View metrics

This topic explains how to view Apigee hybrid metrics in a Cloud Operations dashboard.

About Cloud Operations

For more information about metrics, dashboards, and Cloud Operations see:

Enabling hybrid metrics

Before hybrid metrics can be sent to Cloud Operations , you must first enable metrics collection. See Configure metrics collection for this procedure.

About hybrid metric names and labels

When enabled, hybrid automatically populates Cloud Operations metrics. The domain name prefix of the metrics created by hybrid is:

apigee.googleapis.com/

For example, the /proxy/request_count metric contains the total number of requests received by an API proxy. The metric name in Cloud Operations is therefore:

apigee.googleapis.com/proxy/request_count

Cloud Operations lets you filter and group metrics data based on labels. Some labels are predefined, and others are added explicitly by hybrid. The Available metrics section below lists all of the available hybrid metrics and any labels added specifically for a metric that you can use for filtering and grouping.

Viewing metrics

The following example shows how to view metrics in Cloud Operations:
  1. Open the Monitoring Metrics Explorer in a browser. Alternatively, if you're already in the Cloud Operations console, select Metrics explorer.

  2. In Find resource type and metric, locate and select the metric you want to examine. Choose a specific metric listed in Available metrics , or search for a metric.

  3. Select the desired metric.
  4. Apply filters. Filter choices for each metric are listed in Available metrics .
  5. Cloud Operations displays the chart for the selected metric.
  6. Click Save.

Creating a dashboard

Dashboards are one way for you to view and analyze metric data that is important to you. Cloud Operations provides predefined dashboards for the resources and services that you use, and you can also create custom dashboards.

You use a chart to display an Apigee metric in your custom dashboard. With custom dashboards, you have complete control over the charts that are displayed and their configuration. For more information on creating charts, see Creating charts .

The following example shows how to create a dashboard in Cloud Operations and then to add charts to view metrics data:

  1. Open the Monitoring Metrics Explorer in a browser and then select Dashboards.
  2. Select + Create Dashboard.
  3. Give the dashboard a name. For example: Hybrid Proxy Request Traffic
  4. Click Confirm.

  5. For each chart that you want to add to your dashboard, follow these steps:

    1. In the dashboard, select Add chart.
    2. Select the desired metric as described above in Viewing metrics .
    3. Complete the dialog to define your chart.
    4. Click Save. Cloud Operations displays data for the selected metric.

Available metrics

The following tables list metrics for analyzing proxy traffic. For more information about each Apigee metric, see Google cloud metrics .

Proxy, target, and server traffic metrics

Open Telemetry collects and processes metrics (as described in Metrics collection ) for proxy, target, and server traffic.

The following table describes the metrics that the Open Telemetry collector uses.

Metric name Use
/proxy/request_count Number of requests to the Apigee proxy since the last sample was recorded.
/proxy/response_count Number of responses sent by the Apigee API proxy.
/proxy/latencies Distribution of latencies, which are calculated from the time the request was received by the Apigee proxy to the time the response was sent from the Apigee proxy to the client.
/proxyv2/request_count The total number of API proxy requests received.
/proxyv2/response_count The total number of API proxy responses received.
/proxyv2/latencies_percentile Percentile of all API policy responses to a request.
/target/request_count Number of requests sent to the Apigee target since the last sample was recorded.
/target/response_count Number of responses received from the Apigee target since the last sample was recorded.
/target/latencies Distribution of latencies, which are calculated from the time the request was sent to the Apigee target to the time the response was received by the Apigee proxy. Time does not include the Apigee API proxy overhead.
/targetv2/request_count The total number of requests sent to the proxy's target.
/targetv2/response_count The total number of responses received from the proxy's target.
/server/fault_count

The total number of faults for the server application.

For example, the application could be apigee-runtime , apigee-synchronizer , or apigee-udca . Use the pod_name label to filter results by application.
/server/nio The number of open sockets.
/server/num_threads The number of active non-daemon threads in the server.
/server/request_count

The total number of requests received by the server application.

For example, the application could be apigee-runtime , apigee-synchronizer , or apigee-udca . Use the pod_name label to filter results by application.
/server/response_count

Total number of responses sent by the server application.

For example, the application could be apigee-runtime , apigee-synchronizer , or apigee-udca . Use the pod_name label to filter results by application.
/server/latencies

Latency is the latency in millisecs introduced by the server application.

