Getting Started with Observability Pipelines

Overview

The Observability Pipelines Worker can collect, process, and route logs and metrics from any source to any destination. Using Datadog, you can build and manage all of your Observability Pipelines Worker deployments at scale.

This guide walks you through deploying the Worker in your common tools cluster and configuring the Datadog Agent to send logs and metrics to the Worker.

Assumptions

• You are already using Datadog and want to use Observability Pipelines.
• Your services are deployed to a Kubernetes cluster in Amazon Elastic Kubernetes Service (EKS), Azure Kubernetes Service (AKS), or Google Kubernetes Engine (GKE).
• You have administrative access to the clusters where the Observability Pipelines Worker is going to be deployed, as well as to the workloads that are going to be aggregated.
• You have a common tools or security cluster for your environment to which all other clusters are connected.

Prerequisites

Before installing, make sure you have:

• A valid Datadog API key.
• An Observability Pipelines Configuration ID.

You can generate both of these in Observability Pipelines.

To run the Worker on your Kubernetes nodes, you need a minimum of two nodes with one CPU and 512MB RAM available. Datadog recommends creating a separate node pool for the Workers, which is also the recommended configuration for production deployments.

Provider-specific requirements

Installing the Observability Pipelines Worker

Download the Helm chart

helm repo add datadog https://helm.datadoghq.com

helm repo update

helm update --install \
    opw datadog/observability-pipelines-worker \
    -f aws_eks.yaml

helm repo add datadog https://helm.datadoghq.com

helm repo update

helm update --install \
  opw datadog/observability-pipelines-worker \
  -f azure_aks.yaml

helm repo add datadog https://helm.datadoghq.com

helm repo update

helm update --install \
  opw datadog/observability-pipelines-worker \
  -f google_gke.yaml

Load balancing

Use the load balancers provided by your cloud provider. They adjust based on autoscaling events that the default Helm setup is configured for. The load balancers are internal-facing, so they are only accessible inside your network.

Use the load balancer URL given to you by Helm when you configure the Datadog Agent.

Cross-availability-zone load balancing

The provided Helm configuration tries to simplify load balancing, but you must take into consideration the potential price implications of cross-AZ traffic. Wherever possible, the samples try to avoid creating situations where multiple cross-AZ hops can happen.

service.beta.kubernetes.io/aws-load-balancer-attributes: load_balancing.cross_zone.enabled=true

Buffering

Observability Pipelines includes multiple buffering strategies that allow you to increase the resilience of your cluster to downstream faults. The provided sample configurations use disk buffers, the capacities of which are rated for approximately 10 minutes of data at 10Mbps/core for Observability Pipelines deployments. That is often enough time for transient issues to resolve themselves, or for incident responders to decide what needs to be done with the observability data.

Connect the Agent and the Worker

To send Datadog Agent logs and metrics to the Observability Pipelines Worker, update your agent configuration with the following:

vector:
  logs.enabled: true
  # Adjust protocol to https if TLS/SSL is enabled on the Observability Pipelines Worker
  logs.url: "http://<OPW_HOST>:8282"
  metrics.enabled: true
  # Adjust protocol to https if TLS/SSL is enabled on the Observability Pipelines Worker
  metrics.url: "http://<OPW_HOST>:8282"

OPW_HOST is the EXTERNAL-IP of the load balancer you set up earlier. You can retrieve it by running the following command:

kubectl get svc opw-observability-pipelines-worker

At this point, your observability data should be going to the Worker and is available for data processing. The next section goes through what processing is included by default and the additional options that are available.

Working with data

The Helm chart provided has example processing steps that demonstrate Observability Pipelines tools and ensures that data sent to Datadog is in the correct format.

Processing logs

The provided logs pipeline does the following:

• Tag logs coming through the Observability Pipelines Worker. This helps determine what traffic still needs to be shifted over to the Worker as you update your clusters. These tags also show you how logs are being routed through the load balancer, in case there are imbalances.
• Correct the status of logs coming through the Worker. Due to how the Datadog Agent collects logs from containers, the provided .status attribute does not properly reflect the actual level of the message. It is removed to prevent issues with parsing rules in the backend, where logs are received from the Worker.

The following are two important components in the example configuration:

• logs_parse_ddtags: Parses the tags that are stored in a string into structured data.
• logs_finish_ddtags: Re-encodes the tags so that it is in the format as how the Datadog Agent would send it.

Internally, the Datadog Agent represents log tags as a CSV in a single string. To effectively manipulate these tags, they must be parsed, modified, and then re-encoded before they are sent to the ingest endpoint. These steps are written to automatically perform those actions for you. Any modifications you make to the pipeline, especially for manipulating tags, should be in between these two steps.

Processing metrics

The provided metrics pipeline does not require additional parsing and re-encoding steps. Similar to the logs pipeline, it tags incoming metrics for traffic accounting purposes. Due to the additional cardinality, this may have cost implications for custom metrics.

At this point, your environment is configured for Observability Pipelines with data flowing through it. Further configuration is likely required for your specific use cases, but the tools provided gives you a starting point.

Further reading

Additional helpful documentation, links, and articles:

Production deployment overview for the Observability Pipelines WorkerDOCUMENTATION Safe and Secure Local Processing with Observability PipelinesLEARNING CENTER