Reduce DNS-collector resource use by finding which part of the pipeline is consuming it, then changing the setting that affects that stage and measuring the result. Start with a representative workload and record throughput, CPU, resident memory, disk use, and any backlog or dropped messages. Go runtime limits can help constrain memory; metrics-cache settings and file rotation or compression affect other parts of the resource picture. No single setting is guaranteed to lower CPU, memory, and storage at once.
Start by locating the source of the load
DNS-collector ingests DNS streams or packet captures, filters and transforms data, and routes it to outputs. The enabled stages and workload shape its resource use, so avoid tuning from a single CPU or memory snapshot. First identify the installed version and enabled inputs, transforms, loggers, metrics, and file outputs; configuration keys and performance behavior can vary by release. The project overview describes its modular pipeline.
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Establish a baseline using the same traffic or representative replay, transforms, outputs, host or container limits, and retention period you will use to test a change. The Prometheus logger documentation provides received operations per second, maximum observed operations per second, and message and byte counters that can help put resource readings in context: Prometheus logger documentation.
- Record CPU use alongside throughput, latency, and any queueing, backlog, or loss.
- Track peak and steady resident memory and, where available, Go heap behavior.
- Measure bytes written over time and retained disk bytes after rotation or compression.
- Check whether the metrics and cache detail still meets your monitoring needs.
- Verify output completeness, not just whether the process stays within a limit.
Change one relevant setting at a time and compare it against that baseline. This helps distinguish a resource reduction from a workload shift or a loss of processing capacity.
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Limit memory with Go runtime controls
The DNS-collector performance guide documents two Go runtime controls: GOMEMLIMIT and GOGC. Go 1.19 and later supports GOMEMLIMIT, which prompts the runtime to collect garbage proactively to stay near a heap budget. The guide illustrates GOMEMLIMIT=50MiB ./dnscollector -config config.yml; this is an example, not a recommended value for every deployment. See the performance guide for its examples and release-specific guidance.
GOGC controls how much the heap may grow relative to the live heap before the next garbage-collection cycle; the documented default is 100. A lower value such as the guide’s example GOGC=50 prompts more frequent collection. The guide reports an illustrative peak RSS of roughly 30–40 MB in its stated context; do not treat that result as a prediction for a different traffic mix, configuration, or release.
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The guide also shows container and systemd examples, including GOMEMLIMIT=60MiB and GOGC=75. Its container example pairs those settings with a 100 MiB memory limit and a 50 MiB request. These are documentation examples, not general sizing advice. A heap target must leave room for memory used outside the Go heap and by the operating environment. After lowering a limit or GOGC, watch for increased garbage-collection overhead, CPU use, and throughput changes; a tighter memory target can trade memory headroom for more frequent work.
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Check metrics caches before shrinking them
If Prometheus metric features are enabled, DNS-collector maintains LRU caches for items such as requesters and domains and categories including NOERROR, SERVFAIL, nonexistent, and default-domain metrics. The documentation lists a 3,600-second TTL for the documented caches, with different default capacities. Review which metrics you actually need, cache occupancy, and the reporting window before lowering capacity or TTL. Reducing either can reduce retained cache state, but can also change the metric window or cardinality represented. The Prometheus logger documentation lists the available cache settings.
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Reduce CPU pressure by testing the busy stage
Use traffic and throughput counters to determine whether resource pressure tracks ingest volume, transformation work, logging, or metrics. CPU percentage alone does not show whether a configuration is processing the intended volume: compare CPU with operations per second, message and byte counts, latency, and any backlog or loss.
Recent DNS-collector release notes describe message batching, optional worker pools, lower-allocation DNS parsing and serialization, pointer-based message processing, and optimizations across collectors, transformers, and loggers. The notes report “~40% lower memory footprint” associated with changes to the DNStap collector, wire-DNS decoder, and JSON serialization. They also publish a benchmark comparison between v2.5.0 and v3.0.0: for 1,000,000 messages, execution time was 1.298 seconds versus 686 milliseconds, total CPU time 2.147 seconds versus 542 milliseconds, peak memory 105,680 KB versus 63,428 KB, and throughput 770,451.10 versus 1,457,310.66 messages per second. These are project-published, release-specific benchmark figures, not independently reproduced results or guarantees for another workload. Consult the release notes and compare the versions and configuration you actually run.
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More workers may improve scalability under high throughput, but they are not guaranteed to reduce CPU consumption. Batching may reduce per-message overhead or improve throughput, but its effect depends on the workload and configuration. Use the configuration reference for your installed release, change worker or batching settings only when they address an identified bottleneck, and observe CPU, latency, queueing, and drops together.
Control file storage with rotation and compression
The file logger supports completed-file rotation and optional gzip compression. Compression runs asynchronously, with only one compression task at a time. It can reduce retained file storage, but the documentation does not specify a compression ratio or quantify its CPU cost for a given DNS-collector workload. Check daily output volume, disk headroom, required retention, and whether compression finishes under your actual traffic and rotation cadence. Details are in the file logger documentation.
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The file logger also documents a post-rotation command that can move completed files into date-based backup folders. Moving files changes their location; it does not by itself reduce the total storage they occupy. Do not assume that off-host transfer or deletion is automatic: define and verify a separate lifecycle policy if your retention requirements call for it.
Validate changes without trading away data
- Record the setup: note the DNS-collector version, inputs, transforms, outputs, metrics, runtime settings, and host or container limits.
- Capture a baseline: use representative traffic or replay with the same processing stages, output destination, and retention period. Record CPU, throughput, latency, backlog or loss, peak and steady memory, bytes written, and retained bytes.
- Choose a targeted change: for heap pressure, test a runtime setting; for metric-cache overhead, adjust only a cache whose reporting needs you understand; for retained file growth, evaluate rotation and compression. For CPU pressure, investigate the busy stage and check whether your installed release offers relevant batching or worker settings.
- Repeat the comparison: keep the workload and limits comparable, and check that output remains complete. Revert or revise a change if memory falls but CPU overhead, backlog, latency, or loss becomes unacceptable.
There is no universal DNS-collector hardware or resource baseline in the project documentation reviewed here. Size a deployment against its own traffic rate, enabled processing stages, output destinations, retention target, and release.
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