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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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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. All these contribute significantly towards ensuring smooth functioning.

Metrics 130
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An analysis of performance evolution of Linux’s core operations

The Morning Paper

For the rest of us, if you really need that extra performance (maybe what you get out-of-the-box or with minimal tuning is good enough for your use case) then you can upgrade hardware and/or pay for a commercial license of a tuned distributed (RHEL). A second takeaway is this: security has a cost! Measuring the kernel. Headline results.

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How to Assess MySQL Performance

HammerDB

Therefore, before we attempt to measure our database performance, we should know the system or cloud instance to be tested in detail. Benchmarking the target Two of the more popular database benchmarks for MySQL are HammerDB and sysbench. Operating System: Ubuntu 22.04 Operating System: Ubuntu 22.04

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PostgreSQL Performance Tuning: Optimizing Database Parameters for Maximum Efficiency

Percona

It is primarily the responsibility of the database administrator or developer to tune PostgreSQL according to their system’s workload. Hardware optimization : You need to ensure that the CPU, memory, and storage components meet the performance requirements of the database workload. What is PostgreSQL performance tuning?

Tuning 52
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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. A finely tuned database processes queries more efficiently, leading to swifter results. MySQL relies heavily on the availability of hardware resources to perform at its best.

Tuning 52
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MySQL Key Performance Indicators (KPI) With PMM

Percona

Indexing efficiency Monitoring indexing efficiency in MySQL involves analyzing query performance, using EXPLAIN statements, utilizing performance monitoring tools, reviewing error logs, performing regular index maintenance, and benchmarking/testing. This KPI is also directly related to Query Performance and helps improve it.