Articles

Creating Homebrew Formulas With GoReleaser

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We chose to use GoReleaser at ObservIQ for our distro of the OpenTelemetry Collector to simplify how we build and support many operating systems and architectures. GoReleaser enables us to build targeting a matrix of GOOS and GOARCH  targets as well as automate creating a wide range of deliverables. The ones we have utilized are building tarballs, nfpm packages, docker images, and Homebrew formula.

For this article, the focus is on the Homebrew Taps capabilities in GoReleaser and our journey using it. Our goal was to make it easy for users to install our software on macOS so that they could easily try it out. We went with Homebrew as it’s familiar to many macOS users and would allow a user to try out our software and remove it just as easily when they are finished.

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Manage Microservices With Docker Compose

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As microservices systems expand beyond a handful of services, we often need some way to coordinate everything and ensure consistent communication (avoid human error). Tools such as Kubernetes or Docker Compose have quickly become commonplace for these types of workloads. Today’s example will use Docker Compose.

Docker Compose is an orchestration tool that manages containerized applications, and while I have heard many lament the complexity of Kubernetes, I found Docker Compose to have some complexities as well. We will work through these along the way and explain how I solved them.

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Experimenting With Unique Constraints in CockroachDB, MongoDB and FerretDB

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Motivation

Once I completed my first two articles, I realized there are a lot of possibilities exposed by proxying MongoDB collections through FerretDB backed by CockroachDB. CockroachDB has unique data domiciling capabilities available through multi-region abstractions, inverted and partial indexes, computed columns and of course strong consistency. Today, we’re going to discuss unique constraints in MongoDB and CockroachDB.

High-level Steps

  • Start a 9-node multi-region cluster (CockroachDB Dedicated)
  • Start FerretDB (Docker)
  • Unique Indexes
  • Considerations
  • Conclusion

Step-by-step Instructions

Start a 9-Node Multi-region Cluster (Cockroachdb Dedicated)

I am going to use the same CockroachDB Dedicated cluster from the previous article. Please refer to the previous article for the detailed steps. You can get a 30-day trial of CockroachDB Dedicated following this link.

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From IBM Integration Bus to App Connect Enterprise Containers

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This article is part of a series. For the previous article, see Moving an App Connect Flow Using MQ onto Containers. 

One of the most common integration points is a database, and App Connect is well suited to connecting to a significant variety of datastores. One of the most common protocols used to connect to databases is ODBC, so that is the example that we will work through in this post. 

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Renovate, a Dependabot Alternative

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I won’t introduce Dependabot. Lots and lots of developers use it daily on GitHub. I do use it as well. However, it suffers from two drawbacks:

  • While it’s perfectly integrated with GitHub, integrations with other platforms are less seamless.

  • It’s limited in the list of ecosystems it supports For example, I generally use Docker Compose files for my demos. When necessary, I use Kubernetes. Dependabot supports none.

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Scaling a Node JS Application

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Whenever we build an awesome product we first build it standalone but sooner or later it attracts more users and then our minds start thinking about how to accommodate more users and there comes the need of scaling the application. Generally scaling means providing more elasticity to the application so that it can sustain the high influx of users and run smoothly without any glitches.

Software scalability is an attribute of a tool or a system to increase its capacity and functionalities based on its users’ demands. Scalable software can remain stable while adapting to changes, upgrades, overhauls, and resource reduction

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Using Embedded Milvus to Instantly Install and Run Milvus With Python

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Milvus is an open-source vector database for AI applications. It provides a variety of installation methods, including building from source code and installing Milvus with Docker Compose/Helm/APT/YUM/Ansible. Users can choose one of the installation methods depending on their operating systems and preferences. However, there are many data scientists and AI engineers in the Milvus community who work with Python and yearn for a much simpler installation method than the currently available ones.

Therefore, we released embedded Milvus, a user-friendly Python version, along with Milvus 2.1 to empower more Python developers in our community. This article introduces what embedded Milvus is and provides instructions on how to install and use it.

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Three easy ways to run Kafka without Zookeeper

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There has been a couple of years since the announcement of the removal of Apache Zookeeper as a dependency to manage Apache Kafka metadata. Since version 2.8, we now can run a Kafka cluster without Zookeeper. This article will go over three easy ways to get started with a single node cluster using containers.

Control and data planes

Apache Kafka implements independent control and data planes for its clusters. The control plane manages the cluster, keeps track of what brokers are alive, and takes action when the set changes. Meanwhile, the data plane consists of the features required to handle producers and consumers and their records. In the previous iterations, Zookeeper was the cluster component that held most of the implementation of the control plane. 

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Simplifying “No Data” Applications

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What Is a “No Data” Application

There are a lot of data-driven lines of business applications that will never have a million rows before they get replaced by something else. I call these « no data » applications because they have so little data the database server will never require many optimizations if any. The default settings are good enough.

Postgres can find any row out of a million on your corporate craptop in a relatively slow docker for windows container in 2 ms with any reasonable index. Oddly enough, it will get much faster performance in the cloud, where the vendor has undoubtedly optimized the database code for their particular infrastructure.

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SonarQube Analysis With Ginkgo on Mac

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Ginkgo is the testing framework of choice for many projects developed in Golang. Here is an example setup for SonarQube project analysis.

Prerequisites

  • SonarQube server installation: For the purpose of the tutorial I have a docker image running on my machine on http://localhost:9000.
  • SonarQube project: For the purpose of the tutorial I have created a project with project key com.bdpanajotova.golang-sonar-example and name Golang Sonar Example.
  • Golang project for analysis with Ginkgo tests: Here is the example project I use in GitHub.

Here is the documentation for the fast local setup of SonarQube.

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