Spinnaker is instrumented with numerous metrics internally so that it can be monitored. Monitoring spinnaker typically involves the spinnaker-monitoring daemon, which collects metrics reported by each microservice instance and reports them to a third-party monitoring system which you then use to view overview dashboards, receive alerts, and informally browse depending on your needs.
Spinnaker publishes internal metrics using a multi-dimensional data model based on “tags”. The metrics, data-model, and usage a discussed further in the sections Consuming Metrics and in the Monitoring Reference document.
Spinnaker currently supports three specific third-party systems: Prometheus, Datadog, and Stackdriver. The daemon is extensible so that it should be straight forward to add other systems as well. In fact, each of the supported systems was provided using the daemon’s extension mechanisms – there are not “native” systems.
You can also use the microservice HTTP endpoint
directly to scrape metrics yourself. The JSON document structure is
further documented in the Monitoring reference section.
Configuring Spinnaker Monitoring
Halyard ensures that the Spinnaker monitoring daemon is installed on every host that runs a Spinnaker service capable of being monitored, and is provided with the necessary configuration to supply the third-party system of your choice with each Spinnaker service’s metrics. To do so, Halyard must be provided with third-party specific credentials and/or endpoints explained in each system’s configuration below:
Once this is complete and Spinnaker is deployed, you can optionally use the
spinnaker-monitoring-third-party package to deploy pre-configured Spinnaker
dashboards to your third-party system of choice.
Spinnaker publishes internal metrics using a multi-dimensional data model based on “tags”. Each “metric” has a name and type. Each data point is a numeric value that is time-stamped at the time of reporting and tagged with a set of one or more “label”=”value” tags. These tag values are strings, though some may have numeric-looking values. Taken together, the set of tags convey the context for the reported measurement. Each of these contexts forms a distinct time-series data stream.
For example a metric counting we requests may be tagged with a “status” label and values indicating whether the call was successful or not. So rather than having two metrics, one for successful calls and the other for unsuccessful calls, there is a single metric, where the underlying monitoring system can filter the successful from unsuccessful as you want depending on how you wish to abstract and interpret the data. In practice the metrics have many tags providing a lot of granularity and ways in which you can aggregate and interpret them. The data model is described further in the Monitoring reference section.
In practice there are relatively few distinct metric names (a few hundred). However when considering all the distinct time-series streams from the different label values there are several thousand distinct streams. Some metrics are tagged with the application or account they were used on behalf of, so the number of streams may grow as the scope of your deployment grows. Typically you will be aggregating these dimensions together while breaking out along others. The granularity can come in handy when it comes time to diagnose problems or investigate for deeper understanding of runtime behaviors but you can aggregate across dimensions (or parts of dimensions) when you dont care about that level of refinement.
Each microservice defines and exports its own metrics. The
spinnaker-monitoring daemon augments these by identifying which
microservice they came from (how it does so depends on the backend
metrics system you are using). There are no global “Spinnaker” metrics.
There are only individual metrics on the individual instance of
concrete microservices that, taken together, compose a “Spinnaker” deployment.
Types of Metrics
There are two basic types of metrics currently supported, counters and gauges.
Counters are monotonically increasing values over the lifetime of the process. The process starts out with them at 0, then increments them as appropriate. Some counters may increase by 1 each time, such as the number of calls. Other counters may increase by an arbitrary (but non-negative) amount, such as number of bytes.
Counters are scoped to the process they are in. If you have a counter in each of two different microservice replicas (including a restart), those counters will be independent of one another. Each process only knows about itself. The daemon adds a tag to each data point that identifies which instance it came from so that you can drill down into individual instances if you need. However, typically you will use your monitoring system to aggregate counters across all replicas.
Counters are useful to determine rates. Given two points in time, the counter differences will be the measurement delta and the delta divided by the time difference will be the rate. (divide by another 1000000 to convert nanoseconds to milliseconds, such as for latency-oriented metrics or by another 100000000 for seconds, such as for call-rate metrics).
Spinnaker also has a special type of counter called a Timer.
Timers are used to measure timing information. These are always in nanoseconds. When consuming metrics straight from Spinnaker, a Timer will have two complementary time series. One will have a tag “statistic” with the value “count” and the other a tag with a “statistic” with the value “totalTime”.
