Analysis & Progressive Delivery¶

Argo Rollouts provides several ways to perform analysis to drive progressive delivery. This document describes how to achieve various forms of progressive delivery, varying the point in time analysis is performed, it's frequency, and occurrence.

Custom Resource Definitions¶

CRD	Description
Rollout	A `Rollout` acts as a drop-in replacement for a Deployment resource. It provides additional blueGreen and canary update strategies. These strategies can create AnalysisRuns and Experiments during the update, which will progress the update, or abort it.
AnalysisTemplate	An `AnalysisTemplate` is a template spec which defines how to perform a canary analysis, such as the metrics which it should perform, its frequency, and the values which are considered successful or failed. AnalysisTemplates may be parameterized with inputs values.
ClusterAnalysisTemplate	A `ClusterAnalysisTemplate` is like an `AnalysisTemplate`, but it is not limited to its namespace. It can be used by any `Rollout` throughout the cluster.
AnalysisRun	An `AnalysisRun` is an instantiation of an `AnalysisTemplate`. AnalysisRuns are like Jobs in that they eventually complete. Completed runs are considered Successful, Failed, or Inconclusive, and the result of the run affect if the Rollout's update will continue, abort, or pause, respectively.
Experiment	An `Experiment` is limited run of one or more ReplicaSets for the purposes of analysis. Experiments typically run for a pre-determined duration, but can also run indefinitely until stopped. Experiments may reference an `AnalysisTemplate` to run during or after the experiment. The canonical use case for an Experiment is to start a baseline and canary deployment in parallel, and compare the metrics produced by the baseline and canary pods for an equal comparison.

Background Analysis¶

Analysis can be run in the background -- while the canary is progressing through its rollout steps.

The following example gradually increments the canary weight by 20% every 10 minutes until it reaches 100%. In the background, an AnalysisRun is started based on the AnalysisTemplate named success-rate. The success-rate template queries a prometheus server, measuring the HTTP success rates at 5 minute intervals/samples. It has no end time, and continues until stopped or failed. If the metric is measured to be less than 95%, and there are three such measurements, the analysis is considered Failed. The failed analysis causes the Rollout to abort, setting the canary weight back to zero, and the Rollout would be considered in a Degraded. Otherwise, if the rollout completes all of its canary steps, the rollout is considered successful and the analysis run is stopped by the controller.

This example highlights:

Background analysis style of progressive delivery
Using a Prometheus query to perform a measurement
The ability to parameterize the analysis
Delay starting the analysis run until step 3 (Set Weight 40%)

Rollout

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    canary:
      analysis:
        templates:
        - templateName: success-rate
        startingStep: 2 # delay starting analysis run until setWeight: 40%
        args:
        - name: service-name
          value: guestbook-svc.default.svc.cluster.local
      steps:
      - setWeight: 20
      - pause: {duration: 10m}
      - setWeight: 40
      - pause: {duration: 10m}
      - setWeight: 60
      - pause: {duration: 10m}
      - setWeight: 80
      - pause: {duration: 10m}

AnalysisTemplate

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  args:
  - name: service-name
  metrics:
  - name: success-rate
    interval: 5m
    # NOTE: prometheus queries return results in the form of a vector.
    # So it is common to access the index 0 of the returned array to obtain the value
    successCondition: result[0] >= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code!~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))

Inline Analysis¶

Analysis can also be performed as a rollout step as an inline "analysis" step. When analysis is performed "inlined," an AnalysisRun is started when the step is reached, and blocks the rollout until the run is completed. The success or failure of the analysis run decides if the rollout will proceed to the next step, or abort the rollout completely.

This example sets the canary weight to 20%, pauses for 5 minutes, then runs an analysis. If the analysis was successful, continues with rollout, otherwise aborts.

This example demonstrates:

The ability to invoke an analysis in-line as part of steps

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    canary:
      steps:
      - setWeight: 20
      - pause: {duration: 5m}
      - analysis:
          templates:
          - templateName: success-rate
          args:
          - name: service-name
            value: guestbook-svc.default.svc.cluster.local

In this example, the AnalysisTemplate is identical to the background analysis example, but since no interval is specified, the analysis will perform a single measurement and complete.

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  args:
  - name: service-name
  - name: prometheus-port
    value: 9090
  metrics:
  - name: success-rate
    successCondition: result[0] >= 0.95
    provider:
      prometheus:
        address: "http://prometheus.example.com:{{args.prometheus-port}}"
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code!~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))

Multiple measurements can be performed over a longer duration period, by specifying the count and interval fields:

  metrics:
  - name: success-rate
    successCondition: result[0] >= 0.95
    interval: 60s
    count: 5
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: ...

