Monitoring overview

The Pub/Sub API exports metrics via Cloud Monitoring. Cloud Monitoring lets you to create monitoring dashboards and alerting policies or access the metrics programmatically.

Viewing metrics

To view Cloud Monitoring dashboards or to define alerting policies, go to Monitoring in the Google Cloud Console:

Go to Monitoring

You can also use the Cloud Monitoring API to query and view metrics for subscriptions and topics.

Metrics and resource types

To see the usage metrics that Pub/Sub reports to Cloud Monitoring, view the Metrics List in the Cloud Monitoring documentation.
To see the details for the pubsub_topic, pubsub_subscription, or pubsub_snapshot monitored resource types, view Monitored Resource Types in the Cloud Monitoring documentation.

Ensuring you don't run out of quota

For a given project, you can use the IAM & admin quotas dashboard to view current quotas and usage.

You can view your historical quota usage using the following metrics:

serviceruntime.googleapis.com/quota/rate/net_usage
serviceruntime.googleapis.com/quota/limit

These metrics use the consumer_quota monitored resource type. For more quota-related metrics, see the Metrics List.

For example, the following Monitoring Query Language query creates a chart with the fraction of publisher quota being used in each region:

  fetch consumer_quota
  | filter resource.service == 'pubsub.googleapis.com'
  | { metric serviceruntime.googleapis.com/quota/rate/net_usage
      | filter metric.quota_metric == 'pubsub.googleapis.com/regionalpublisher'
      | align delta_gauge(1m)
      | group_by [metric.quota_metric, resource.location],
          sum(value.net_usage)
    ; metric serviceruntime.googleapis.com/quota/limit
      | filter metric.quota_metric == 'pubsub.googleapis.com/regionalpublisher'
      | group_by [metric.quota_metric, resource.location],
          sliding(1m), max(val()) }
  | ratio

If you anticipate your usage exceeding the default quota limits, create alerting policies for all the relevant quotas. These alerts should fire when your usage reaches some fraction of the limit. For example, the following Monitoring Query Language query will trigger your alerting policy when any Pub/Sub quota exceeds 80% usage:

  fetch consumer_quota
  | filter resource.service == 'pubsub.googleapis.com'
  | { metric serviceruntime.googleapis.com/quota/rate/net_usage
      | align delta_gauge(1m)
      | group_by [metric.quota_metric, resource.location],
          sum(value.net_usage)
    ; metric serviceruntime.googleapis.com/quota/limit
      | group_by [metric.quota_metric, resource.location],
          sliding(1m), max(val()) }
  | ratio
  | every 1m
  | condition gt(val(), 0.8 '1')

For more customized monitoring of and alerting on quota metrics, see Using quota metrics.

See Quotas and limits for more information about quotas.

Keeping subscribers healthy

Monitoring the backlog

To ensure that your subscribers are keeping up with the flow of messages, create a dashboard that shows the following metrics, aggregated by resource, for all your subscriptions:

subscription/num_undelivered_messages
subscription/oldest_unacked_message_age

Create alerting policies that will fire when these values are unusually large in the context of your system. For instance, the absolute number of undelivered messages is not necessarily meaningful. A backlog of a million messages might be acceptable for a million message-per-second subscription, but unacceptable for a one message-per-second subscription.

Symptoms	Problem	Solutions
Both the `oldest_unacked_message_age` and `num_undelivered_messages` are growing in tandem.	Subscribers not keeping up with message volume	Add more subscriber threads or processes. Add more subscriber machines or containers. Look for signs of bugs in your code that prevent it from successfully acknowledging messages or processing them in a timely fashion (see Monitoring ack deadline expiration).
If there is a steady, small backlog size combined with a steadily growing `oldest_unacked_message_age`, there may be a small number of messages that cannot be processed.	Stuck messages	Examine your application logs to understand whether some messages are causing your code to crash. It's unlikely—but possible —that the offending messages are stuck on Pub/Sub rather than in your client. Raise a support case once you are confident your code successfully processes each message.
The `oldest_unacked_message_age` exceeds the subscription's message retention duration.	Permanent data loss	Set up an alert that fires well in advance of the subscription's message retention duration lapsing.

Monitoring ack deadline expiration

In order to reduce end-to-end latency of message delivery, Pub/Sub allows subscriber clients a limited amount of time to acknowledge a given message (known as the "ack deadline") before re-delivering the message. If your subscribers take too long to acknowledge messages, the messages will be re-delivered, resulting in the subscribers seeing duplicate messages. This can happen for a number of reasons:

Your subscribers are under-provisioned (you need more threads or machines).
Each message takes longer to process than the message acknowledgement deadline. Google Cloud Client Libraries generally extend the deadline for individual messages up to a configurable maximum. However, a maximum extension deadline is also in effect for the libraries.
Some messages consistently crash the client.

