Remove Availability Remove Benchmarking Remove Hardware Remove Latency
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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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Why you should benchmark your database using stored procedures

HammerDB

HammerDB uses stored procedures to achieve maximum throughput when benchmarking your database. HammerDB has always used stored procedures as a design decision because the original benchmark was implemented as close as possible to the example workload in the TPC-C specification that uses stored procedures. On MySQL, we saw a 1.5X

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MySQL Key Performance Indicators (KPI) With PMM

Percona

Database uptime and availability Monitoring database uptime and availability is crucial as it directly impacts the availability of critical data and the performance of applications or websites that rely on the MySQL database. This KPI is also directly related to Query Performance and helps improve it.

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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 reduction in latency ensures that applications and websites provide a more rapid and responsive user experience. This does not apply to read (SELECT) traffic.

Tuning 52
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HammerDB v4.0 New Features Pt1: TPROC-C & TPROC-H

HammerDB

A full understanding of why this is important requires some knowledge of the evolution of database hardware and software. The HammerDB TPROC-C workload by design intended as CPU and memory intensive workload derived from TPC-C – so that we get to benchmark at maximum CPU performance at a much smaller database footprint.

C++ 40
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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. on end-to-end latency) and less than 0.15% on throughput.

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Kubernetes for Big Data Workloads

Abhishek Tiwari

A recent performance benchmark completed by Intel and BlueData using the BigBench benchmarking kit has shown that the performance ratios for container-based Hadoop workloads on BlueData EPIC are equal to and in some cases, better than bare-metal Hadoop [7]. Kubernetes has all necessary primitives available to make it happen.