ClusterAnalysisTemplates¶

Important

Available since v0.9.0

A Rollout can reference a Cluster scoped AnalysisTemplate called a ClusterAnalysisTemplate. This can be useful when you want to share an AnalysisTemplate across multiple Rollouts; in different namespaces, and avoid duplicating the same template in every namespace. Use the field clusterScope: true to reference a ClusterAnalysisTemplate instead of an AnalysisTemplate.

Rollout

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    canary:
      steps:
      - setWeight: 20
      - pause: {duration: 5m}
      - analysis:
          templates:
          - templateName: success-rate
            clusterScope: true
          args:
          - name: service-name
            value: guestbook-svc.default.svc.cluster.local

ClusterAnalysisTemplate

apiVersion: argoproj.io/v1alpha1
kind: ClusterAnalysisTemplate
metadata:
  name: success-rate
spec:
  args:
  - name: service-name
  - name: prometheus-port
    value: 9090
  metrics:
  - name: success-rate
    successCondition: result[0] >= 0.95
    provider:
      prometheus:
        address: "http://prometheus.example.com:{{args.prometheus-port}}"
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code!~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))

Note

The resulting AnalysisRun will still run in the namespace of the Rollout

Analysis with Multiple Templates¶

A Rollout can reference multiple AnalysisTemplates when constructing an AnalysisRun. This allows users to compose analysis from multiple AnalysisTemplates. If multiple templates are referenced, then the controller will merge the templates together. The controller combines the metrics and args fields of all the templates.

Rollout

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    canary:
      analysis:
        templates:
        - templateName: success-rate
        - templateName: error-rate
        args:
        - name: service-name
          value: guestbook-svc.default.svc.cluster.local

AnalysisTemplate

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: success-rate
spec:
  args:
  - name: service-name
  metrics:
  - name: success-rate
    interval: 5m
    successCondition: result[0] >= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code!~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))
---
apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: error-rate
spec:
  args:
  - name: service-name
  metrics:
  - name: error-rate
    interval: 5m
    successCondition: result[0] <= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code=~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))

AnalysisRun

# NOTE: Generated AnalysisRun from the multiple templates
apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
metadata:
  name: guestbook-CurrentPodHash-multiple-templates
spec:
  args:
  - name: service-name
    value: guestbook-svc.default.svc.cluster.local
  metrics:
  - name: success-rate
    interval: 5m
    successCondition: result[0] >= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code!~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))
  - name: error-rate
    interval: 5m
    successCondition: result[0] <= 0.95
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code=~"5.*"}[5m]
          )) /
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}"}[5m]
          ))

Note

The controller will error when merging the templates if:

Multiple metrics in the templates have the same name
Two arguments with the same name both have values

Analysis Template Arguments¶

AnalysisTemplates may declare a set of arguments that can be passed by Rollouts. The args can then be used as in metrics configuration and are resolved at the time the AnalysisRun is created. Argument placeholders are defined as {{ args.<name> }}.

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
metadata:
  name: args-example
spec:
  args:
  # required
  - name: service-name
  - name: stable-hash
  - name: latest-hash
  # optional
  - name: api-url
    value: http://example/measure
  # from secret
  - name: api-token
    valueFrom:
      secretKeyRef:
        name: token-secret
        key: apiToken
  metrics:
  - name: webmetric
    successCondition: result == 'true'
    provider:
      web:
        # placeholders are resolved when an AnalysisRun is created
        url: "{{ args.api-url }}?service={{ args.service-name }}"
        headers:
          - key: Authorization
            value: "Bearer {{ args.api-token }}"
        jsonPath: "{$.results.ok}"

Analysis arguments defined in a Rollout are merged with the args from the AnalysisTemplate when the AnalysisRun is created.

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    canary:
      analysis:
        templates:
        - templateName: args-example
        args:
        # required value
        - name: service-name
          value: guestbook-svc.default.svc.cluster.local
        # override default value
        - name: api-url
          value: http://other-api
        # pod template hash from the stable ReplicaSet
        - name: stable-hash
          valueFrom:
            podTemplateHashValue: Stable
        # pod template hash from the latest ReplicaSet
        - name: latest-hash
          valueFrom:
            podTemplateHashValue: Latest

Analysis arguments also support valueFrom for reading metadata fields and passing them as arguments to AnalysisTemplate. An example would be to reference metadata labels like env and region and passing them along to AnalysisTemplate.

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
  labels:
    appType: demo-app
    buildType: nginx-app
    ...
    env: dev
    region: us-west-2
spec:
...
  strategy:
    canary:
      analysis:
        templates:
        - templateName: args-example
        args:
        ...
        - name: env
          valueFrom:
            fieldRef:
              fieldPath: metadata.labels['env']
        # region where this app is deployed
        - name: region
          valueFrom:
            fieldRef:
              fieldPath: metadata.labels['region']

BlueGreen Pre Promotion Analysis¶

A Rollout using the BlueGreen strategy can launch an AnalysisRun before it switches traffic to the new version using pre-promotion. This can be used to block the Service selector switch until the AnalysisRun finishes successfully. The success or failure of the AnalysisRun decides if the Rollout switches traffic, or abort the Rollout completely.

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    blueGreen:
      activeService: active-svc
      previewService: preview-svc
      prePromotionAnalysis:
        templates:
        - templateName: smoke-tests
        args:
        - name: service-name
          value: preview-svc.default.svc.cluster.local

In this example, the Rollout creates a pre-promotion AnalysisRun once the new ReplicaSet is fully available. The Rollout will not switch traffic to the new version until the analysis run finishes successfully.

Note: if theautoPromotionSeconds field is specified and the Rollout has waited auto promotion seconds amount of time, the Rollout marks the AnalysisRun successful and switches the traffic to a new version automatically. If the AnalysisRun completes before then, the Rollout will not create another AnalysisRun and wait out the rest of the autoPromotionSeconds.

BlueGreen Post Promotion Analysis¶

A Rollout using a BlueGreen strategy can launch an analysis run after the traffic switch to the new version using post-promotion analysis. If post-promotion Analysis fails or errors, the Rollout enters an aborted state and switches traffic back to the previous stable Replicaset. When post-analysis is Successful, the Rollout is considered fully promoted and the new ReplicaSet will be marked as stable. The old ReplicaSet will then be scaled down according to scaleDownDelaySeconds (default 30 seconds).

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
...
  strategy:
    blueGreen:
      activeService: active-svc
      previewService: preview-svc
      scaleDownDelaySeconds: 600 # 10 minutes
      postPromotionAnalysis:
        templates:
        - templateName: smoke-tests
        args:
        - name: service-name
          value: preview-svc.default.svc.cluster.local

Failure Conditions¶

failureCondition can be used to cause an analysis run to fail. The following example continually polls a prometheus server to get the total number of errors every 5 minutes, causing the analysis run to fail if 10 or more errors were encountered.

  metrics:
  - name: total-errors
    interval: 5m
    failureCondition: result[0] >= 10
    failureLimit: 3
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: |
          sum(irate(
            istio_requests_total{reporter="source",destination_service=~"{{args.service-name}}",response_code~"5.*"}[5m]
          ))

Inconclusive Runs¶

Analysis runs can also be considered Inconclusive, which indicates the run was neither successful, nor failed. Inconclusive runs causes a rollout to become paused at its current step. Manual intervention is then needed to either resume the rollout, or abort. One example of how analysis runs could become Inconclusive, is when a metric defines no success or failure conditions.

  metrics:
  - name: my-query
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: ...

Inconclusive analysis runs might also happen when both success and failure conditions are specified, but the measurement value did not meet either condition.

  metrics:
  - name: success-rate
    successCondition: result[0] >= 0.90
    failureCondition: result[0] < 0.50
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: ...

A use case for having Inconclusive analysis runs are to enable Argo Rollouts to automate the execution of analysis runs, and collect the measurement, but still allow human judgement to decide whether or not measurement value is acceptable and decide to proceed or abort.

Delay Analysis Runs¶

If the analysis run does not need to start immediately (i.e give the metric provider time to collect metrics on the canary version), Analysis Runs can delay the specific metric analysis. Each metric can be configured to have a different delay. In additional to the metric specific delays, the rollouts with background analysis can delay creating an analysis run until a certain step is reached

Delaying a specific analysis metric:

  metrics:
  - name: success-rate
    # Do not start this analysis until 5 minutes after the analysis run starts
    initialDelay: 5m
    successCondition: result[0] >= 0.90
    provider:
      prometheus:
        address: http://prometheus.example.com:9090
        query: ...

Delaying starting background analysis run until step 3 (Set Weight 40%):

apiVersion: argoproj.io/v1alpha1
kind: Rollout
metadata:
  name: guestbook
spec:
  strategy:
    canary:
      analysis:
        templates:
        - templateName: success-rate
        startingStep: 2
      steps:
      - setWeight: 20
      - pause: {duration: 10m}
      - setWeight: 40
      - pause: {duration: 10m}

Referencing Secrets¶

AnalysisTemplates and AnalysisRuns can reference secret objects in .spec.args. This allows users to securely pass authentication information to Metric Providers, like login credentials or API tokens.

An AnalysisRun can only reference secrets from the same namespace as it's running in. This is only relevant for AnalysisRuns, since AnalysisTemplates do not resolve the secret.

In the following example, an AnalysisTemplate references an API token and passes it to a Web metric provider.

This example demonstrates:

The ability to reference a secret in the AnalysisTemplate .spec.args
The ability to pass secret arguments to Metric Providers

apiVersion: argoproj.io/v1alpha1
kind: AnalysisTemplate
spec:
  args:
  - name: api-token
    valueFrom:
      secretKeyRef:
        name: token-secret
        key: apiToken
  metrics:
  - name: webmetric
    provider:
      web:
        headers:
        - key: Authorization
          value: "Bearer {{ args.api-token }}"

Handling Metric Results¶

NaN and Infinity¶

Metric providers can sometimes return values of NaN (not a number) and infinity. Users can edit the successCondition and failureCondition fields to handle these cases accordingly.

Here are three examples where a metric result of NaN is considered successful, inconclusive and failed respectively.

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: isNaN(result) || result >= 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-02-10T00:15:26Z"
      phase: Successful
      startedAt: "2021-02-10T00:15:26Z"
      value: NaN
    name: success-rate
    phase: Successful
    successful: 1
  phase: Successful
  startedAt: "2021-02-10T00:15:26Z"

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: result >= 0.95
    failureCondition: result < 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-02-10T00:15:26Z"
      phase: Inconclusive
      startedAt: "2021-02-10T00:15:26Z"
      value: NaN
    name: success-rate
    phase: Inconclusive
    successful: 1
  phase: Inconclusive
  startedAt: "2021-02-10T00:15:26Z"

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: result >= 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-02-10T00:15:26Z"
      phase: Failed
      startedAt: "2021-02-10T00:15:26Z"
      value: NaN
    name: success-rate
    phase: Failed
    successful: 1
  phase: Failed
  startedAt: "2021-02-10T00:15:26Z"

Here are two examples where a metric result of infinity is considered successful and failed respectively.

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: result >= 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-02-10T00:15:26Z"
      phase: Successful
      startedAt: "2021-02-10T00:15:26Z"
      value: +Inf
    name: success-rate
    phase: Successful
    successful: 1
  phase: Successful
  startedAt: "2021-02-10T00:15:26Z"

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    failureCondition: isInf(result)
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-02-10T00:15:26Z"
      phase: Failed
      startedAt: "2021-02-10T00:15:26Z"
      value: +Inf
    name: success-rate
    phase: Failed
    successful: 1
  phase: Failed
  startedAt: "2021-02-10T00:15:26Z"

Empty array¶

Prometheus¶

Metric providers can sometimes return empty array, e.g., no data returned from prometheus query.

Here are two examples where a metric result of empty array is considered successful and failed respectively.

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: len(result) == 0 || result >= 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-09-08T19:15:49Z"
      phase: Successful
      startedAt: "2021-09-08T19:15:49Z"
      value: '[]'
    name: success-rate
    phase: Successful
    successful: 1
  phase: Successful
  startedAt:  "2021-09-08T19:15:49Z"

apiVersion: argoproj.io/v1alpha1
kind: AnalysisRun
  ...
    successCondition: len(result) > 0 && result >= 0.95
status:
  metricResults:
  - count: 1
    measurements:
    - finishedAt: "2021-09-08T19:19:44Z"
      phase: Failed
      startedAt: "2021-09-08T19:19:44Z"
      value: '[]'
    name: success-rate
    phase: Failed
    successful: 1
  phase: Failed
  startedAt: "2021-09-08T19:19:44Z"

Datadog¶

Datadog queries can return empty results if the query takes place during a time interval with no metrics. The Datadog provider will return a nil value yielding an error during the evaluation phase like:

invalid operation: < (mismatched types <nil> and float64)

However, empty query results yielding a nil value can be handled using the default() function. Here is a succeeding example using the default() function:

successCondition: default(result, 0) < 0.05