The Monitoring Daemon transforms these into two distinct counters and removes the “statistic” label entirely. The “count” counter is named with the original metric name plus a “__count” suffix. The “totalTime” with a “__totalTime” suffix.
The “count” represents the number of measurements taken. The “totalTime” represents the number of nanoseconds measured across all the calls. Dividing the “totalTime” by the “count” over some time window gives the latency over that time window.
For example given a series of measurements for the pair of metrics example__count and example__totalTime, where the sum of the __count values was 5 and of the __totalTime values was 50000000, then dividing the time by count gives 10000000 as an average time per count. Since this is in nanoseconds, we can divide by another 1000000000 to get 0.1 seconds per call. (or we could divide by 1000000 to get 100 milliseconds per call)
Note that in order to do this, the tag bindings for the two measurements should be the same. Dividing measurements whose count has a success=true tag by times that have success=false tags wont give you the average time of the success calls (but would give you the average cost in total time spent for each successful call outcome if that is what you wanted.)
Gauges are instantaneous value readings at a given point in time. Like counters, individual gauges are scoped to individual microservice instances. The daemon adds an instance tag to each data point so that you can identify the particular instance if you want to, but typically you will use your monitoring system to aggregate across instances.
Since gauges are instantaneous, the values between samples is unknown. Gauges are useful to determine current state, such as the size of queues. Sometimes answers to questions provided by gauges (e.g. active requests) might be answered by taking the difference in counters (e.g. completed requests - started requests).
Each microservice has a
controller.invocations metric used to
instrument API calls into it. Since this is a timer, in practice
this is broken out into two ‘controller.invocations__count’ and
These typically have the labels “controller”, “method”, “status”, “statusCode”, and “success”. Some microservices may add an additional label such as “account” or “application” depending on the nature of the microservices API.
These metrics will have several time series, such as those with the following tag bindings:
You can aggregate over the success tag to count successful calls vs failures, perhaps breaking out by controller and/or method to see where the failures were. You can break out by statusCode to see which controller and/or method the errors are coming from and so forth.
Different metrics have different tags depending on their concept and semantics. Some of these tags may be of more interest than others. In the case above, some of the tags are at different levels of abstraction and not actually independent. For example a 2xx status will always be success=true and a non-2xx status code will always be success=false. Which to use is a matter of convienence but given the status tag (which can distinguish 4xx from 5xx errors) the success tag does not add any additional time-series permutations since its value is not actually independent.
Each of the supplied monitoring solutions provides a set of dashboards tailored for that system. These are likely to evolve at different rates so are not completely analogous or consistent across systems and might not be completely consistent with the document. However, the gist and intent described here should still hold since the monitoring intent is the same across all the concrete systems.
As a rule of thumb, the dashboards currently prefer showing value differences (over rates) for 1-minute sliding windows. This might change in the future. Some of the caveats here are due to the choice to show values over rates, but at this time the values seem more meaningful than rates, particularly where there arent continuous streams of activity. Where latencies are shown, they are computed using the counters from the past minute.
Depending on the chart and underlying monitoring system, some charts show instantaneous value differences (between samples) while others show differences over a sliding window. The accuracy of the timeline may vary depending on the dashboard, but the underlying trends and relative signals over time will still be valid.
Types of Dashboards
There are several different dashboards. Each monitoring system has its own implementation of the dashboards. See the corresponding documentation for that system for more details or caveats.
Note: Some systems might have an earlier prototype “KitchenSinkDashboard” that has not yet been broken out into the individual dashboards. Most of the information is still there, just all in the one dashboard.
These dashboards are tailored for an individual microservice. As a rule of thumb they provide a system wide view of all replicas of a given microservice while also letting you isolate a particular instance. They show success/error counts and latencies for the different APIs the microservice offers as well as special metrics that are fundamental to the operation or responsibilities of that particular service.
Spinnaker <Provider> API
These dashboards are tailored for a particular cloud provider. They show a system level perspective of Spinnaker’s interaction with that provider. Depending on the provider, the dashboard details may vary. In general they offer a system wide view while also letting you isolate a particular instance and region, showing success/error counts and latencies for different resource interactions or individual operations. This provides visibility into what your deployment is doing and where any problems might be coming from.
The intent of this dashboard is provide the most essential or useful metrics to quickly suggest whether there are any issues and confirm Spinnaker is behaving normally. Your needs may vary so consult each of the other dashboards and consider refining your own. If you do, also consider sharing that back!