Docker Compose Research Articles

A Reproducible Tutorial on Reproducibility in Database Systems Research

Reproducibility is a key aspect of the scientific method, and it is essential for building trust in the results of research. This tutorial aims to provide concrete guidance on how to leverage containerized reproducibility using Docker for database systems research. In this tutorial, we present a step-by-step guide on how to prepare a Docker-based artifact for an experiment. We will cover topics such as Dockerfiles, Docker images, Docker Compose, automation using Python, Bash, and Make, and also artifact documentation and packaging best practices. The tutorial itself is a reproducible artifact, and we provide a public GitHub repository with all the code and examples used in the tutorial. This repository can serve as a starting point to prepare artifacts for experiments and publications.

Patterns of multi-container composition for service orchestration with Docker Compose

Patterns of multi-container composition for service orchestration with Docker Compose

Deploying the ATLAS Metadata Interface (AMI) stack in a Docker Compose or Kubernetes environment

ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. This paper describes how a renewed architecture and integration with modern technologies ease the usage and deployment of a complete AMI stack. It describes how to deploy AMI in a Docker Compose or Kubernetes environment, with a particular emphasis on the registration of existing databases, the addition of more metadata sources, and the generation of high level Web search interfaces using dedicated wizards.

Containers-Based Network Services Deployment: A Practical Approach

In recent years, virtualizing network servicesand functions has enabled optimizing hardware resources onresource-constrained devices, such as CPU, memory, and storage.Traditional virtualization is achieved through virtual machinesusing a layer known as a hypervisor. While this form of virtualization offers advantages such as scalability and portability, it has disadvantages in terms of performance compared to non-virtualized deployments. In this context, alternative virtualization technologies like containers allow virtualization on the same physical infrastructure, improving overall performance, portability, and service scalability. This paper implements the deployment of network services on the Raspberry Pi development platform, which has limited resources. This is achieved through a multi-container virtualization solution using the Docker Compose tool, based on Docker containerization technology. Finally, a performance analysis of the implemented virtualization solution is conducted in terms of resource utilization by each service.

Integration of SonarQube, The Quality Inspector for GO & Docker Compose

Project success at any company is mostly dependent on code quality and security. Continuous evaluation of code quality is necessary to attain peak efficiency and minimize the likelihood of mistakes. Nevertheless, without the use of a static code analysis tool, achieving complete code visibility might be difficult. The SonarQube platform will be of interest to your company if your software development team is looking to enhance code quality. Nonetheless, developers must make sure that coding standards are followed at all times if their teams employ a CI/CD pipeline to update the code base. This article will define SonarQube, highlight some of its best features, and discuss why companies should use it for code analysis, and integrate it with a GO project and Docker Compose.

DESIGN OF GENERATION SYSTEM BASED ON ARTIFICIAL INTELLIGENCE ALGORITHM. PART 1

The development of modern tools and methods of artificial intelligent provides a wide range of functionality for various scientific and applied tasks, and also allows obtaining new results. A question arises about how to apply elements of artificial intelligence to obtain new combinations and connections obtained by generation. As a purpose of the article is considered applying a neural network technology for the formation of new, unique recipes for cocktails with the aim of demonstrating the effectiveness of implementation and verification methods and models of artificial intelligence. To solve generation problems according to ingredient recipes, in the article all stages of the technological process are analyzed from data collection, analysis, preprocessing to the selection of a mathematical algorithm and modeling of the trained system. Each stage of the work contains a separate description of the steps necessary to solve the task. The machine learning algorithm has the ability to process thousands of examples to find a certain pattern of combinations of ingredients. And important attention is paid to the formation of a training date.
 At the stage of data collection, individuals and main problems are considered, which are checked during the work of the parser. The important part is the partial use of encrypted technologies when using Internet resources and reading the DOM tree with the code of the HTML page. The paper shows settings for the automated algorithm of information collection. For the parser development, a tool for rapid project development and effective management as Docker and Docker Compose add-ons were used. Completed the stage with the acquisition of the data set and the data for neural network models has been analyzed. The stage consists in preprocessing, decoding and compositions collected data in relevant tables. During the automated data collection process, information with significant noise and redundant elements is processed. Considerable attention is paid to the process of cleaning and preparation of useful information, because the purity of the data and their completeness in the most cases determine the quality of the mathematical model and the modeling process for finding new laws and combinations in generation.

