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Performance Tuning Java Applications in Linux

DZone

You may also like: How to Properly Plan JVM Performance Tuning. While Performance Tuning an application both Code and Hardware running the code should be accounted for. In this blog post, we shall go over various aspects that have to be taken care of to extract maximum performance out of a Java Application running on Linux.

Java 147
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Crucial Redis Monitoring Metrics You Must Watch

Scalegrid

This blog post lists the important database metrics to monitor. 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.

Metrics 130
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What is serverless computing? Driving efficiency without sacrificing observability

Dynatrace

This allows teams to sidestep much of the cost and time associated with managing hardware, platforms, and operating systems on-premises, while also gaining the flexibility to scale rapidly and efficiently. When an application is triggered, it can cause latency as the application starts. This creates latency when they need to restart.

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InnoDB Performance Optimization Basics

Percona

This blog is in reference to our previous ones for ‘Innodb Performance Optimizations Basics’ 2007 and 2013. Although there have been many blogs about adjusting MySQL variables for better performance since then, I think this topic deserves a blog update since the last update was a decade ago, and MySQL 5.7

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How To Scale a Single-Host PostgreSQL Database With Citus

Percona

PostgreSQL Cluster One coordinator node citus-coord-01 Three worker nodes citus1 citus2 citus3 Hardware AWS Instance Ubuntu Server 20.04, SSD volume type 64-bit (x86) c5.xlarge And now, execute the benchmark: -- execute the following on the coordinator node pgbench -c 20 -j 3 -T 60 -P 3 pgbench The results are not pretty.

Database 103
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Towards a Reliable Device Management Platform

The Netflix TechBlog

Complementing the hardware is the software on the RAE and in the cloud, and bridging the software on both ends is a bi-directional control plane. In this blog post, we will focus on the latter feature set. In particular, the Kafka integration is the most relevant for this blog post. million elements.

Latency 213
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The evolution of single-core bandwidth in multicore processors

John McCalpin

Some I will address today, and some in the next blog entry. Details in the next blog entry.) To understand what is happening here, we need to understand the way memory bandwidth interacts with memory latency and the concurrency (parallelism) of memory accesses. Stay tuned! Why not something like STREAM?