Red Hat Universal Base Images for Docker users

Red Hat Universal Base Images for Docker users

Red Hat Universal Base Images (UBIs) allow developers using Docker on Windows and Mac platforms to tap into the benefits of the large Red Hat ecosystem. This article demonstrates how to use Red Hat Universal Base Images with Docker from a non-Red Hat system, such as a Windows or Mac workstation.

Red Hat Enterprise Linux and Docker

When Red Hat Enterprise Linux (RHEL) 8 was released almost a year ago, and it came with lots of new features related to containers. The biggest ones were the new container tools (Podman, Buildah, and skopeo) and the new Red Hat Universal Base Images. There was also confusion because RHEL 8 dropped support for the Docker toolset. Some developers thought that they could not work with Docker anymore, and had to either migrate to a Red Hat-ecosystem Linux system such as CentOS or stay away from Red Hat customers.

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Ceph storage monitoring with Zabbix

Ceph storage monitoring with Zabbix

Storage prices are decreasing, while business demands are growing, and companies are storing more data than ever before. Following this growth pattern, demand grows for monitoring and data protection involving software-defined storage. Downtimes have a high cost that can directly impact business continuity and cause irreversible damage to organizations. Aftereffects include loss of assets and information; interruption of services and operations; law, regulation, or contract violations; along with the financial impacts from losing customers and damaging a company’s reputation.

Gartner estimates that a minute of downtime costs enterprise organizations $5,600, and an hour costs over $300,000.

On the other hand, in a DevOps context, it’s essential to think about continuous monitoring, which is a proactive approach to monitoring throughout the full application’s life cycle and that of its components. This approach helps identify the root cause of possible problems and then quickly and proactively prevent performance issues or future outages. In this article, you will learn how to implement Ceph storage monitoring using the enterprise open source tool Zabbix.

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Kogito 0.8.0 features online editors and cloud-native business automation

Kogito 0.8.0 features online editors and cloud-native business automation

Kogito is a cloud-native business automation solution that offers a powerful, developer-friendly experience. Based on production-tested open source projects Drools and jBPM, Kogito has business rules and processes down to a science. Kogito also aligns with popular lightweight runtimes such as Quarkus and Spring Boot to support developers building business-driven applications.

This article is an overview of the new enhancements for Kogito 0.8.0, which was released on March 10, 2020.

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Testing memory-based horizontal pod autoscaling on OpenShift

Testing memory-based horizontal pod autoscaling on OpenShift

Red Hat OpenShift offers horizontal pod autoscaling (HPA) primarily for CPUs, but it can also perform memory-based HPA, which is useful for applications that are more memory-intensive than CPU-intensive. In this article, I demonstrate how to use OpenShift’s memory-based horizontal pod autoscaling feature (tech preview) to autoscale your pods if the demands on memory increase. The test performed in this article might not necessarily reflect a real application. The tests only aim to demonstrate memory-based HPA in the simplest way possible.

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Cross-language link-time optimization using Red Hat Developer Tools

Cross-language link-time optimization using Red Hat Developer Tools

Several months ago, the LLVM project blog published an article, Closing the gap: cross-language LTO between Rust and C/C++. In it, they explained that link-time optimization can improve performance by optimizing throughout the whole program, such as inlining function calls between different objects. Since Rust and Clang both use LLVM for code generation, we can even achieve this benefit across different programming languages.

In Red Hat Developer Tools, we have the Rust and LLVM Toolsets, which can easily be used together for cross-language link-time optimization (LTO), so let’s try it out.

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How to use the new Kafka Client API for Kafka-specific message properties in Open Liberty 20.0.0.3

How to use the new Kafka Client API for Kafka-specific message properties in Open Liberty 20.0.0.3

In Open Liberty 20.0.0.3, you can now access Kafka-specific properties such as the message key and message headers, rather than just the message payload, as was the case with the basic MicroProfile Reactive Messaging Message API. Also, you can now set the SameSite attribute in the session cookie, the LTPA, and JWT cookies as well as in application-defined cookies.

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Extending gdbserver to support an strace client

Extending gdbserver to support an strace client

The strace command traces system calls and signals, deciding them and their corresponding arguments into a symbolic form. A frequent debugging request from developers is the ability to allow strace to trace system calls for a program that is also being debugged by GDB, like this:

% gdb --args test-program
(gdb) b main
Breakpoint 1 at 0x40128e: file test-program.c, line 22.
(gdb) run
Starting program: test-program
Breakpoint 1, main (argc=3, argv=0x7fffffffdb98) at test-program.c:22
22 int thread_count = 2;
(gdb)

In another terminal window, we invoke strace on the same process GDB is debugging:

% strace -p $(pgrep -f test-program)
strace: attach: ptrace(PTRACE_SEIZE, 27882): Operation not permitted

The culprit here is that the ptrace system call, which is used by both GDB and strace to control the execution of programs, and does not allow both strace and GDB to control the same process.

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Possible issues with debugging and inspecting compiler-optimized binaries

Possible issues with debugging and inspecting compiler-optimized binaries

Developers think of their programs as a serial sequence of operations running as written in the original source code. However, program source code is just a specification for computations. The compiler analyzes the source code and determines if changes to the specified operations will yield the same visible results but be more efficient. It will eliminate operations that are ultimately not visible, and rearrange operations to extract more parallelism and hide latency. These differences between the original program’s source code and the optimized binary that actually runs might be visible when inspecting the execution of the optimized binary via tools like GDB and SystemTap.

To aid with the debugging and instrumentation of binaries the compiler generates debug information to map between the source code and executable binary. The debug information includes which line of source code each machine instruction is associated with, where the variables are located, and how to unwind the stack to get a backtrace of function calls. However, even with the compiler generating this information, a number of non-intuitive effects might be observed when instrumenting a compiler-optimized binary:

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How to customize Fedora CoreOS for dedicated workloads with OSTree

How to customize Fedora CoreOS for dedicated workloads with OSTree

In part one of this series, I introduced Fedora CoreOS (and Red Hat CoreOS) and explained why its immutable and atomic nature is important for running containers. I then walked you through getting Fedora CoreOS, creating an Ignition file, booting Fedora CoreOS, logging in, and running a test container. In this article, I will walk you through customizing Fedora CoreOS and making use of its immutable and atomic nature.

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