For example, the application could be apigee-runtime , apigee-synchronizer , or apigee-udca . Use the pod_name label to filter results by application.
/upstream/request_count

The number of requests sent by the server application to its upstream application.

For example, for the apigee-synchronizer , the control plane is upstream. So upstream/request_count for apigee-synchronizer is a metric that indicates the requests that apigee-synchronizer made to the control plane.
/upstream/response_count

The number of responses received by the server application from its upstream application.

For example, for the apigee-synchronizer , the control plane is upstream. So upstream/response_count for apigee-synchronizer is a metric that indicates the requests that apigee-synchronizer received from the control plane.
/upstream/latencies

The latency incurred at the upstream server application in milliseconds.

For example, for the apigee-synchronizer , the control plane is upstream. So upstream/latencies for apigee-synchronizer is a metric that indicates the latency from the control plane.

UDCA metrics

Open Telemetry collects and processes metrics (as described in Metrics collection ) for the UDCA service just as it does for other hybrid services.

The following table describes the metrics that the Open Telemetry collector uses in the UDCA metrics data.

Metric name
Use
/udca/server/local_file_oldest_ts

The timestamp, in milliseconds since the start of the Unix Epoch, for the oldest file in the dataset.

This is computed every 60 seconds and does not reflect the state in real time. If the UDCA is up to date and there are no files waiting to be uploaded when this metric is computed, then this value will be 0.

If this value keeps increasing, then old files are still on disk.

/udca/server/local_file_latest_ts

The timestamp, in milliseconds since the start of the Unix Epoch, for the latest file on disk by state.

This is computed every 60 seconds and does not reflect the state in real time. If the UDCA is up to date and there are no files waiting to be uploaded when this metric is computed, then this value will be 0.

/udca/server/local_file_count

A count of the number of files on disk in the data collection pod.

Ideally, the value will be close to 0. A consistent high value indicates that files are not being uploaded or that the UDCA is not able to upload them fast enough.

This value is computed every 60 seconds and does not reflect the state of the UDCA in real time.

/udca/server/total_latencies

The time interval, in seconds, between the data file being created and the data file being successfully uploaded.

Buckets will be 100ms, 250ms, 500ms, 1s, 2s, 4s, 8s, 16s, 32s, and 64s.

Histogram for total latency from file creation time to successful upload time.

/udca/server/upload_latencies

The total time, in seconds, that UDCA spent uploading a data file.

Buckets will be 100ms, 250ms, 500ms, 1s, 2s, 4s, 8s, 16s, 32s, and 64s.

The metrics will display a histogram for total upload latency, including all upstream calls.

/udca/upstream/http_error_count

The total count of HTTP errors that UDCA encountered. This metric is useful to help determine which part of the UDCA external dependencies are failing and for what reason.

These errors can arise for various services ( getDataLocation , Cloud storage , Token generator ) and for various datasets (such as api and trace ) with various response codes.

/udca/upstream/http_latencies

The upstream latency of services, in seconds.

Buckets will be 100ms, 250ms, 500ms, 1s, 2s, 4s, 8s, 16s, 32s, and 64s.

Histogram for latency from upstream services.

/udca/upstream/uploaded_file_sizes

The size of the file being uploaded to the Apigee services, in bytes.

Buckets will be 1KB, 10KB, 100KB, 1MB, 10MB, 100MB, and 1GB.

Histogram for file size by dataset, organization and environment.

/udca/upstream/uploaded_file_count
A count of the files that UDCA uploaded to the Apigee services.

Note that:

  • The event dataset value should keep growing.
  • The api dataset value should keep growing if org/env has constant traffic.
  • The trace dataset value should increase when you use the Apigee trace tools to debug or inspect your requests.
/udca/disk/used_bytes

The space occupied by the data files on the data collection pod's disk, in bytes.

An increase in this value over time:

  • ready_to_upload implies the agent is lagging behind.
  • failed implies files are piling up on disk and not being uploaded. This value is computed every 60 seconds.
/udca/server/pruned_file_count
Count of files which have been deleted because their Time To Life (TTL) was beyond a set threshold. The dataset can include API, trace, and others, and state can be UPLOADED , FAILED , or DISCARDED .
/udca/server/retry_cache_size

A count of the number of files, by dataset, that UDCA is retrying to upload.