It can be useful to measure the rate at which subscribers miss the ack deadline. The specific metric depends on the subscription type:

Pull and StreamingPull: subscription/pull_ack_message_operation_count filtered by response_code != "success"
Push: subscription/push_request_count filtered by response_code != "success"

Excessive ack deadline expiration rates can result in costly inefficiencies in your system. You pay for every redelivery and for attempting to process each message repeatedly. Conversely, a small expiration rate (for example, 0.1-1%) might be healthy.

Monitoring push subscriptions

For push subscriptions, you should also monitor these metrics:

subscription/push_request_count

Group the metric by response_code and subcription_id. Since Pub/Sub push subscriptions use response codes as implicit message acknowledgements, it is important to monitor push request response codes. Because push subscriptions exponentially back off when they encounter timeouts or errors, your backlog can grow quickly based on how your endpoint responds.

Consider setting an alert for high error rates (create a metric filtered by response class), since those rates lead to slower delivery and a growing backlog. However, push request counts are likely to be more useful as a tool for investigating growing backlog size and age.
subscription/num_outstanding_messages

Pub/Sub generally limits the number of outstanding messages. You should aim for fewer than 1000 outstanding messages in most situations. As a rule, the service adjusts the limit based on the overall throughput of the subscription in increments of 1000, once the throughput achieves a rate on the order of ten thousand messages per second. No specific guarantees are made beyond the maximum value, so 1000 is a good guide.
subscription/push_request_latencies

This metric helps you understand your push endpoint's response latency distribution. Because of the limit on the number of outstanding messages, endpoint latency affects subscription throughput. If it takes 100 milliseconds to process each message, your throughput limit is likely to be 10 messages per second.

To access higher outstanding message limits, push subscribers must acknowledge more than 99% of the messages they receive.

You can calculate the fraction of messages that subscribers acknowledge using the Monitoring Query Language. The following MQL query creates a chart with the fraction of messages that subscribers acknowledge on a subscription:

  fetch pubsub_subscription
  | metric 'pubsub.googleapis.com/subscription/push_request_count'
  | filter
      (resource.subscription_id == '$SUBSCRIPTION')
      | filter_ratio_by [], metric.response_class == 'ack'
      | every 1m

Monitoring subscriptions with filters

Pub/Sub automatically acknowledges the messages that don't match a filter. You can monitor the number, size, and cost of these messages.

To monitor the number of messages that don't match a filter, use the subscription/ack_message_count metric with the delivery_type label and the filter value.

To monitor the size and cost of messages that don't match a filter, use the subscription/byte_cost metric with the operation_type label and the filter_drop value. For more information about the fees for these messages, see the Pub/Sub pricing page.

Monitoring forwarded undeliverable messages

To monitor undeliverable messages that Pub/Sub forwards to a dead-letter topic, use the subscription/dead_letter_message_count metric. The subscription/dead_letter_message_count metric counts the number of undeliverable messages that Pub/Sub forwards from a subscription.

To verify that Pub/Sub is forwarding undeliverable messages, you can compare the subscription/dead_letter_message_count metric with the topic/send_message_operation_count metric from the topic that Pub/Sub forwards these messages to.

You can also attach a subscription to the dead-letter topic and then monitor the forwarded undeliverable messages using the following metrics:

subscription/num_undelivered_messages: the number of messages that subscribers haven't processed
subscription/oldest_unacked_message_age: the amount of time before the next message expires

Keeping publishers healthy

The primary goal of a publisher is to persist message data quickly. Monitor this performance using topic/send_request_count, grouped by response_code. This metric gives you an indication of whether Pub/Sub is healthy and accepting requests.

A background rate of retryable errors (significantly lower than 1%) should not be a cause for concern, since most Google Cloud Client Libraries retry message failures. You should investigate error rates that are greater than 1%. Because non-retryable codes are handled by your application (rather than by the client library), you should examine response codes. If your publisher application does not have a good way of signaling an unhealthy state, consider setting an alert on the send_request_count metric.

It is equally important to track failed publish requests in your publish client. While client libraries generally retry failed requests, they do not guarantee publication. Refer to Publishing messages for ways to detect permanent publish failures when using Google Cloud Client Libraries. At a minimum, your publisher application should log permanent publish errors. If you log those errors to Cloud Logging, you can set up a logs-based metric with an alerting policy.