HyperDX

This step is optional if you have the OpenTelemetry Collector already running.

For detailed installation instructions for the OpenTelemetry Collector , please refer to the OpenTelemetry Collector Documentation. This guide provides comprehensive steps to get you up and running with the Collector on various platforms.

1. Configure HTTP Receiver: In the receivers section of your OpenTelemetry Collector config, ensure the http receiver is set with endpoint: 0.0.0.0:4318.

   Copy

   receivers:
     otlp:
       protocols:
         http:
           endpoint: 0.0.0.0:4318
   

2. Define Exporters: Add otlp exporter to export metrics and traces to HyperDX.

   Copy

   exporters:
     # HTTP setup
     otlphttp/hdx:
       endpoint: 'https://in-otel.hyperdx.io'
       headers:
       authorization: YOUR_HYPERDX_API_KEY_HERE
       compression: gzip
   
     # gRPC setup (alternative)
     otlp/hdx:
       endpoint: 'in-otel.hyperdx.io:4317'
       headers:
       authorization: YOUR_HYPERDX_API_KEY_HERE
       compression: gzip
   

   Replace:

   1. YOUR_HYPERDX_API_KEY_HERE with the your HyperDX API Key.
      • Example - x6xx7265-43x3-476x-1112-x9x52x29xxxx

3. Assign Exporters to Pipelines: Link otlphttp/hdx to service.pipelines.traces and service.pipelines.metrics for data export.

   Copy

   service:
     pipelines:
       traces:
         receivers: [ otlp ]
         exporters: [ otlphttp/hdx ]
       metrics:
         receivers: [ otlp ]
         exporters: [ otlphttp/hdx ]
   

Complete Configuration Example

import openlit

openlit.init(
  otlp_endpoint="YOUR_OTELCOL_URL:4318", 
)

import openlit

openlit.init(
  otlp_endpoint="YOUR_OTELCOL_URL:4318", 
)

import openlit

openlit.init()

export OTEL_EXPORTER_OTLP_ENDPOINT = "YOUR_OTELCOL_URL:4318"

Refer to the OpenLIT Python SDK repository for more advanced configurations and use cases.

You can directly import a pre-built dashboard using this URL: here

This is an unsaved dashboard URL. When you click on it, the dashboard will open in your own HyperDX instance. You can then choose to save it, and it will be added to your own HyperDX instance.