This content originally appeared on DEV Community and was authored by Thomas Johnson
OpenTelemetry (OTel) has emerged as a powerful, vendor-neutral solution for collecting and managing observability data. At the heart of this system lies the OTel Collector, a versatile component that handles the ingestion, processing, and export of telemetry data.
While the Collector’s modular design offers great flexibility through its receivers, processors, exporters, and extensions, proper configuration is essential to avoid issues like data loss, performance problems, and security vulnerabilities.
This 3 part, short series explores key best practices for optimizing your OTel Collector setup to create a robust and efficient observability pipeline.
Implementing Effective Processor Logic
Processors form a critical component in the OpenTelemetry pipeline, working collaboratively to transform and optimize telemetry data before it reaches its destination. Strategic processor configuration can significantly reduce system overhead, improve data quality, and ensure efficient resource utilization.
Common Processing Challenges
Modern applications can generate massive amounts of telemetry data, often producing thousands of spans every second. This high volume creates several potential issues:
- System instability during peak traffic periods
- Backend systems overwhelmed with non-essential data
- Exposure of sensitive information requiring compliance measures
Essential Processor Components
To address these challenges, a well-structured processor pipeline should include these key elements:
- Memory Limiter: Maintains system stability by enforcing memory usage boundaries
- Batch Processor: Consolidates telemetry data to optimize network usage and enhance throughput
- Filter Processor: Eliminates unnecessary data points to improve storage efficiency
- Attributes Processor: Manages data context and handles sensitive information
Processor Configuration Example
An effective processor configuration might include batching data in groups of 1,000 with a 10-second timeout, limiting memory usage to 1024 MiB with spike protection, and implementing filtering rules for specific scenarios. The configuration can selectively process:
- HTTP error responses (status codes 400 and above)
- gRPC-related events
- Specific metric types from designated hosts
- Log entries based on severity levels
Additionally, processors can enrich data by adding contextual information, such as environment tags, which proves valuable for analysis and troubleshooting. This combination of processors creates a balanced pipeline that optimizes performance while maintaining data quality and system stability.
Here’s an example processor pipeline:
What’s Next
This is just a brief overview and it doesn’t include many important considerations when it comes to the OpenTelemetry Collector.
If you are interested in a deep dive in the above concepts, visit the original: OTel Collector: Best Practices & Examples
I cover these topics in depth:
- Choose appropriate processor logic
- Prioritize security
- Optimize the receiver configuration
- Efficiently export to the backend
- Monitor the Collector
- Integrate with appropriate tooling
- Putting it all together
If you’d like to chat about this topic, DM me on any of the socials (LinkedIn, X/Twitter, Threads, Bluesky) - I’m always open to a conversation about tech! 
This content originally appeared on DEV Community and was authored by Thomas Johnson