Operations | Monitoring | ITSM | DevOps | Cloud

The latest News and Information on Service Reliability Engineering and related technologies.

Introducing Bits AI SRE, your AI on-call teammate

Bits AI SRE is your AI on-call teammate, built to autonomously investigate alerts and coordinate incident response. Integrated with Datadog, Slack, GitHub, Confluence, and more, Bits analyzes telemetry, reads documentation, and reviews recent deployments to determine the root cause of alerts—often before you’ve even opened your laptop. In fact, if you're using Datadog On-Call, you can view Bits’s findings right from your phone—so you’re always one step ahead, no matter where you are.

Ship Confluent Cloud Observability in Minutes

You're running Kafka on Confluent Cloud. You care about lag, throughput, retries, and replication. But where do you see those metrics? Confluent gives you metrics, sure, but not all in one place. Some live behind a metrics API, others behind Connect clusters or Schema Registries. You either wire them manually or give up. What if you could stream those metrics to a platform built for high-frequency, high-cardinality time series, and do it in minutes?

Monitor Nginx with OpenTelemetry Tracing

At 3:47 AM, your NGINX logs show a 500 error. Around the same time, your APM flags a spike in API latency. But what's the root cause, and why is it so hard to correlate logs, traces, and metrics? When API response times cross 3 seconds, identifying whether the slowdown is at the NGINX layer, the application, or the database shouldn't require guesswork. That's where OpenTelemetry instrumentation for NGINX becomes essential.

How to Set Up Real User Monitoring

Synthetic monitoring provides consistent, repeatable results, 2.1s load times, passing Lighthouse scores, and minimal variability. But those numbers reflect lab conditions. On slower networks, like 3G in Southeast Asia, real users may see much higher load times, 5.8s or more. This isn’t a fault of the tools. It’s a difference in testing context. Synthetic tests run on fast machines, stable connections, and clean environments.

Risk Register for SREs: A Practical Guide to Proactive Incident Prevention

A risk register is one of the most powerful tools in an SRE's arsenal for maintaining system reliability. By systematically documenting potential threats to your infrastructure and services, you can shift from reactive firefighting to proactive risk management.

Set Up ClickHouse with Docker Compose

ClickHouse is built for high-performance OLAP workloads, capable of scanning billions of rows in seconds. If your analytical queries are bottlenecked on PostgreSQL or MySQL, or you're burning too much on Elasticsearch infrastructure, ClickHouse offers a faster and more cost-efficient alternative. This blog walks through setting up ClickHouse locally with Docker Compose and scaling toward a production-grade cluster with monitoring in place.

Stream AWS Metrics to Grafana with Last9 in 10 minutes

It’s 2:47 AM and your Lambda functions are timing out. API response times are spiking. You’re flipping between the CloudWatch console, your APM tool, and your logs, trying to figure out what’s going wrong. CloudWatch has the metrics you need: CPU usage, memory pressure, and request rates — but connecting that data to what your app is doing takes time. The delay in stitching it all together slows down your incident response.

Query and Analyze Logs Visually, Without Writing LogQL

It’s 2 AM. An incident’s in progress. Error rates are climbing. You jump into the logs, filter by service, adjust the time window… and now you need a LogQL query. You write one. It errors out. You fix the syntax, try again, only to realize you need a different filter or a new aggregation. Back to rewriting. By the time you’ve got the query right, you’ve already lost 10–15 minutes. The system is still broken, and you still don’t know why.

Trace Go Apps Using Runtime Tracing and OpenTelemetry

When your Go service hits 500ms latencies but CPU usage is flat, tracing gives you visibility into what the profiler misses. With 1–2% runtime overhead, Go’s built-in tracing tools help you: This makes it easier to debug performance regressions that don’t leave a clear footprint.