Monitoring
115 bites tagged Monitoring — interview questions with model answers, and 60-second explainers.
Time-Series Compression: Storing More with Less
Time-series compression stores data more efficiently by saving the *difference* between consecutive points, not the full values. It's key for managing terabyte-scale monitoring and IoT data, often saving over 90% on storage.
Capacity Planning: Don't Run Out of Room
Capacity planning matches your system's resources to user demand, crucial for handling traffic spikes or budgeting cloud spend. The main footgun is planning for theoretical 'design capacity' instead of realistic 'effective capacity' which accounts for…
Steady State Hypothesis: The Core of Chaos Engineering
The steady state hypothesis is the core of chaos engineering: you bet your system's key metrics won't change when you break something. It's used to test resilience by defining "normal" (e.g., latency <200ms) and then trying to disrupt it with faults.
Performance Budgets: Set Limits to Stay Fast
A performance budget is a hard limit on metrics like bundle size or load time, acting as a guardrail against regressions. It's used in CI/CD to fail builds that exceed size limits or in monitoring to alert when load times degrade.
Time-Series Forecasting: Predicting the Future from the Past
Time-series forecasting uses past data points, ordered by time, to predict future values. It's used for capacity planning and financial modeling. The footgun is assuming past trends will hold, as sudden system changes can invalidate all predictions.
Benchmarking: Know Your System's Limits
Benchmarking finds your system's limits by measuring its responsiveness and stability under a controlled workload. Use it to catch performance regressions, compare tech choices, or for capacity planning. The footgun: trusting benchmarks run on your laptop.
Shadow Deployment: Test in Production, Safely
Shadow deployment copies live user traffic to a new "shadow" service for testing without user impact. It's used to validate new code versions with real-world load or to analyze traffic for security threats.
Quality Gates: Your Automated Release Checklist
A quality gate is an automated checklist that decides if code is ready for release. It runs in your CI pipeline, blocking merges or failing builds if metrics like code coverage or bug counts don't meet predefined standards.
Automated Canary Analysis: Let the Metrics Decide
Automated canary analysis uses metrics to decide if a new release is safe. It compares a new 'canary' version against the stable 'baseline' in production, scoring its health before a full rollout.
Auto-Remediation: Automated Fixes for Common Failures
Auto-remediation is a system's immune response, automatically detecting and fixing known problems like a crashed service. It's a core SRE practice for improving availability, but a bad script can create a 'remediation storm' that worsens an outage.
Executable Runbooks: Code, Not Just Checklists
An executable runbook turns a procedural document into an automated script. Instead of reading steps, you run them. It's used for incident response or maintenance, ensuring consistency. The footgun is not making them idempotent, which can worsen an outage.
Public Status Page: Your System's Voice During an Outage
A public status page is a dedicated site for communicating your service's health, turning "is it down?" support tickets into a single source of truth. It's used to report outages, degradation, and scheduled maintenance for public-facing services.
Incident Command System (ICS): Taming Outage Chaos
ICS gives a chaotic outage a clear command structure, defining roles so everyone knows who's in charge. It's used for major service outages or security breaches where multiple teams must coordinate. The footgun: not pre-assigning roles before a crisis hits.
The Incident Management Lifecycle
Incident management is a structured loop for handling service disruptions. It's not just about fixing the problem now, but identifying, analyzing, and correcting hazards to prevent them from happening again. The biggest mistake is skipping the 'prevent' step.
Game Days: Practice Breaking Your System Before It Breaks Itself
A Game Day is a live fire drill for your systems. You intentionally inject failure—like shutting down a service—to see how your team and automation respond, finding weaknesses before a real outage does.
ChatOps: Your CLI Inside Your Chat Room
ChatOps moves your command-line tools into your team's chat, making operations a spectator sport. Instead of a solo SSH session, you run commands via a bot for all to see. Use it for deployments and status checks. The biggest footgun is security.
Mean Time To Repair (MTTR): Measuring Your Fix Velocity
MTTR measures how quickly your team can fix a problem once active work begins. It's the 'wrench time' of incident response, not total outage duration. SREs track it to gauge runbook and diagnostic effectiveness.
Mean Time to Acknowledge (MTTA): Your First Response Clock
MTTA measures the time from an alert firing to a human acknowledging it. It's about reaction speed, not fix time. On-call teams use this to ensure issues are seen quickly, minimizing downtime.
On-Call Management Platforms: Who Wakes Up?
An on-call platform is a smart switchboard for production alerts, ensuring the right engineer gets paged when things break. It connects monitoring tools to on-call schedules and escalation rules.
Incident Command: Who Does What in a Crisis
The Incident Command System (ICS) is a playbook for major outages, assigning clear roles to avoid chaos. It's like an emergency response crew for your software. Use it when multiple teams must coordinate.
On-Call Rotations: Engineering Reliability Under Pressure
On-call rotations are the human backstop for service reliability, with engineers responding to alerts in minutes. This is critical for high-availability services like search or email.
Query Federation: Combining Prometheus Servers
Query federation lets one Prometheus server scrape metrics from another, creating a meta-monitor. Use it to build a global view from local servers or to combine application and infrastructure metrics for richer alerts.
Downsampling: Trading Granularity for Speed in Time Series Data
Downsampling trades resolution for speed by summarizing old, high-granularity metrics into coarser ones. This makes long-range queries faster and cheaper, common in systems like Thanos for long-term Prometheus data.
Elasticsearch: The Search Engine in the ELK Stack
Elasticsearch is a distributed search engine for querying massive, schema-free JSON datasets via an HTTP API. It's the core of log analysis platforms like the ELK stack, enabling fast search over terabytes of logs.
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