What is OpenTelemetry Collector Logs: High Volume Dropping

Resolving High Volume Log Dropping in OpenTelemetry Collector

Understanding OpenTelemetry Collector

The OpenTelemetry Collector is a vendor-agnostic service that collects, processes, and exports telemetry data (metrics, logs, and traces) to various backends. It is designed to handle large volumes of telemetry data efficiently, providing a centralized point for data collection and processing.

Identifying the Symptom: High Volume Log Dropping

When using OpenTelemetry Collector, you might encounter a situation where logs are being dropped. This symptom is typically observed when the volume of incoming logs exceeds the processing capacity of the collector, leading to data loss.

Root Cause Analysis

The primary reason for log dropping in high-volume scenarios is that the collector's capacity is insufficient to handle the incoming data rate. This can be due to inadequate resource allocation, inefficient pipeline configuration, or lack of log sampling mechanisms.

Resource Constraints

Resource constraints such as CPU, memory, or network bandwidth can limit the collector's ability to process logs efficiently.

Pipeline Configuration

An improperly configured processing pipeline can lead to bottlenecks, causing logs to be dropped.

Steps to Resolve High Volume Log Dropping

1. Increase Collector Capacity

To handle higher log volumes, consider scaling up the resources allocated to the OpenTelemetry Collector. This can be achieved by increasing CPU and memory limits in your deployment configuration. For example, in a Kubernetes environment, you can modify the resource requests and limits in your Deployment or StatefulSet:

resources: requests: memory: "512Mi" cpu: "500m" limits: memory: "1Gi" cpu: "1"

2. Optimize Log Processing Pipeline

Review and optimize the processing pipeline to ensure efficient handling of logs. This includes:

Removing unnecessary processors or exporters. Ensuring that the pipeline stages are appropriately configured to handle the expected load.

Refer to the OpenTelemetry Collector Configuration documentation for detailed guidance on configuring pipelines.

3. Implement Log Sampling

Implement log sampling to reduce the volume of logs processed by the collector. Sampling can be configured in the pipeline to only process a subset of logs, thus reducing the load. Here's an example of configuring a sampling processor:

processors: tail_sampling: decision_wait: 30s num_traces: 100000 expected_new_traces_per_sec: 10 policies: - name: always_sample type: always_sample

4. Monitor and Adjust

Continuously monitor the performance of your OpenTelemetry Collector using metrics and logs. Adjust the configuration as needed to ensure optimal performance. Utilize tools like Prometheus for monitoring and Grafana for visualization.

Conclusion

By increasing the collector's capacity, optimizing the processing pipeline, and implementing log sampling, you can effectively address the issue of high volume log dropping in OpenTelemetry Collector. Regular monitoring and adjustments will help maintain the efficiency and reliability of your telemetry data collection system.