DNS Telemetry Collection with Open‑Source eBPF Sensors at Scale

In the context of modern networking and cybersecurity, DNS telemetry has emerged as a critical layer of observability. DNS logs provide insights into device behavior, application communication patterns, threat indicators, and network performance. Traditional approaches to DNS telemetry collection have relied heavily on application-layer logging at recursive resolvers, passive packet capture on mirror ports, or endpoint agents with kernel hooks. Each method carries inherent trade-offs in overhead, latency, visibility, and deployment complexity. However, with the maturation of eBPF (extended Berkeley Packet Filter) technology in the Linux kernel, a new approach has become feasible—one that offers fine-grained, high-performance DNS telemetry collection directly within the operating system, without modifying applications or duplicating traffic. Using open-source eBPF sensors, organizations can now collect DNS telemetry at scale across heterogeneous environments, with minimal performance impact and unprecedented contextual richness.

eBPF enables the safe execution of user-defined programs in kernel space, offering an efficient mechanism to attach logic to various system events, including network socket activity, function calls, and system calls. When applied to DNS, eBPF programs can be attached to specific points in the networking stack where DNS queries and responses flow—such as the udp_recvmsg, udp_sendmsg, or skb_clone hooks—or higher-level libraries like getaddrinfo if user-space tracing is enabled. This allows DNS telemetry to be captured directly from the wire, often before the data even leaves the host, without requiring modification of the DNS client or interception via traditional proxies.

Open-source projects like Cilium’s Hubble, Tracee by Aqua Security, and Deepfence’s PacketStreamer have demonstrated how eBPF can be used for observability, and several dedicated tools such as “ebpf_exporter,” “bpftrace,” and “dnsflow” have emerged with focused capabilities for DNS. These sensors are written in restricted C code, compiled to bytecode, and loaded into the kernel via tools such as bpftool or user-space agents running on the host. Once deployed, these sensors can monitor all DNS-related socket interactions, capturing metadata including timestamp, query name, query type, source and destination IPs, ports, response codes, and round-trip times. In addition to this, they can associate DNS queries with the process ID, executable path, user ID, container ID, and namespace—providing critical context that is unavailable in network-only telemetry.

At scale, DNS telemetry collected via eBPF is streamed to big-data ingestion platforms such as Apache Kafka, AWS Kinesis, or NATS for further processing. These pipelines decouple data collection from analysis, enabling flexible integration with processing frameworks like Apache Flink, Apache Beam, or Spark Structured Streaming. Each telemetry event is serialized in a compact format—commonly protobuf, Avro, or JSON—and optionally enriched in flight with geolocation, ASN information, or threat intelligence labels based on the queried domains or resolved IPs.

Scaling eBPF-based telemetry requires careful orchestration. In large environments, sensors are deployed as part of a DaemonSet in Kubernetes clusters or through fleet management tools like Ansible, Chef, or FleetDM for bare-metal and virtual machines. The sensors are typically configured with filters to reduce noise—such as excluding popular CDN domains, internal service discovery queries, or well-known recursive resolvers. eBPF maps are used to maintain efficient lookups and counters in kernel space, avoiding the need to emit telemetry for every packet unless a new flow or anomaly is detected. Rate-limiting, adaptive sampling, and buffer backpressure handling are implemented to ensure that kernel-space instrumentation does not overload the system.

One of the significant advantages of eBPF-based DNS telemetry is its ability to maintain strong security guarantees. Because eBPF programs are verified before execution and run in an isolated VM within the kernel, they cannot crash the system or perform unauthorized memory access. Furthermore, since telemetry is collected at the kernel level, it is resistant to evasion by user-space malware or tampering by compromised applications. This makes it especially suitable for environments with high security and compliance requirements, such as financial institutions, critical infrastructure, and managed detection and response (MDR) platforms.

Data collected through eBPF sensors supports a wide range of downstream analytics. Security operations teams use the telemetry to detect command-and-control traffic patterns, domain generation algorithm (DGA) activity, exfiltration over DNS, and anomalous query bursts. Performance teams monitor resolution latency, cache hit ratios, and failure rates per host or service. Developers and DevOps engineers use process-level DNS telemetry to debug service discovery issues, verify DNSSEC validation behavior, and trace dependencies between microservices. All of these use cases benefit from the high fidelity and process attribution that eBPF sensors uniquely provide.

Storage and query of this telemetry at scale is typically managed through a combination of hot and cold data stores. Recent telemetry is indexed and stored in columnar engines like ClickHouse, Apache Druid, or Elasticsearch for low-latency querying and dashboarding. Longer-term retention for compliance or retrospective analysis is offloaded to object stores like Amazon S3 or Google Cloud Storage, with data lifecycle policies and partitioning strategies based on time, tenant, or source system. Metadata catalogs and schema registries ensure that downstream systems can query the data consistently, even as the schema evolves with additional fields or enrichment layers.

To improve efficiency further, advanced deployments implement edge summarization and local roll-ups. For example, instead of forwarding every individual DNS response event, agents may aggregate metrics such as “queries per domain per process per minute” and emit only the deltas. This not only reduces bandwidth but also increases the value of the data by aligning it with analytical use cases. Such pre-aggregation logic can be implemented directly within the eBPF program, in user-space agents, or in stream processing applications that act as intermediaries between raw data and storage.

Observability into the telemetry pipeline itself is also critical. Monitoring dashboards track metrics such as sensor uptime, dropped packet rates, map fill ratios, and serialization lag. Alerts are triggered if eBPF maps reach memory thresholds or if telemetry volumes deviate from historical baselines. This ensures that the collection infrastructure remains robust, especially during incident response scenarios when visibility is most critical.

DNS telemetry collection using open-source eBPF sensors represents a new standard for scalable, secure, and context-rich observability. It bridges the gap between traditional packet inspection and application logging, providing real-time insight into DNS activity with low overhead and deep system integration. As more organizations adopt eBPF-based tooling as part of their observability stacks, the ability to harness DNS telemetry for operational intelligence, threat detection, and performance optimization will expand dramatically. The flexibility of eBPF, combined with the power of modern big-data pipelines, positions this approach as a foundational pillar in the future of internet-scale DNS analytics.

In the context of modern networking and cybersecurity, DNS telemetry has emerged as a critical layer of observability. DNS logs provide insights into device behavior, application communication patterns, threat indicators, and network performance. Traditional approaches to DNS telemetry collection have relied heavily on application-layer logging at recursive resolvers, passive packet capture on mirror ports, or…