Exploring Cancer Incidence, Risk Factors, and Mortality in the Lleida Region: Interactive, Open-source R Shiny Application for Cancer Data Analysis

The cancer incidence rate is essential to public health surveillance. The analysis of this information allows authorities to know the cancer situation in their regions, especially to determine cancer patterns, monitor cancer trends, and help prioritize the allocation of health resource. This study aimed to present the design and implementation of an R Shiny application to assist cancer registries conduct rapid descriptive and predictive analytics in a user-friendly, intuitive, portable, and scalable way. Moreover, we wanted to describe the design and implementation road map to inspire other population registries to exploit their data sets and develop similar tools and models. The first step was to consolidate the data into the population registry cancer database. These data were cross validated by ASEDAT software, checked later, and reviewed by experts. Next, we developed an online tool to visualize the data and generate reports to assist decision-making under the R Shiny framework. Currently, the application can generate descriptive analytics using population variables, such as age, sex, and cancer type; cancer incidence in region-level geographical heat maps; line plots to visualize temporal trends; and typical risk factor plots. The application also showed descriptive plots about cancer mortality in the Lleida region. This web platform was built as a microservices cloud platform. The web back end consists of an application programming interface and a database, which NodeJS and MongoDB have implemented. All these parts were encapsulated and deployed by Docker and Docker Compose. The results provide a successful case study in which the tool was applied to the cancer registry of the Lleida region. The study illustrates how researchers and cancer registries can use the application to analyze cancer databases. Furthermore, the results highlight the analytics related to risk factors, second tumors, and cancer mortality. The application shows the incidence and evolution of each cancer during a specific period for gender, age groups, and cancer location, among other functionalities. The risk factors view permitted us to detect that approximately 60% of cancer patients were diagnosed with excess weight at diagnosis. Regarding mortality, the application showed that lung cancer registered the highest number of deaths for both genders. Breast cancer was the lethal cancer in women. Finally, a customization guide was included as a result of this implementation to deploy the architecture presented. This paper aimed to document a successful methodology for exploiting the data in population cancer registries and propose guidelines for other similar records to develop similar tools. We intend to inspire other entities to build an application that can help decision-making and make data more accessible and transparent for the community of users.

Modeling Different Deployment Variants of a Composite Application in a Single Declarative Deployment Model

For automating the deployment of composite applications, typically, declarative deployment models are used. Depending on the context, the deployment of an application has to fulfill different requirements, such as costs and elasticity. As a consequence, one and the same application, i.e., its components, and their dependencies, often need to be deployed in different variants. If each different variant of a deployment is described using an individual deployment model, it quickly results in a large number of models, which are error prone to maintain. Deployment technologies, such as Terraform or Ansible, support conditional components and dependencies which allow modeling different deployment variants of a composite application in a single deployment model. However, there are deployment technologies, such as TOSCA and Docker Compose, which do not support such conditional elements. To address this, we extend the Essential Deployment Metamodel (EDMM) by conditional components and dependencies. EDMM is a declarative deployment model which can be mapped to several deployment technologies including Terraform, Ansible, TOSCA, and Docker Compose. Preprocessing such an extended model, i.e., conditional elements are evaluated and either preserved or removed, generates an EDMM conform model. As a result, conditional elements can be integrated on top of existing deployment technologies that are unaware of such concepts. We evaluate this by implementing a preprocessor for TOSCA, called OpenTOSCA Vintner, which employs the open-source TOSCA orchestrators xOpera and Unfurl to execute the generated TOSCA conform models.

