computed[dimension["ip"]]["sent"])
dimension["prom_metrics"]["received"].inc(computed[dimension["ip"]]["received"])
dimension["prom_metrics"]["rtt"].inc(computed[dimension["ip"]]["rtt"])
dimension["prom_metrics"]["min"].set(computed[dimension["ip"]]["min"])
dimension["prom_metrics"]["max"].set(computed[dimension["ip"]]["max"])
dimension["prom_metrics"]["mdev"].set(computed[dimension["ip"]]["mdev"])
for dimension in labeled_prom_metrics["external_targets"]:
result = computed[dimension["host"]]
dimension["prom_metrics"]["sent"].inc(computed[dimension["host"]]["sent"])
dimension["prom_metrics"]["received"].inc(computed[dimension["host"]]["received"])
dimension["prom_metrics"]["rtt"].inc(computed[dimension["host"]]["rtt"])
dimension["prom_metrics"]["min"].set(computed[dimension["host"]]["min"])
dimension["prom_metrics"]["max"].set(computed[dimension["host"]]["max"])
dimension["prom_metrics"]["mdev"].set(computed[dimension["host"]]["mdev"])
prometheus_client.write_to_textfile(
envs["PROMETHEUS_TEXTFILE_DIR"] + envs["PROMETHEUS_TEXTFILE_PREFIX"] + envs["MY_NODE_NAME"] + ".prom", registry)
該腳本在每個Kubernetes節點上運行，并且每秒兩次發送ICMP數據包到Kubernetes集群的所有實例。收集的結果會存儲在文本文件中。
該腳本會包含在Docker鏡像中：
FROM python:3.6-alpine3.8 COPY rootfs / WORKDIR /app RUN pip3 install --upgrade pip && pip3 install -r requirements.txt && apk add --no-cache fping ENTRYPOINT ["python3", "/app/ping-exporter.py"]
另外，我們還創建了一個ServiceAccount和一個具有唯一權限的對應角色用于獲取節點列表(這樣我們就可以知道它們的IP地址)：
apiVersion: v1 kind: ServiceAccount metadata: name: ping-exporter namespace: d8-system --- kind: ClusterRole apiVersion: rbac.authorization.k8s.io/v1 metadata: name: d8-system:ping-exporter rules: - apiGroups: [""] resources: ["nodes"] verbs: ["list"] --- kind: ClusterRoleBinding apiVersion: rbac.authorization.k8s.io/v1 metadata: name: d8-system:kube-ping-exporter subjects: - kind: ServiceAccount name: ping-exporter namespace: d8-system roleRef: apiGroup: rbac.authorization.k8s.io kind: ClusterRole name: d8-system:ping-exporter
最后，我們需要DaemonSet來運行在集群中的所有實例：
apiVersion: apps/v1 kind: DaemonSet metadata: name: ping-exporter namespace: d8-system spec: updateStrategy: type: RollingUpdate selector: matchLabels: name: ping-exporter template: metadata: labels: name: ping-exporter spec: terminationGracePeriodSeconds: 0 tolerations: - operator: "Exists" hostNetwork: true serviceAccountName: ping-exporter priorityClassName: cluster-low containers: - image: private-registry.flant.com/ping-exporter/ping-exporter:v1 name: ping-exporter env: - name: MY_NODE_NAME valueFrom: fieldRef: fieldPath: spec.nodeName - name: PROMETHEUS_TEXTFILE_DIR value: /node-exporter-textfile/ - name: PROMETHEUS_TEXTFILE_PREFIX value: ping-exporter_ volumeMounts: - name: textfile mountPath: /node-exporter-textfile - name: config mountPath: /config volumes: - name: textfile hostPath: path: /var/run/node-exporter-textfile - name: config configMap: name: ping-exporter-config imagePullSecrets: - name: private-registry
該解決方案的最后操作細節是：
Python腳本執行時，其結果(即存儲在主機上/var/run/node-exporter-textfile目錄中的文本文件)將傳遞到DaemonSet類型的node-exporter。
node-exporter使用--collector.textfile.directory /host/textfile參數啟動，這里的/host/textfile是hostPath目錄/var/run/node-exporter-textfile。(你可以點擊這里了解關于node-exporter中文本文件收集器的更多信息。)
最后node-exporter讀取這些文件，然后Prometheus從node-exporter實例上收集所有數據。
那么結果如何?
現在該來享受期待已久的結果了。指標創建之后，我們可以使用它們，當然也可以對其進行可視化。以下可以看到它們是怎樣的。
首先，有一個通用選擇器可讓我們在其中選擇節點以檢查其“源”和“目標”連接。你可以獲得一個匯總表，用于在Grafana儀表板中指定的時間段內ping選定節點的結果：
以下是包含有關選定節點的組合統計信息的圖形：
另外，我們有一個記錄列表，其中每個記錄都鏈接到在“源”節點中選擇的每個特定節點的圖：
如果將記錄展開，你將看到從當前節點到目標節點中已選擇的所有其他節點的詳細ping統計信息：
下面是相關的圖形：
節點之間的ping出現問題的圖看起來如何?
如果你在現實生活中觀察到類似情況，那就該進行故障排查了!
最后，這是我們對外部主機執行ping操作的可視化效果：
我們可以檢查所有節點的總體視圖，也可以僅檢查任何特定節點的圖形：
看完了這篇文章，相信你對“Kubernetes節點之間的ping監控怎么實現”有了一定的了解，如果想了解更多相關知識，歡迎關注億速云行業資訊頻道，感謝各位的閱讀！
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