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Start by identifying the source of resource pressure
DNS-collector ingests DNS streams or packet captures, filters and transforms the data, then routes it to outputs. Its resource needs therefore depend on traffic volume and which collectors, transformations, metrics, and loggers are enabled. The project overview describes its modular pipeline; it does not provide a universal hardware-sizing figure.
Before changing settings, note the installed DNS-collector version and establish a baseline. Compare equivalent traffic, transforms, outputs, and host or container limits. Observe CPU alongside throughput and loss or backlog; memory as both resident use and Go heap behavior; disk writes and retained bytes across rotation; and output completeness and latency. A lower resource reading is not an improvement if it comes with missing data, excessive delay, or metrics that no longer answer your operational questions.
Use available metrics to understand workload
The Prometheus logger documentation describes received operations per second, maximum observed operations per second, and message and byte counters. Use these to relate resource changes to the volume being processed. They help characterize load, but do not by themselves identify which pipeline stage is consuming CPU.
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Limit memory without setting an unsafe target
DNS-collector’s performance guide documents two Go runtime controls. On Go 1.19 and later, GOMEMLIMIT prompts the runtime to collect garbage more proactively to stay near a heap budget. GOGC controls how much allocation growth relative to the live heap is allowed before the next garbage-collection cycle; the documented default is 100.
Set a heap budget and verify its effects
The guide gives GOMEMLIMIT=50MiB ./dnscollector -config config.yml as an example, not a universal target. A heap budget is not the same thing as total process memory: leave room for non-heap memory and the operating environment. Tightening the limit can increase garbage-collection work, so track CPU and throughput as well as memory after changing it.
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The same guide uses GOGC=50 to illustrate more aggressive collection and describes a peak RSS outcome of roughly 30–40 MB in its stated context. That figure is not a forecast for another release or traffic mix. Test the setting against your own workload rather than treating the example as a promised result.
Apply container or systemd examples cautiously
The performance guide also shows configuration examples using GOMEMLIMIT=60MiB and GOGC=75 with a container memory limit of 100 MiB and a request of 50 MiB, as well as a systemd environment example. These are documentation examples, not recommended defaults for every deployment. Make sure the heap target fits within the actual memory limit with headroom for other process and system needs, then check for higher GC overhead, throughput changes, or instability.
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Reduce Prometheus cache use only if the reporting trade-off is acceptable
If Prometheus metrics are enabled, inspect the documented LRU caches for requesters and domains and categories such as NOERROR, SERVFAIL, nonexistent, and default-domain metrics. The documentation lists a 3,600-second TTL for these caches and different default capacities. Reducing cache capacity or retention may reduce the amount of metric state retained, but can also change the metric window or cardinality represented. Check what your monitoring needs to retain before changing the limits, then confirm the resulting metrics remain useful.
Address CPU use in the processing pipeline
First determine whether CPU use changes with incoming volume, transformations, or output work. Use throughput and byte or message counters to put CPU observations in context, and compare under the same input rate and pipeline configuration. Do not tune from a CPU percentage alone: a change can raise throughput while also raising total CPU use, or lower CPU while creating a backlog.
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Check release-specific performance improvements
The release notes describe performance work including message batching, optional worker pools, lower-allocation DNS parsing and serialization, pointer-based message processing, and optimizations across collectors, transformers, and loggers. The project reports roughly 40% lower memory footprint in connection with changes to the DNStap collector, wire-DNS decoder, and JSON serialization. This is specific to the release and comparison described there, not a general expectation for every deployment.
The release page also publishes a benchmark comparing v2.5.0 and v3.0.0 on 1,000,000 messages: execution time was 1.298 seconds versus 686 milliseconds, total CPU time was 2.147 seconds versus 542 milliseconds, peak memory was 105,680 KB versus 63,428 KB, and throughput was 770,451.10 versus 1,457,310.66 messages per second. These are project-published benchmark figures for that comparison, not independently reproduced results or a guarantee for your traffic, transforms, or outputs. Confirm your installed version and its configuration reference before applying release-specific controls.
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Tune workers and batching against throughput and loss
More workers can help scale high-throughput processing, but they do not necessarily reduce CPU consumption. Batching may improve throughput and reduce per-message overhead, but its effect depends on workload and configuration. Change one relevant setting at a time and watch CPU, latency, queueing, and drops together. Keep a change only when it improves the constraint you care about without unacceptable loss or delay.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Reduce retained log storage with rotation and compression
The file logger documentation describes log-file rotation and optional gzip compression. Compression runs asynchronously, with one compression task at a time. It can reduce the space occupied by retained log files, but the documentation does not specify a compression ratio or quantify CPU cost for DNS-collector. Actual storage savings and processing impact depend on your data and rotation cadence.
Measure daily file output and retained bytes after rotation and compression complete. Check disk headroom and confirm compression keeps up with the rate of rotation. If it does not, the asynchronous work may accumulate relative to your retention needs; adjust your configuration or operational plan based on observed output and available capacity.
Understand what a post-rotation hook does
The file logger guide also documents a post-rotation command that can move completed files into date-based backup folders. This is a file-lifecycle hook, not a mechanism that inherently reduces total storage. Account for where those files go and how long they remain when assessing disk use.
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Run a controlled before-and-after check
- Record the starting configuration. Note the DNS-collector version, enabled pipeline stages, runtime settings, metric caches, file logger options, and host or container limits.
- Establish a representative baseline. Use the same traffic or replay and record CPU, throughput, loss or backlog, peak and steady resident memory, Go heap behavior, disk bytes written, retained bytes after rotation, metric usefulness, output completeness, and latency.
- Change one relevant control. For memory pressure, test a runtime limit or cache adjustment; for CPU pressure, investigate the processing version, workers, or batching; for storage pressure, verify rotation and compression behavior. Avoid changing unrelated controls simultaneously.
- Repeat under comparable conditions. Keep input, transformations, outputs, retention period, and resource limits consistent. Compare resource use alongside throughput, data completeness, latency, and monitoring needs.
- Keep, revise, or revert based on the result. Revert changes that trade away required data, observability, or latency without solving the actual bottleneck. Record the version and configuration that produced the result.
Plan capacity from your own workload
The reviewed project documentation does not establish a universal CPU, memory, or disk requirement for DNS-collector. Capacity depends on the traffic rate, enabled transforms, destinations, metrics, retention, and software version. Size and tune the deployment by measuring that combination under representative load; project benchmark results and example runtime settings are useful reference points, not substitutes for deployment-specific testing.
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