Visual notations in container orchestrations: an empirical study with Docker Compose

Container orchestration tools supporting infrastructure-as-code allow new forms of collaboration between developers and operatives. Still, their text-based nature permits naive mistakes and is more difficult to read as complexity increases. We can find few examples of low-code approaches for defining the orchestration of containers, and there seems to be a lack of empirical studies showing the benefits and limitations of such approaches. We hypothesize that a complete visual notation for Docker-based orchestrations could reduce the effort, the error rate, and the development time. Therefore, we developed a tool featuring such a visual notation for Docker Compose configurations, and we empirically evaluated it in a controlled experiment with novice developers. The results show a significant reduction in development time and error-proneness when defining Docker Compose files, supporting our hypothesis. The participants also thought the prototype easier to use and useful, and wanted to use it in the future.

Application of Blockchain Technology in Water Rights Trading in the Irrigation Area under the Internet-of-Things Environment

Water rights trading is an important way to solve the shortage of water resources. Combined with the characteristics of water resources and the requirements of the market economy, the organic combination of the government and the market should be strengthened. Through the establishment of a water rights system and a water rights trading market, the implementation of water rights trading will be promoted. Due to the decentralized and distributed structure, blockchain technology greatly reduces its intermediary cost in the application process. The tamper-resistant timestamp feature can realize data tracking and information anticounterfeiting, and flexible programming attributes can promote the specification of market order. Firstly, the reasons for water rights trading are studied, and the water rights trading system is analyzed. It is found that the system has the problems of the cumbersome transaction process and high transaction costs. According to the characteristics of water rights trading, the alliance chain Hyperledger Fabric is selected as the underlying network system of the trading system. Secondly, combined with water rights trading, Hyperledger Fabric multichannel technology is used to design a three-channel accounting method, and the chain code for water rights trading is designed and implemented based on the Go language. CouchDB is adopted to store key-value data, and Ubuntu, Docker, and Docker Compose are used to construct an operating environment of the chain code. Finally, the proposed system is tested. The test findings reveal that the distributed water volume of experimental area 1 is 41.8368 million m3, the current water volume is 23.831 million m3, and the quota management water volume is 20.432 million m3. The distributed water volume in area 2 is 40.4605 million m3, the current water volume is 4317.64 m3, and the quota management water volume is 77.4795 million m3. The distributed water volume in area 3 is 65.6928 million m3, the current water volume is 77.472 million m3, and the quota management water volume is 64.412 million m3. The water rights trading system based on Hyperledger Fabric can record each transaction data, which can be stored in each node, and the data stored in each node are the same. The proposed scheme provides a certain reference for the application of blockchain technology in water rights trading in irrigation areas.

LAMP Stack Deployment using Docker Compose

Small businesses typically use a single cloud server to host multiple projects, and their databases are sometimes hosted on the same server. As a result, all projects are hosted in the same environment. This will cause problems in the future, such as the inability to upgrade the server's environment because all projects are dependent on the same environment; in the future, that environment support will be discontinued; or in the future, if the database fails, it will affect all projects that hosted on the server.

A study of how Docker Compose is used to compose multi-component systems

A study of how Docker Compose is used to compose multi-component systems

Introduction to MDSplus using Docker

• Docker is a tool used in modern software engineering. • Docker is an ecosystem of tools that allow for distributing and running software in isolated contexts. • Docker Compose can be used to document and deploy infrastructure in a file. • MDSplus can be used with Docker to test and deploy MDSip services. • MDSplus can be used with Docker to test old/new versions of MDSplus tools. • Docker has limitations that impact its use with MDSplus. With increased use of MDSplus comes an influx of new users. With them comes a need for more and better ways to learn the suite of tools that is MDSplus. The MDSplus team continually evaluates new technologies to improve our software and user experience. To this end, we investigated Docker to determine if and how it could help new users understand MDSplus, make MDSplus easier to install, and allow us to easily test old/new versions. To achieve this, a set of Docker images and instructions have been developed. This paper will provide an overview of MDSplus, and detail the methods to create and use the Docker images. Additionally, we will explore the limitations of such an approach, and the recommended applications. The project where these Docker Images were built, along with the Demo is here: https://github.com/WhoBrokeTheBuild/DockerizedMDSplus https://hub.docker.com/r/whobrokethebuild/mdsplus The now official Docker Images are available here: https://github.com/MDSplus/Docker https://hub.docker.com/r/mdsplus/mdsplus

