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ThousandEyes

ThousandEyes monitors the performance of both local and wide-area networks. ThousandEyes combines Internet and WAN visibility, browser synthetics, end-user monitoring, and Internet Insights to deliver a holistic view of your hybrid digital ecosystem – across cloud, SaaS, and the Internet. It's a SaaS-based tool that helps troubleshoot application delivery and maps Internet performance.

Scope of Support

Currently, Nobl9 integration with ThousandEyes supports the following metrics:

  • Network Latency - is the interval time (in milliseconds) from sending a network packet to when the test-assigned agent receives the response.

  • Network Loss - is the total loss of the network packet (in percents).

  • Page Load - is the interval of time (in milliseconds) from the load event to the point when the website is loaded.

  • DOM Load Time - is the interval of time (in milliseconds) required for the browser to build the website’s Document Object Model (DOM)

  • HTTP Server Response - is the interval between the beginning of the request and the web server successfully sending the first byte of the response to the client.

  • HTTP Server Availability - is the availability for a given Agent. For HTTP Server Availability, Nobl9 receives status codes of an http response, for example, 200, 201, 400, or 501.

    warning

    When there are multiple ThousandEyes Agents, Nobl9 can receive multiple HTTP response status codes in a single request. In this situation, Nobl9 ingests only the highest status code.

  • HTTP Server Throughput - is the Wire Size divided by the Receive Time (in bits per second). For more information, refer to the ThousandEyes Documentation.

Each measurement is obtained per ThousandEyes test interval.

The following table is an overview of the ThousandEyes metric and corresponding Nobl9 values:

MetricNobl9 Value
Network Latencynet-latency
Network lossnet-loss
Page loadweb-page-load
DOM load timeweb-dom-load
HTTP Server Responsehttp-response-time
HTTP Server Availabilityhttp-server-availability
HTTP Server Throughputhttp-server-throughput

Authentication

Nobl9 requires OAUTH_BEARER_TOKEN to communicate with ThousandEyes.

To get the OAUTH_BEARER_TOKEN:

  1. Log in to your ThousandEyes account.

  2. Navigate to Account Settings.

  3. Select Users and Roles.

  4. Navigate to the bottom of the page and you will see User API Tokens.

  5. Select OAuth Bearer Token.
    Currently, Nobl9 only supports OAUTH_BEARER_TOKEN.

Adding ThousandEyes as a Data Source in the UI

To add ThousandEyes as a data source in Nobl9 using the Agent or Direct 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.

ThousandEyes Direct

Direct Configuration in the UI

Direct configuration for ThousandEyes requires users to enter their credentials which Nobl9 stores safely. Follow these steps to set up a direct configuration:

  1. Enter your OAuth Bearer Token.
    Refer to the Authentication section above for more details.
  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. Click the Add Data Source button.

ThousandEyes Agent

Agent Configuration in the UI

Follow the instructions below to configure your ThousandEyes Agent. Refer to the section above for the description of the fields.

  1. Enter a Project.
  2. Enter a Display Name.
  3. Enter a Name.
  4. Create a Description.
  5. Click the Add Data Source button.

Agent Using CLI - YAML

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

apiVersion: n9/v1alpha
kind: Agent
metadata:
name: thousandeyes
displayName: ThousandEyes # optional
project: thousandeyes
spec:
sourceOf:
- Metrics
- Services
thousandEyes: {}
warning

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

Deploying ThousandEyes 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 (e.g., replace <OAUTH_BEARER_TOKEN> with your actual token).

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-thousandeyes-secret
namespace: default
type: Opaque
stringData:
thousandeyes_oauth_bearer_token: "<THOUSANDEYES_OAUTH_BEARER_TOKEN>"
client_id: "unique_client_id"
client_secret: "unique_client_secret"
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: nobl9-agent-nobl9-dev-thousandeyes-agent-deployment
namespace: default
spec:
replicas: 1
selector:
matchLabels:
nobl9-agent-name: "my-unique-agent-name"
nobl9-agent-project: "thousandeyes"
nobl9-agent-organization: "nobl9-dev"
template:
metadata:
labels:
nobl9-agent-name: "my-unique-agent-name"
nobl9-agent-project: "thousandeyes"
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-thousandeyes-secret
- name: N9_CLIENT_SECRET
valueFrom:
secretKeyRef:
key: client_secret
name: nobl9-agent-nobl9-dev-thousandeyes-secret
- name: THOUSANDEYES_OAUTH_BEARER_TOKEN
valueFrom:
secretKeyRef:
key: thousandeyes_oauth_bearer_token
name: nobl9-agent-nobl9-dev-thousandeyes-secret
# 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"

Creating SLOs with ThousandEyes

Creating SLOs in the UI

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

  1. Navigate to Service Level Objectives.

  2. Click the button.
  3. In step 1 of the SLO wizard, select the Service the SLO will be associated with.

  4. In step 2, select ThousandEyes as the Data Source for your SLO, then specify the Metric. For ThousandEyes you can use only a Threshold Metric, where a single time series is evaluated against a threshold, The ThousandEyes metric specification has two fields: testID and testType.

    • Enter a testID. It is an ID of the test configured in ThousandEyes. It is a mandatory field, and it must be a positive number.

    • Choose a testType. It is a ThousandEyes metric. You can choose between the following ThousandEyes metrics supported by Nobl9 (refer to the Scope of Support section for more details):

      • Network Latency

      • Network Loss

      • Page Load

      • DOM Load Time

      • HTTP Server Response

      • HTTP Server Availability

      • HTTP Server Throughput

  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 ThousandEyes - YAML samples

- apiVersion: n9/v1alpha
kind: SLO
metadata:
name: my-thousandeyes-web-page-load-time-rolling-occurrences
project: thousandeyes
spec:
service: thousandeyes
indicator:
metricSource:
name: thousandeyes
timeWindows:
- unit: Hour
count: 1
isRolling: true
budgetingMethod: Occurrences
objectives:
- displayName: Good
op: lte
rawMetric:
query:
thousandEyes:
testID: 2280492
testType: web-page-load
value: 75
target: 0.90

Important notes:

The ThousandEyes metric specification expects two fields: testID and testType.

  • testID is an ID of the test configured in ThousandEyes. It is a mandatory field, and it must be a positive number.

  • testType is an end-to-end ThousandEyes metric. It is a string field. Enter one of the below ThousandEyes metrics supported by Nobl9 (for more details, refer to the Scope of Support section above).

    • net-latency

    • net-loss

    • web-page-load

    • web-dom-load

    • http-response-time

    • http-server-availability

    • http-server-throughput

When the testType field is missing in the ThousandEyes YAML configuration, net-latency value is assumed.

caution

If you want to apply http-server-availability and http-server-throughput as testType, update your Agent to the 0.52.0 version.

Querying the ThousandEyes Server

Nobl9 queries the ThousandEyes instance once every minute. If the Agent doesn’t collect any data in a given minute, the next query will be extended to the past two minutes.

ThousandEyes API Rate Limits

The ThousandEyes API throttles API requests using a 240 request-per-minute (per organization) limit with each page having max 1000 elements included.

Test Data | ThousandEyes Documentation

ThousandEyes Knowledge Base | ThousandEyes Documentation

ThousandEyes End-to-End Metrics | ThousandEyes Documentation

Agent Metrics | Nobl9 Documentation

Creating SLOs via Terraform | Nobl9 Terraform Documentation

Creating Agents via Terraform | Nobl9 Terraform Documentation