After 3 retries for each file, UDCA moves the file to the /failed subdirectory and removes it from this cache. An increase in this value over time implies that the cache is not being cleared, which happens when files are moved to the /failed subdirectory after 3 retries.

Cassandra metrics

Open Telemetry collects and processes metrics (as described in Metrics collection ) for Cassandra just as it does for other hybrid services.

The following table describes the metrics that the Open Telemetry collector uses in the Cassandra metrics data.

Metric name (excluding domain) Use
/cassandra/process_max_fds Maximum number of open file descriptors.
/cassandra/process_open_fds Open file descriptors.
/cassandra/jvm_memory_pool_bytes_max JVM maximum memory usage for the pool.
/cassandra/jvm_memory_pool_bytes_init JVM initial memory usage for the pool.
/cassandra/jvm_memory_bytes_max JVM heap maximum memory usage.
/cassandra/process_cpu_seconds_total User and system CPU time spent in seconds.
/cassandra/jvm_memory_bytes_used JVM heap memory usage.
/cassandra/compaction_pendingtasks Outstanding compactions for Cassandra sstables. See Compaction for more.
/cassandra/jvm_memory_bytes_init JVM heap initial memory usage.
/cassandra/jvm_memory_pool_bytes_used JVM pool memory usage.
/cassandra/jvm_memory_pool_bytes_committed JVM pool committed memory usage.
/cassandra/clientrequest_latency Read request latency in the 75th percentile range in microseconds.
/cassandra/jvm_memory_bytes_committed JVM heap committed memory usage.

Working with Cassandra metrics

Apigee recommends the following metrics as critical to monitor for your Cassandra database:

  • Cassandra request rate : Use this metric to monitor the cassandra read and write request rate.
    Metric: apigee.googleapis.com/cassandra/clientrequest_latency
    Resource labels: project_id , location , cluster_name , namespace_name , pod_name , container_name
    Metric labels: scope , unit

    Use these labels to filter the specific resource or for grouping.

    To monitor cassandra read request rate, apply the following filter.

    Filters:
    metric.scope == 'Read'
    metric.unit == 'OneMinuteRate'

    To monitor cassandra write request rate, apply the following filter.

    Filters:
    metric.scope == 'Write'
    metric.unit == 'OneMinuteRate'
  • Cassandra request latency : Use this metric to monitor the cassandra read and write request latency. This is the same metric as the request rate, apigee.googleapis.com/cassandra/clientrequest_latency with different filters applied.

    To monitor cassandra read request latency, apply the following filter.

    Filters:
    metric.scope == 'Read'
    metric.unit == '99thPercentile' or '95thPercentile' or '75thPercentile'

    To monitor cassandra write request latency, apply the following filter.

    Filters:
    metric.scope == 'Write'
    metric.unit == '99thPercentile' or '95thPercentile' or '75thPercentile'
  • Cassandra pod CPU request utilization
    Metric: kubernetes.io/container/cpu/request_utilization (GKE on Google Cloud)
    kubernetes.io/anthos/container/cpu/request_utilization (Google Distributed Cloud)
    Resource labels: project_id , location , cluster_name , namespace_name , pod_name , container_name

    Use these labels to filter the specific resource or for grouping.
  • Cassandra data volume utilization
    Metric: kubernetes.io/pod/volume/utilization (GKE on Google Cloud)
    kubernetes.io/anthos/pod/volume/utilization (Google Distributed Cloud)
    Resource labels: project_id , location , cluster_name , namespace_name , pod_name
    Metric labels: volume_name

    Use these labels to filter the specific resource or for grouping.

Recommendations for scaling the Cassandra cluster

The following guidelines can serve as a recommended cluster for the decision to scale your Cassandra cluster. In general, if read or write requests consistently show 99th percentile latency or the latency is trending upwards continuously, and you see corresponding spikes in CPU request utilization spike and the read or write request rates, your Cassandra cluster can be considered to be under stress. You may want to consider scaling up the cluster. For more information see Scaling Cassandra

Metric Threshold Trigger duration
kubernetes.io/pod/volume/utilization
85% 5min
kubernetes.io/container/cpu/request_utilization
85% 3min
Read request Latency 99thPercentile
5s 3min
Write request Latency 99thPercentile
5s 3min