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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key Takeaways Critical performance indicators such as latency, CPU usage, memory utilization, hit rate, and number of connected clients/slaves/evictions must be monitored to maintain Redis’s high throughput and low latency capabilities. It can achieve impressive performance, handling up to 50 million operations per second.

Metrics 130
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Redis vs Memcached in 2024

Scalegrid

This article will explore how they handle data storage and scalability, perform in different scenarios, and, most importantly, how these factors influence your choice. It uses a hash table to manage these pairs, divided into fixed-size buckets with linked lists for key-value storage. Redis Database Management with ScaleGrid ScaleGrid.io

Cache 130
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Evaluating the Evaluation: A Benchmarking Checklist

Brendan Gregg

These have inspired me to summarize another performance activity: evaluating benchmark accuracy. Accurate benchmarking rewards engineering investment that actually improves performance, but, unfortunately, inaccurate benchmarking is more common. If the benchmark reported 20k ops/sec, you should ask: why not 40k ops/sec?

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Building Netflix’s Distributed Tracing Infrastructure

The Netflix TechBlog

Reconstructing a streaming session was a tedious and time consuming process that involved tracing all interactions (requests) between the Netflix app, our Content Delivery Network (CDN), and backend microservices. A second job taps the data feed from the first job, does tail sampling of data and writes traces to the storage system.

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Grafana Dashboards: A PoC Implementing the PostgreSQL Extension pg_stat_monitor

Percona

This allows for much better data accuracy, especially in the case of high-resolution or unreliable networks. A script executing a benchmarking run: #!/bin/bash Multi-Dimensional Grouping : While pg_stat_statements groups counters by userid, dbid, queryid, pg_stat_monitor uses a more detailed group for higher precision.

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What Is a Workload in Cloud Computing

Scalegrid

This is sometimes referred to as using an “over-cloud” model that involves a centrally managed resource pool that spans all parts of a connected global network with internal connections between regional borders, such as two instances in IAD-ORD for NYC-JS webpage DNS routing.

Cloud 130
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Evaluating the Evaluation: A Benchmarking Checklist

Brendan Gregg

These have inspired me to summarize another performance activity: evaluating benchmark accuracy. Accurate benchmarking rewards engineering investment that actually improves performance, but, unfortunately, inaccurate benchmarking is more common. If the benchmark reported 20k ops/sec, you should ask: why not 40k ops/sec?