The challenge of inconsistent telemetry

Symptoms at scale

Senior engineers often report the following:

• APM dashboards showing traffic gaps while the underlying service logs remain continuous.
• Distributed traces missing spans from specific services.
• Metric granularity dropping unexpectedly from 1s to 1m intervals.
• Sudden unexplained increases in ingestion costs.

Why it matters

Incomplete or distorted telemetry undermines incident response. If traces skip critical hops, root cause analysis takes hours instead of minutes. Gaps in metrics cause alert flapping or false negatives. At enterprise scale, every percent of ingestion waste can cost tens of thousands of dollars monthly.

Background: how New Relic collects and processes telemetry

Agent based vs OpenTelemetry

New Relic supports both its proprietary language agents (Java, PHP, Node.js, .NET, Python, Go) and OpenTelemetry SDKs/collectors. The agents auto instrument common frameworks but rely on bytecode injection and environment variables. OpenTelemetry offers vendor neutrality but requires more explicit configuration and sampling decisions.

Data pipelines and aggregation

Metrics and traces are buffered locally, batched, and sent to regional New Relic collectors over HTTPS. If local buffers fill, older data is dropped. Aggregation can alter granularity depending on throughput. On ingestion, sampling and retention rules apply before dashboards render data.

Diagnostics: where to start

Step 1: confirm agent health

Each agent writes detailed logs. Enable debug mode temporarily to confirm spans are being generated and flushed. Look for buffer overflows, license key mismatches, or connectivity issues to the collector endpoint.

# Example: enable debug for Java agent
JAVA_TOOL_OPTIONS="-javaagent:/newrelic/newrelic.jar"
NEW_RELIC_LOG_LEVEL=finest
NEW_RELIC_LOG=stdout

Step 2: compare with service side logs

If your service logs show requests being processed while New Relic shows a gap, the problem is either in local buffering or upstream aggregation. Correlate timestamps to detect clock drift that skews trace stitching.

Step 3: validate distributed tracing headers

New Relic’s W3C trace context propagation requires all services to forward headers correctly. Misconfigured proxies, load balancers, or service meshes often strip or overwrite traceparent and tracestate headers, breaking spans.

// Node.js middleware example to log incoming headers
app.use((req,res,next)=>{
  console.log(req.headers["traceparent"], req.headers["tracestate"]);
  next();
});

Step 4: monitor network egress

High egress latency or intermittent TLS termination errors between the agent and New Relic collector endpoints can explain missing batches. Use tcpdump or cloud VPC flow logs to confirm outbound traffic consistency.

Root causes and their architectural implications

Misaligned sampling strategies

Combining New Relic agent side sampling with OpenTelemetry collector sampling doubles data loss. At high traffic rates, aggressive head based sampling may remove exactly the traces you need for incidents. The implication is that sampling must be centralized and coordinated.

Proxy and mesh side effects

In Istio or Linkerd meshes, sidecars may strip or normalize headers. This disrupts New Relic trace correlation and results in fragmented service maps. At scale, fragmented traces lead to blind spots exactly where the system is most complex.

Metric rollups under ingestion pressure

New Relic back end aggregates under high throughput, rolling 1s metrics into 1m buckets. This changes alert sensitivity and obscures short lived spikes. Enterprises relying on tight SLO windows must design around this rollup behavior.

Incorrect license scoping

Using multiple accounts or sub accounts with the wrong license key results in partial data landing in the wrong dashboards. This produces confusion and broken alerts. Long term, consolidating license keys and accounts is critical.

Step by step fixes

1) Standardize on propagation

Ensure all services propagate W3C trace headers unmodified. If using service meshes, configure explicit passthrough of traceparent and tracestate.

istio-proxy sidecar injection config:
proxyMetadata:
  DNS_CAPTURE: "true"
  TRACEPARENT: "true"

2) Unify sampling decisions

Decide whether sampling occurs at the edge collector (OpenTelemetry) or in New Relic. Do not mix both. Prefer tail based sampling via OpenTelemetry collector for critical traces, forwarding all spans to New Relic for storage.

3) Tune local agent buffers

Increase agent buffer sizes in high throughput services to prevent dropped data. For JVM agents, adjust newrelic.config.transaction_tracer.max_segments and related settings.

4) Harden time synchronization

Enable NTP or chrony across all nodes. Clock drift beyond a few hundred milliseconds causes broken trace stitching and misleading latency graphs.

5) Audit license key usage

Run inventory across environments to confirm consistent license key usage. Document account boundaries to ensure developers attach agents to the right account. Use account wide dashboards instead of mixing sub accounts.

6) Control ingestion costs

Apply metric filters and drop low value custom metrics. Use New Relic’s usage API to identify noisy services and refactor instrumentation. Introduce dimensionality reduction (labels, tags) to prevent cardinality explosions.

Best practices for enterprise observability with New Relic

• Adopt OpenTelemetry collector as a central ingestion layer, sending data to New Relic for long term storage and visualization.
• Use consistent propagation headers across all services, regardless of language.
• Implement service mesh passthrough rules to protect trace headers.
• Enable debug logging only temporarily; rotate logs to avoid disk bloat.
• Regularly review cost reports and apply ingestion controls.
• Integrate New Relic alerts with PagerDuty/Slack using SLO aligned thresholds.

Pitfalls to avoid

• Assuming New Relic agents auto instrument every custom library; manual spans are required for in house frameworks.
• Combining multiple sampling layers without coordination.
• Ignoring time synchronization across hybrid cloud nodes.
• Leaving debug logs on indefinitely in production, causing noise and cost.
• Failing to align account/license structure with organizational hierarchy.

Conclusion

New Relic anomalies in enterprise systems often stem from propagation failures, sampling mismatches, buffer overflows, or architectural blind spots created by service meshes and ingestion rollups. By standardizing propagation, unifying sampling, tuning buffers, enforcing clock sync, and auditing license usage, teams can restore observability integrity. Long term, pairing New Relic with OpenTelemetry collectors gives organizations flexibility, vendor neutrality, and control over costs without sacrificing visibility.

FAQs

1. Why are my distributed traces missing spans from certain services?

Most likely the trace context headers are being stripped or altered by a proxy or service mesh. Ensure full passthrough of W3C headers and verify application frameworks do not overwrite them.

2. How do I prevent ingestion costs from spiking unexpectedly?

Review the usage API regularly, apply metric filters, and drop high cardinality custom metrics. Centralize sampling and avoid instrumenting noisy endpoints with unbounded labels.

3. Can I run both New Relic agents and OpenTelemetry instrumentation?

Yes, but mixing requires careful design. Either let the New Relic agent export spans to OpenTelemetry collector, or standardize on OTel with a New Relic exporter. Avoid double instrumentation.

4. What is the impact of clock drift on traces?

Even a few hundred milliseconds of drift can misalign spans, making latency appear in the wrong service. Synchronize all nodes with NTP/chrony and monitor drift continuously.

5. Should we disable metric rollups in New Relic?

No. Rollups are internal to New Relic and not configurable. Instead, capture critical signals at the edge collector with higher granularity and export summaries alongside traces.