Choose between OTel Collector agent and gateway deployment patterns
domain: opentelemetry.io · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Use the agent pattern (DaemonSet or sidecar) when you need locality: collecting host metrics, scraping local endpoints, or minimising network hops from your workload to the Collector
Use the gateway pattern (centralised Deployment behind a load balancer) when you need shared processing: tail sampling, cross-service enrichment, fan-out to multiple backends, or policy enforcement at a single chokepoint
For most production environments combine both: agents collect and lightly buffer, forwarding over OTLP to a gateway tier that batches, samples, and exports
Configure agents with smaller batch sizes (timeout: 200ms, send_batch_size: 512) and short queue sizes to keep per-node memory low; configure gateways with larger batch sizes and deeper queues for throughput
In Kubernetes deploy agents as a DaemonSet so each node has exactly one agent; deploy gateway as a Deployment (2+ replicas) fronted by a ClusterIP Service or an L7 gRPC-aware load balancer
Instrument agent config with health_check and zpages extensions; instrument gateway with the same plus pprof for profiling under load
Known gotchas
Standard L4 load balancers establish a single persistent gRPC connection to one backend pod, negating horizontal scale—use an L7 gRPC-aware load balancer or a headless Service with client-side balancing
Running multiple agents on the same node (e.g., DaemonSet + sidecar both watching the same log paths) will produce duplicate log records; pick one collection point per node
Agents close to workloads are most at risk of data loss if the gateway is unavailable—enable retry_on_failure and a sending_queue on the agent's OTLP exporter so it can buffer during gateway restarts
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