Gateway

The gateway collector deployment pattern consists of applications (or other collectors) sending telemetry signals to a single OTLP endpoint provided by one or more collector instances running as a standalone service (for example, a deployment in Kubernetes), typically per cluster, per data center or per region.

In the general case you can use an out-of-the-box load balancer to distribute the load amongst the collectors:

For use cases where the processing of the telemetry data processing has to happen in a specific collector, you would use a two-tiered setup with a collector that has a pipeline configured with the Trace ID/Service-name aware load-balancing exporter in the first tier and the collectors handling the scale out in the second tier. For example, you will need to use the load-balancing exporter when using the Tail Sampling processor so that all spans for a given trace reach the same collector instance where the tail sampling policy is applied.

Let’s have a look at such a case where we are using the load-balancing exporter:

1. In the app, the SDK is configured to send OTLP data to a central location.
2. A collector configured using the load-balancing exporter that distributes signals to a group of collectors.
3. The collectors are configured to send telemetry data to one or more backends.

Example

For a concrete example of the centralized collector deployment pattern we first need to have a closer look at the load-balancing exporter. It has two main configuration fields:

• The resolver, which determines where to find the downstream collectors (or: backends). If you use the static sub-key here, you will have to manually enumerate the collector URLs. The other supported resolver is the DNS resolver which will periodically check for updates and resolve IP addresses. For this resolver type, the hostname sub-key specifies the hostname to query in order to obtain the list of IP addresses.
• With the routing_key field you tell the load-balancing exporter to route spans to specific downstream collectors. If you set this field to traceID (default) then the Load-balancing exporter exports spans based on their traceID. Otherwise, if you use service as the value for routing_key, it exports spans based on their service name which is useful when using connectors like the Span Metrics connector, so all spans of a service will be send to the same downstream collector for metric collection, guaranteeting accurate aggregations.

The first-tier collector servicing the OTLP endpoint would be configured as shown below:

• Static
• DNS
• DNS with service

receivers:
  otlp:
    protocols:
      grpc:

exporters:
  loadbalancing:
    protocol:
      otlp:
        insecure: true
    resolver:
      static:
        hostnames:
          - collector-1.example.com:4317
          - collector-2.example.com:5317
          - collector-3.example.com

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [loadbalancing]

receivers:
  otlp:
    protocols:
      grpc:

exporters:
  loadbalancing:
    protocol:
      otlp:
        insecure: true
    resolver:
      dns:
        hostname: collectors.example.com

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [loadbalancing]

receivers:
  otlp:
    protocols:
      grpc:

exporters:
  loadbalancing:
    routing_key: "service"
    protocol:
      otlp:
        insecure: true
    resolver:
      dns:
        hostname: collectors.example.com
        port: 5317

service:
  pipelines:
    traces:
      receivers: [otlp]
      exporters: [loadbalancing]

The load-balancing exporter emits metrics including otelcol_loadbalancer_num_backends and otelcol_loadbalancer_backend_latency that you can use for health and performance monitoring of the OTLP endpoint collector.

Tradeoffs

Pros:

• Separation of concerns such as centrally managed credentials
• Centralized policy management (for example, filtering certain logs or sampling)

Cons:

• It’s one more thing to maintain and that can fail (complexity)
• Added latency in case of cascaded collectors
• Higher overall resource usage (costs)