Question

我正在寻找一种使用Datadog监控GCP Dataflow管道的解决方案，以提取内置指标以及Beam自定义指标。目前，Datadog为其他GCP服务提供集成，但不为Dataflow提供集成。有没有人做过类似的工作，可以分享如何将其构建为自定义解决方案的指针？

Answer 1

目前，我仅看到两种可能性：

使用来自Google-cloud-clients / google-cloud-monitoring的客户端和与Datadog集成的stackdriver来使用GCP自定义指标
使用部署在云中的datadog代理，并使用Datadog StatsD客户端（Java，Python，Go）连接到它

使用GCP自定义指标 https://cloud.google.com/monitoring/custom-metrics/creating-metrics 和datadog与GCP集成 https://www.datadoghq.com/product/integrations/#cat-google-cloud

final MetricServiceClient client = MetricServiceClient.create();
ProjectName name = ProjectName.of(projectId);

MetricDescriptor descriptor = MetricDescriptor.newBuilder()
    .setType(metricType)
    .setDescription("This is a simple example of a custom metric.")
    .setMetricKind(MetricDescriptor.MetricKind.GAUGE)
    .setValueType(MetricDescriptor.ValueType.DOUBLE)
    .build();

CreateMetricDescriptorRequest request = CreateMetricDescriptorRequest.newBuilder()
    .setName(name.toString())
    .setMetricDescriptor(descriptor)
    .build();

client.createMetricDescriptor(request);

使用datadog statsd客户端，Java一个- https://github.com/DataDog/java-dogstatsd-client，以便您可以部署 GCP上的datadog代理并通过它进行连接。使用Kubernetes的示例。 https://docs.datadoghq.com/tracing/setup/kubernetes/#deploy-agent-daemonset

import com.timgroup.statsd.ServiceCheck;
import com.timgroup.statsd.StatsDClient;
import com.timgroup.statsd.NonBlockingStatsDClient;

public class Foo {

  private static final StatsDClient statsd = new NonBlockingStatsDClient(
    "my.prefix",                          /* prefix to any stats; may be null or empty string */
    "statsd-host",                        /* common case: localhost */
    8125,                                 /* port */
    new String[] {"tag:value"}            /* Datadog extension: Constant tags, always applied */
  );

  public static final void main(String[] args) {
    statsd.incrementCounter("foo");
    statsd.recordGaugeValue("bar", 100);
    statsd.recordGaugeValue("baz", 0.01); /* DataDog extension: support for floating-point gauges */
    statsd.recordHistogramValue("qux", 15);     /* DataDog extension: histograms */
    statsd.recordHistogramValue("qux", 15.5);   /* ...also floating-point */
    statsd.recordDistributionValue("qux", 15);     /* DataDog extension: global distributions */
    statsd.recordDistributionValue("qux", 15.5);   /* ...also floating-point */

    ServiceCheck sc = ServiceCheck
          .builder()
          .withName("my.check.name")
          .withStatus(ServiceCheck.Status.OK)
          .build();
    statsd.serviceCheck(sc); /* Datadog extension: send service check status */

    /* Compatibility note: Unlike upstream statsd, DataDog expects execution times to be a
     * floating-point value in seconds, not a millisecond value. This library
     * does the conversion from ms to fractional seconds.
     */
    statsd.recordExecutionTime("bag", 25, "cluster:foo"); /* DataDog extension: cluster tag */
  }
}

用于kubernetes的datadog deployment.yaml

apiVersion: extensions/v1beta1
kind: DaemonSet
metadata:
  name: datadog-agent
spec:
  template:
    metadata:
      labels:
        app: datadog-agent
      name: datadog-agent
    spec:
      serviceAccountName: datadog-agent
      containers:
      - image: datadog/agent:latest
        imagePullPolicy: Always
        name: datadog-agent
        ports:
          - containerPort: 8125
            # hostPort: 8125
            name: dogstatsdport
            protocol: UDP
          - containerPort: 8126
            # hostPort: 8126
            name: traceport
            protocol: TCP
        env:
          - name: DD_APM_ENABLED
            value: "true"
          - name: DD_API_KEY
            value: "<YOUR_API_KEY>"
          - name: DD_COLLECT_KUBERNETES_EVENTS
            value: "true"
          - name: DD_LEADER_ELECTION
            value: "true"
          - name: KUBERNETES
            value: "yes"
          - name: DD_KUBERNETES_KUBELET_HOST
            valueFrom:
              fieldRef:
                fieldPath: status.hostIP
        resources:
          requests:
            memory: "256Mi"
            cpu: "200m"
          limits:
            memory: "256Mi"
            cpu: "200m"
        volumeMounts:
          - name: dockersocket
            mountPath: /var/run/docker.sock
          - name: procdir
            mountPath: /host/proc
            readOnly: true
          - name: cgroups
            mountPath: /host/sys/fs/cgroup
            readOnly: true
        livenessProbe:
          exec:
            command:
            - ./probe.sh
          initialDelaySeconds: 15
          periodSeconds: 5
      volumes:
        - hostPath:
            path: /var/run/docker.sock
          name: dockersocket
        - hostPath:
            path: /proc
          name: procdir
        - hostPath:
            path: /sys/fs/cgroup
          name: cgroups

目前，我正在对此进行调查，因此我不确定如何执行此操作。

使用Datadog监视Dataflow管道

1 个答案: