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AMS Prometheus

Amazon Managed Service for Prometheus (or AMS Prometheus) is a monitoring and alerting service that enables easy-to-use monitoring of containerized applications and infrastructure. It's scalable and you don't have to worry about hosting it yourself.

Scope of Support

Currently, Nobl9 integration with AMS Prometheus does not support IAM roles for bare EC2 instances: (IAM Roles | AWS Documentation).

Authentication

For more details, refer to Authentication for AMS Prometheus.

To set up the connection, AMS Prometheus requires users to enter the URL. To get the URL:

  1. Sign in to your AWS account.

  2. Click the AMS Prometheus in the Management & Governance panel:

    Image 1: Configuring authentication for AMS Prometheus (1)
  3. Click the All workspaces.

    Image 2: Configuring authentication for AMS Prometheus (2)
  4. Choose a relevant workspace:

    Image 3: Configuring authentication for AMS Prometheus (3)
  5. In the Summary screen, copy the Endpoint - query URL

    Image 4: Configuring authentication for AMS Prometheus (4)
warning

The url that you need is the Endpoint - query URL without the /api/v1/query string at the end of the URL.

Adding Amazon Managed Service for Prometheus as a Data Source in the UI

To add Amazon Managed Service for Prometheus as a data source in Nobl9 using the Agent connection method, follow these steps:

  1. Navigate to Integrations > Sources.
  2. Click the button.
  3. Click the relevant Source icon.
  4. Choose a relevant connection method (Agent or Direct), then configure the source as described below.

AMS Prometheus Agent

Agent Configuration in the UI

Follow the instructions below to create your AMS Prometheus agent connection:

  1. Add the URL to connect to your data source (mandatory).
    Refer to Authentication for AMS Prometheus for detailed instructions on how to get the URL.

  2. Enter a Region (mandatory).
    The list of supported Regions for AMS Prometheus is here.

  1. Select a Project.
    Specifying a Project is helpful when multiple users are spread across multiple teams or projects. When the Project field is left blank then object is assigned to project default.
  2. Enter a Display Name.
    You can enter a friendly name with spaces in this field.
  3. Enter a Name.
    The name is mandatory and can only contain lowercase, alphanumeric characters and dashes (for example, my-project-name). This field is populated automatically when you enter a display name, but you can edit the result.
  4. Enter a Description.
    Here you can add details such as who is responsible for the integration (team/owner) and the purpose of creating it.
  5. Enter a Maximum Period for Historical Data Retrieval.
    • This value defines how far back in the past your data will be retrieved.
    • The value for the Maximum period of data retrieval depends on the Source. Check the Replay documentation for details.
    • Entering a more extended Period might slow down the loading time when creating an SLO.
      • The value must be a positive integer.
  6. Enter a Default Period for Historical Data Retrieval.
    • It is the Period that will be used by the SLO connected to this data source.
    • The value must be a positive integer or 0.
    • By default, this value is set to 0. If you set it to >0, you will create an SLO with Replay.
  7. Click the Add Data Source button.

Agent Using CLI - YAML

The YAML for setting up an Agent connection to AMS Prometheus looks like this:

apiVersion: n9/v1alpha
kind: Agent
metadata:
name: amazon-prometheus-agent
displayName: AMS Prometheus Agent # optional
project: default
spec:
description: Agent settings for Amazon Managed Service for Prometheus datasource # optional
sourceOf:
- Metrics
amazonPrometheus:
url: https://aps-workspaces.eu-central-1.amazonaws.com/workspaces/some_workspace_id
region: eu-central-1
roleArn: arn:aws:iam::12345/role/n9-access # optional
historicalDataRetrieval:
maxDuration:
value: 30 # integer greater than or equal to 0
unit: Day # accepted values: Minute, Hour, Day
defaultDuration: # value must be less than or equal to value of maxDuration
value: 0 # integer greater than or equal to 0
unit: Day # accepted values: Minute, Hour, Day

Important notes:

warning

You can deploy only one Agent in one YAML file by using the sloctl apply command.

caution

The Nobl9 Agent makes 1 request to API per minute per unique query. Make sure that your AMS Prometheus server can handle additional traffic. For more details, go to AMP Quotas.

Deploying AMS Prometheus Agent

When you add the data source, Nobl9 automatically generates a Kubernetes configuration and a Docker command line for you to use to deploy the Agent. Both of these are available in the web UI, under the Agent Configuration section. Be sure to swap in your credentials.

If you use Kubernetes, you can apply the supplied YAML config file to a Kubernetes cluster to deploy the Agent. It will look something like this:

# DISCLAIMER: This deployment description contains only the fields necessary for the purpose of this demo.
# It is not a ready-to-apply k8s deployment description, and the client_id and client_secret are only exemplary values.

apiVersion: v1
kind: Secret
metadata:
name: nobl9-agent-nobl9-dev-awsprometheus-month-prom
namespace: default
type: Opaque
stringData:
client_id: "unique_client_id"
client_secret: "unique_client_secret"
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: nobl9-agent-nobl9-dev-awsprometheus-month-prom
namespace: default
spec:
replicas: 1
selector:
matchLabels:
nobl9-agent-name: "prom"
nobl9-agent-project: "awsprometheus-month"
nobl9-agent-organization: "nobl9-dev"
template:
metadata:
labels:
nobl9-agent-name: "amazon-prom"
nobl9-agent-project: "amazon-prometheus-month"
nobl9-agent-organization: "nobl9-dev"
spec:
containers:
- name: agent-container
image: nobl9/agent:latest
resources:
requests:
memory: "350Mi"
cpu: "0.1"
env:
- name: N9_CLIENT_ID
valueFrom:
secretKeyRef:
key: client_id
name: nobl9-agent-nobl9-dev-awsprometheus-month-prom
- name: N9_CLIENT_SECRET
valueFrom:
secretKeyRef:
key: client_secret
name: nobl9-agent-nobl9-dev-awsprometheus-month-prom
# The N9_METRICS_PORT is a variable specifying the port to which the /metrics and /health endpoints are exposed.
# The 9090 is the default value and can be changed.
# If you don’t want the metrics to be exposed, comment out or delete the N9_METRICS_PORT variable.
- name: N9_METRICS_PORT
value: "9090"
# N9_ALLOWED_URLS is an optional security parameter that limits the URLs that an Agent can query
# for metrics. URLs defined in the Nobl9 app are prefix-compared against the N9_ALLOWED_URLS list of
# comma separated URLs.
# - name: N9_ALLOWED_URLS
# value: "http://172.16.0.2/api/v1/query,http://172.16.0.3"

Creating SLOs with AMS Prometheus

Creating SLOs in the UI

Follow the instructions below to create your SLOs with AWS Prometheus in the UI:

  1. Navigate to Service Level Objectives.

  2. Click the button.
  3. In the SLO Wizard, select a Data Source for your SLO then specify the Metric. You can choose either a Threshold Metric, where a single time series is evaluated against a threshold, or a Ratio Metric, which allows you to enter two time series to compare (for example, a count of good requests and total requests).

    note

    For the Ratio Metric, you can choose the Data Count Method:

    • For the Non-incremental method, we expect it to be the components of the sum.
    • For the Incremental method, we expect the value of a metric to be the current sum of some numerator.

    For more information, refer to the SLO Calculations Guide.

  4. Enter a Query (for the Threshold metric) or Good Query, and Total Query (for the Ratio metric). The following are query examples:

    • Threshold metric for AMS Prometheus: Query: sum(changes(kube_pod_status_ready{condition="true"}[5m]))

    • Ratio metric for AMS Prometheus: Good Query: sum(server_requests{code=~"2xx|3xx",host=""}) Total Query: server_requests{code="total",host=""}

  5. In step 3, define a Time Window for the SLO.

  6. In step 4, specify the Error Budget Calculation Method and your Objective(s).

  7. In step 5, add a Name, Description, and other details about your SLO. You can also select Alert Policies and Labels on this screen.

  8. When you’re done, click Create SLO.

SLO using AMS Prometheus - YAML samples

Here's an example of Amazon Managed Service for Prometheus using rawMetric (Threshold metric):

apiVersion: n9/v1alpha
kind: SLO
metadata:
displayName: My AMS Prometheus SLO
name: my-awsprometheus-slo
project: my-awsprometheus-project
spec:
budgetingMethod: Occurrences
description: ""
indicator:
metricSource:
name: my-amsprometheus-source
service: my-service
objectives:
- target: 0.8
op: lte
rawMetric:
query:
amazonPrometheus:
promql: myapp_server_requestMsec{host="*",job="nginx"}
displayName: average
value: 200
- target: 0.5
op: lte
rawMetric:
query:
amazonPrometheus:
promql: myapp_server_requestMsec{host="*",job="nginx"}
displayName: so-so
value: 150
timeWindows:
- calendar:
startTime: "2020-11-14 11:00:00"
timeZone: Etc/UTC
count: 1
isRolling: false
unit: Day

Important notes:

  • Specification for metric from Prometheus has one mandatory field:

    • promql – a Prometheus query in the PromQL (Prometheus Query Language) that allows the user to select and aggregate time-series data in real-time.

Querying the AMS Prometheus Server

Nobl9 Agent makes 1 request to API per minute per unique query. Make sure that your Prometheus server can handle additional traffic. For more details on Amazon service quota, go to Service Quotas | Amazon Managed Service for Prometheus Documentation.

Service Quotas | Amazon Managed Service for Prometheus Documentation

Credential Types | Amazon Managed Service for Prometheus Documentation

Specifying Credentials | Amazon Managed Service for Prometheus Documentation

AWS Service Endpoints | Amazon Managed Service for Prometheus Documentation

Agent Metrics | Nobl9 Documentation

Creating SLOs via Terraform | Nobl9 Terraform Documentation

Creating Agents via Terraform | Nobl9 Terraform Documentation