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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

Key metrics like throughput, request latency, and memory utilization are essential for assessing Redis health, with tools like the MONITOR command and Redis-benchmark for latency and throughput analysis and MEMORY USAGE/STATS commands for evaluating memory. Cache Hit Ratio The cache hit ratio represents the efficiency of cache usage.

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Compress objects, not cache lines: an object-based compressed memory hierarchy

The Morning Paper

Compress objects, not cache lines: an object-based compressed memory hierarchy Tsai & Sanchez, ASPLOS’19. So while you probably won’t be using Zippads in practice anytime soon, it’s a wonderful example of what’s possible when you’re prepared to take a fresh look at “the way we’ve always done things.” Implications.

Cache 61
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Impact of Querying Table Information From information_schema

Percona

A lot of useful information can be retrieved from this schema, for example, table metadata and foreign key relations, but trying to query I_S can induce performance degradation if your server is under heavy load, as shown in the following example test. The same tests have been executed in Percona Server for MySQL 5.7

Cache 103
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How to Assess MySQL Performance

HammerDB

For example, if you are buying the latest Amazon memory-optimized EC2 instance (R7iz), the AWS page ( [link] ) tells us the following: Up to 3.9 For example, if you are buying the latest Amazon memory-optimized EC2 instance (R7iz), the AWS page ( [link] ) tells us the following: Up to 3.9 For storage, FIO is generally used.

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MySQL Key Performance Indicators (KPI) With PMM

Percona

Let’s dive in and learn how (and what) to effectively monitor MySQL performance, along with examples from PMM, by understanding the critical KPIs to watch for. This includes metrics such as query execution time, the number of queries executed per second, and the utilization of query cache and adaptive hash index.

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MySQL Performance Tuning 101: Key Tips to Improve MySQL Database Performance

Percona

This results in expedited query execution, reduced resource utilization, and more efficient exploitation of the available hardware resources. This not only enhances performance but also enables you to make more efficient use of your hardware resources, potentially resulting in cost savings on infrastructure.

Tuning 52
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Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices

The Morning Paper

Last time around we looked at the DeathStarBench suite of microservices-based benchmark applications and learned that microservices systems can be especially latency sensitive, and that hotspots can propagate through a microservices architecture in interesting ways. When available, it can use hardware level performance counters.