Performance

Scaling AMQ 7 Brokers with AMQ Interconnect

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By Roman Martin Gil

Red Hat JBoss AMQ Interconnect provides flexible routing of messages between AMQP-enabled endpoints, including clients, brokers, and standalone services. With a single connection to a network of AMQ Interconnect routers, a client can exchange messages with any other endpoint connected to the network.

AMQ Interconnect can create various topologies to manage a high volume of traffic or define an elastic network in front of AMQ 7 brokers. This article shows a sample AMQ Interconnect topology for scaling AMQ 7 brokers easily.

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SystemTap’s BPF Backend Introduces Tracepoint Support

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By Aaron Merey

This blog is the third in a series on stapbpf, SystemTap’s BPF (Berkeley Packet Filter) backend. In the first post, Introducing stapbpf – SystemTap’s new BPF backend, I explain what BPF is and what features it brings to SystemTap. In the second post, What are BPF Maps and how are they used in stapbpf, I examine BPF maps, one of BPF’s key components, and their role in stapbpf’s implementation.

In this post, I introduce stapbpf’s recently added support for tracepoint probes. Tracepoints are statically-inserted hooks in the Linux kernel onto which user-defined probes can be attached. Tracepoints can be found in a variety of locations throughout the Linux kernel, including performance-critical subsystems such as the scheduler. Therefore, tracepoint probes must terminate quickly in order to avoid significant performance penalties or unusual behavior in these subsystems. BPF’s lack of loops and limit of 4k instructions means that it’s sufficient for this task.

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Red Hat OpenShift Container Platform Load Testing Tips

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By Supannee Amnajmongkol

A large bank in the Association of Southeast Asian Nations (ASEAN) plans to develop a new mobile back-end application using microservices and container technology. They expect the platform to be able to support 10,000,000 customers with 5,000 TPS. They decided to use Red Hat OpenShift Container Platform (OCP) as the runtime platform for this application. To ensure that this platform is able to support their throughput requirements and future growth rate, they have performed internal load testing with their infrastructure and mock-up services. This article will share the lessons learned load testing Red Hat OpenShift Container Platform.

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Create a scalable REST API with Falcon and RHSCL

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By Shane Boulden

APIs are critical to automation, integration and developing cloud-native applications, and it’s vital they can be scaled to meet the demands of your user-base. In this article, we’ll create a database-backed REST API based on the Python Falcon framework using Red Hat Software Collections (RHSCL), test how it performs, and scale-out in response to a growing user-base.

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What are BPF Maps and how are they used in stapbpf

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By Aaron Merey

Compared to SystemTap’s default backend, one of stapbpf’s most distinguishing features is the absence of a kernel module runtime. The BPF machinery inside the kernel instead mostly handles its runtime. Therefore it would be very helpful if BPF provided us with a way for states to be maintained across multiple invocations of BPF programs and for userspace programs to be able to communicate with BPF programs. This is accomplished by BPF maps. In this blog post, I will introduce BPF maps and explain their role in stapbpf’s implementation.

What are BPF maps?

BPF maps are essentially generic data structures consisting of key/value pairs. They are created from userspace using the BPF system call, which returns a file descriptor for the map. The key size and value size are specified by the user, allowing for the storage of key/value pairs with arbitrary types. Once a map is created, elements can be accessed from userspace using the BPF system call. Maps are automatically deallocated once the user process that created the map terminates (although it is possible to force the map to persist longer than this process). Stapbpf uses the following function to create new BPF maps.

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Configuring mKahaDB persistence storage for ActiveMQ

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By Chandra Shekhar Pandey

In this post, I wanted to address how to configure mKahaDB persistence storage on ActiveMQ for better management and reducing disk usage.

Default configured KahaDB persistence adapter works well when all the destinations (queues/topics) being managed by the broker have similar performance. However, an enterprise solution where several third parties are involved is never the case.

There are multiple queues or topics and different consumers or listeners listening to these queues/topics. Some consumers might be slower than other consumers. This will grow the message store’s disk usage rapidly. Due to this situation and being single KahaDB all store destinations might perform slow.

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Using New Relic in Red Hat Mobile Node.js Applications

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By Evan Shortiss

Introduction

New Relic is an application-monitoring platform that provides in-depth analytics and analysis for applications regardless of the type of environment where they are deployed, or as New Relic put it themselves:

“Gain end-to-end visibility across your customer experience, application performance, and dynamic infrastructure with the New Relic Digital Intelligence Platform.” – New Relic

You might ask why there’s a use for New Relic’s monitoring capabilities when Red Hat Mobile Application Platform (RHMAP) and OpenShift Container Platform both offer insights into the CPU, Disk, Memory, and general resource utilization of your server-side applications. While these generic resource reports are valuable, they might not offer the detail required to debug a specific issue. Since New Relic is built as an analytics platform from the ground up it is capable of providing unique insights into the specific runtime of your applications. For example, the JavaScript code deployed in Node.js applications is run using the V8 JavaScript engine which has a life-cycle that can have a significant impact on the performance of your application depending on how you’ve written it. Utilizing New Relic’s Node.js module provides a real-time view of V8 engine performance and how they might be affecting the performance of your production application. By using this data, you can refine your application code to reduce memory usage, which in turn can free CPU resources due to less frequent garbage collections. Neat!

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Profiling NodeJS applications with Linux Performance Tools

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By Cesar Valdez

Using Linux Perf Tools

The Performance Analysis Tool for Linux (perf) is a powerful tool to profile applications. It works by using a mix of hardware counters (is fast) and software counters, all provided by the Linux Performance Counter (LPC) subsystem that takes charge of the complex task of wrapping the CPU counters for the different type of CPUs. So you can have access to a very efficient way to get information of running processes through their C API or a convenient command in this case (perf).

This command gives you access to a great variety of system and process level events but in this entry, I will use it to investigate CPU bounded issues.  

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