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Latency, throughput, and response time are key metrics in system performance.

The RED Method helps monitor microservices using Rate, Errors, and Duration metrics.

Logs, metrics, and traces help engineers debug issues in software systems.

Optimizing Elasticsearch involves using filters, caching, and measuring performance.

Elasticsearch aggregations help summarize data using metrics for calculations and buckets for grouping.

SLIs measure service performance, SLOs set reliability targets, and SLAs are customer agreements.

The USE Method helps find and analyze infrastructure bottlenecks in software systems.

Elasticsearch aggregations help analyze data by grouping, calculating, and estimating values.

Observability helps engineers understand system behavior to debug production issues effectively.

Elasticsearch ranks search results using BM25 based on relevance scoring.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Latency is the time for one request, while throughput is how many requests are processed.

Alerting helps notify the right people about production issues quickly.

Monitoring Kafka consumer lag helps prevent production problems by ensuring consumers keep up with message traffic.

Elasticsearch ILM automates index management to reduce storage costs and meet data needs.

A CDN speeds up web applications by caching content closer to users.

Deep pagination in Elasticsearch can slow down searches significantly, but there are efficient methods to handle it.