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DevOps & observability · #newrelic
Learn how to add New Relic custom attributes in Java, query them with NRQL, and isolate latency spikes in your checkout API.
Learn how to enrich New Relic APM traces with custom key‑value pairs (e.g., user‑role, tenant‑id) using the Java agent API, verify the data, and roll back safely if needed.
Guide to decide between New Relic Java agent auto‑instrumentation and manual custom instrumentation for Java microservices, with constraints, a comparison table, trade‑offs, implementation steps, validation, limitations, and rollback.
Learn how to enable New Relic Distributed Tracing in a Java microservice stack, configure sampling, verify header propagation, and balance observability with cost. Practical steps, code snippets, and troubleshooting tips included.
Learn how to diagnose and resolve data ingestion gaps in New Relic by analyzing NRQL buckets, testing network egress, and auditing agent logs for handshake failures.
Stop guessing where latency lives in your Kubernetes cluster. Learn how to use New Relic Distributed Tracing to map request flows and pinpoint the exact service or external API causing delays.
The “Data ingestion rate limit exceeded” error appears when an agent sends more trace or event data than the plan allows. In a low‑traffic application, enabling full transaction tracing or distributed tracing can trigger this limit and inflate the bill. New Relic offers a global or per‑agent trace_sampling setting and a transaction_tracer.enabled toggle that
Managing data ingestion costs for low-traffic workloads requires a balance between budget constraints and telemetry visibility. In New Relic, reducing billable GB per month can be achieved through targeted Data Drop rules or probabilistic sampling. Drop rules provide a binary mechanism to discard specific, high-volume noise—such as repetitive health check te
The goal is to ascertain whether New Relic intends to introduce a test‑mode capability within its agents that would automatically separate test telemetry from production data without requiring users to maintain distinct accounts or license keys. Currently, data isolation depends entirely on using separate account identifiers, and no built‑in flag exists to m
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