---
title: Collector Data Flow Dashboard
---

Monitoring data flow through the OpenTelemetry Collector is crucial for several
reasons. Gaining a macro-level perspective on incoming data, such as sample
counts and cardinality, is essential for comprehending the collector's internal
dynamics. However, when delving into the details, the interconnections can
become complex. The Collector Data Flow Dashboard aims to demonstrate the
capabilities of the OpenTelemetry demo application, offering a solid foundation
for users to build upon. Collector Data Flow Dashboard provides valuable
guidance on which metrics to monitor. Users can tailor their own dashboard
variations by adding necessary metrics specific to their use cases, such as
memory_delimiter processor or other data flow indicators. This demo dashboard
serves as a starting point, enabling users to explore diverse usage scenarios
and adapt the tool to their unique monitoring needs.

## Data Flow Overview

The diagram below provides an overview of the system components, showcasing the
configuration derived from the OpenTelemetry Collector (otelcol) configuration
file utilized by the OpenTelemetry demo application. Additionally, it highlights
the observability data (traces and metrics) flow within the system.

![OpenTelemetry Collector Data Flow Overview](otelcol-data-flow-overview.png)

## Ingress/Egress Metrics

The metrics depicted in the diagram below are employed to monitor both egress
and ingress data flows. These metrics are generated by the otelcol process,
exported on port 8888, and subsequently scraped by Prometheus. The namespace
associated with these metrics is "otelcol," and the job name is labeled as
`otel.`

![OpenTelemetry Collector Ingress and Egress Metrics](otelcol-data-flow-metrics.png)

Labels serve as a valuable tool for identifying specific metric sets (such as
exporter, receiver, or job), enabling differentiation among metric sets within
the overall namespace. It is important to note that you will only encounter
refused metrics if the memory limits, as defined in the memory delimiter
processor, are exceeded.

### Ingress Traces Pipeline

- `otelcol_receiver_accepted_spans`
- `otelcol_receiver_refused_spans`
- `by (receiver,transport)`

### Ingress Metrics Pipeline

- `otelcol_receiver_accepted_metric_points`
- `otelcol_receiver_refused_metric_points`
- `by (receiver,transport)`

### Processor

Currently, the only processor present in the demo application is a batch
processor, which is used by both traces and metrics pipelines.

- `otelcol_processor_batch_batch_send_size_sum`

### Egress Traces Pipeline

- `otelcol_exporter_sent_spans`
- `otelcol_exporter_send_failed_spans`
- `by (exporter)`

### Egress Metrics Pipeline

- `otelcol_exporter_sent_metric_points`
- `otelcol_exporter_send_failed_metric_points`
- `by (exporter)`

### Prometheus Scraping

- `scrape_samples_scraped`
- `by (job)`

## Dashboard

You can access the dashboard by navigating to the Grafana UI, selecting the
**OpenTelemetry Collector Data Flow** dashboard under browse icon on the
left-hand side of the screen.

![OpenTelemetry Collector Data Flow dashboard](otelcol-data-flow-dashboard.png)

The dashboard has four main sections:

1. Process Metrics
2. Traces Pipeline
3. Metrics Pipeline
4. Prometheus Scraping

Sections 2,3 and 4 represent overall data flow using the metrics mentioned
above. Additionally, export ratio is calculated for each pipeline to understand
the data flow.

### Export Ratio

Export ratio is basically the ratio between receiver and exporter metrics. You
can notice over the dashboard screenshot above that the export ratio on metrics
is way too high than the received metrics. This is because the demo application
is configured to generate span metrics which is a processor that generates
metrics from spans inside collector as illustrated in overview diagram.

### Process Metrics

Very limited but informative process metrics are added to dashboard. For
example, you might observe more than one instance of otelcol running on the
system during restarts or similar. This can be useful for understanding spikes
on dataflow.

![OpenTelemetry Collector Process Metrics](otelcol-dashbord-process-metrics.png)
