XDP: From zero to 14 Mpps
In past years, the kernel community has been using different approaches in the quest for ever-increasing networking performance. While improvements have been measurable in several areas, a new wave of architecture-related security issues and related counter-measures has undone most of the gains, and purely in-kernel solutions for some packet-processing intensive workloads still lag behind the bypass solution, namely Data Plane Development Kit (DPDK), by almost an order of magnitude.
But the kernel community never sleeps (almost literally) and the holy grail of kernel-based networking performance has been found under the name of XDP: the eXpress Data Path. XDP is available in Red Hat Enterprise Linux 8 Beta, which you can download and run now.
Continue reading “Achieving high-performance, low-latency networking with XDP: Part I”
A number of the SystemTap script examples in the newly released SystemTap 4.0 available in Fedora 28 and 29 have reduced the amount of time required to convert the scripts into running instrumentation by using the
This article discusses the particular changes made in the scripts and how you might also use this new tapset to make the instrumentation that monitors system calls smaller and more efficient. (This article is a follow-on to my previous article: Analyzing and reducing SystemTap’s startup cost for scripts.)
The key observation that triggered the creation of the
syscall_any tapset was a number of scripts that did not use the
syscall arguments. The scripts often used
syscall.*.return, but they were only concerned with the particular
syscall name and the return value. This type of information for all the system calls is available from the
sys_exit kernel tracepoints. Thus, rather than creating hundreds of kprobes for each of the individual functions implementing the various system calls, there are just a couple of tracepoints being used in their place.
Continue reading “Reducing the startup overhead of SystemTap monitoring scripts with syscall_any tapset”
SystemTap is a powerful tool for investigating system issues, but for some SystemTap instrumentation scripts, the startup times are too long. This article is Part 1 of a series and describes how to analyze and reduce SystemTap’s startup costs for scripts.
We can use SystemTap to investigate this problem and provide some hard data on the time required for each of the passes that SystemTap uses to convert a SystemTap script into instrumentation. SystemTap has a set of probe points marking the start and end of passes from 0 to 5:
- pass0: Parsing command-line arguments
- pass1: Parsing scripts
- pass2: Elaboration
- pass3: Translation to C
- pass4: Compilation of C code into kernel module
- pass5: Running the instrumentation
Articles in this series:
Continue reading “Analyzing and reducing SystemTap’s startup cost for scripts”
Microservices and serverless architectures are being implemented, or are a part of the roadmap, in most modern solution stacks. Given that Java is still the dominant language for business applications, the need for reducing the startup time for Java is becoming more important. Serverless architectures are one such area that needs faster startup times, and applications hosted on container platforms such as Red Hat Openshift can benefit from both fast Java startup time and a smaller Docker image size.
Let’s see how GraalVM can be beneficial for Java-based programs in terms of speed and size improvements. Surely, these gains are not bound to containers or serverless architectures and can be applied to a variety of use cases.
Continue reading “Natively compile Java code for better startup time”
ASP.NET Core is the web framework for .NET Core. Performance is a key feature. The stack is heavily optimized and continuously benchmarked. Kestrel is the name of the HTTP server. In this blog post, we’ll replace Kestrel’s networking layer with a Linux-specific implementation and benchmark it against the default out-of-the-box implementations. The TechEmpower web framework benchmarks are used to compare the network layer performance.
Continue reading “Improving .NET Core Kestrel performance using a Linux-specific transport”
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.
Continue reading “SystemTap’s BPF Backend Introduces Tracepoint Support”
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.
Continue reading “Red Hat OpenShift Container Platform Load Testing Tips”
Last week I presented a talk on the subject of Java Class Metadata at FOSDEM 2018 in the Free Java Room. In my presentation I explained:
- What Java Class Metadata is
- Why it helps to know about it
- What you might do to measure it and reduce the impact of the metadata’s footprint on your Java application
Continue reading “Java Class Metadata: A User Guide”
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.
Continue reading Create a scalable REST API with Falcon and RHSCL