View metrics

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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:

• Metrics Explorer documentation
• Introduction to alerting
• Creating charts

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 /proxyv2/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/proxyv2/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.

Proxy, target, and server traffic metrics

The Prometheus service collects and processes metrics (as described in Metrics collection) for proxy, target, and server traffic.

The following table describes the metrics and labels that Prometheus uses. These labels are used in the metrics log entries.

Metric name	Label	Use
/proxyv2/request_count	method	The total number of API proxy requests received.
/proxyv2/response_count	method response_code	The total number of API proxy responses received.
/proxyv2/latencies_percentile	method	Percentile of all API policy responses to a request.
/targetv2/request_count	method target_type target_endpoint	The total number of requests sent to the proxy's target.
/targetv2/response_count	method response_code target_type target_endpoint	The total number of responses received from the proxy's target.
/server/fault_count	source	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	state	The number of open sockets.
/server/num_threads		The number of active non-daemon threads in the server.
/server/request_count	method type	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	method response_code type	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	method response_code type	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	method type	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	method response_code type	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	method response_code type	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

The Prometheus service 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 and labels that Prometheus uses in the UDCA metrics data. These labels are used in the metrics log entries.

Metric name	Label	Use
/udca/server/local_file_oldest_ts	dataset state	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	dataset state	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	dataset state	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	dataset	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	dataset	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	service dataset response_code	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	service dataset	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	dataset	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	dataset	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	dataset state	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	dataset state	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	dataset	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

The Prometheus service 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 and labels that Prometheus uses in the Cassandra metrics data. These labels are used in the metrics log entries.

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