Fine‐grained management of cloud‐native applications, based on TOSCA

Containers and container‐based orchestrators allow deploying and managing multi‐service applications over cloud platforms, by coupling the lifecycle of each application service to that of its hosting container. In this use case letter, we demonstrate how the OASIS standard TOSCA and Toskose allow overcoming such a coupling by enabling to exploit Docker containers as system containers where to independently deploy and manage application services. More precisely, we show how TOSCA and Toskose enable a fine‐grained, service‐aware management of a third‐party multi‐service application on top of existing Docker‐based container orchestrators (such as Docker Compose and Kubernetes) and independently of whether the application is deployed on premises or on a public cloud (such as MS Azure).

BIoTA: A Buildout IoT Application Language

Domain-Specific Languages (DSLs) serve a wide variety of purposes in the development of complex applications. The Internet of Things (IoT) brings additional complexity to software development due to its inherent distribution and inclusion of a massive number of heterogeneous devices (sensors and actuators). The process of developing software architectures for IoT involves the interaction of several components that play distinct roles. In this paper, we propose a language called BIoTA (Buildout IoT Application Language), a DSL whose objective is to assist and streamline the building of software architectures for IoT. The specification and implementation of the BIoTA language involve a grammar and a compiler, responsible for syntax and semantic analysis, as well as code generation. They facilitate the formalization of software architectures using automata, which would otherwise be created in an informal and ad-hoc manner to address specific IoT scenarios. We developed an IDE (Integrated Development Environment) capable of reading and creating software architectures using the BIoTA language. With the BIoTA IDE, we demonstrate three examples of software architectures for IoT smart applications in public buildings, irrigation, and parking. The IDE also makes it possible to export architectures to a software distribution package pattern based on containers (Docker Compose) for future deployment.

Разработка мобильного приложения «Open Kazakhstan» на платформе Android

This article discusses the development of the mobile application «Open Kazakhstan» on the Android platform. This is a unique multimedia guide for mobile phones, designed in social and cultural services and tourism for the South Kazakhstan region of the Republic of Kazakhstan. In this article, the author described the conceptual scheme of the «Open Kazakhstan» mobile application, which allows you to save time and money for tourists, find attractions and will become an indispensable assistant while traveling. «Open Kazakhstan» is a geographic information tourism system that was developed in the CRM Docker Compose and WordPress systems. The author, studying the market of mobile applications for tourism, came to the conclusion that the mobile application «Open Kazakhstan» will be in great demand in the Kazakh society.

Pixel: a content management platform for quantitative omics data.

BackgroundIn biology, high-throughput experimental technologies, also referred as “omics” technologies, are increasingly used in research laboratories. Several thousands of gene expression measurements can be obtained in a single experiment. Researchers are routinely facing the challenge to annotate, store, explore and mine all the biological information they have at their disposal. We present here the Pixel web application (Pixel Web App), an original content management platform to help people involved in a multi-omics biological project.MethodsThe Pixel Web App is built with open source technologies and hosted on the collaborative development platform GitHub (https://github.com/Candihub/pixel). It is written in Python using the Django framework and stores all the data in a PostgreSQL database. It is developed in the open and licensed under the BSD 3-clause license. The Pixel Web App is also heavily tested with both unit and functional tests, a strong code coverage and continuous integration provided by CircleCI. To ease the development and the deployment of the Pixel Web App, Docker and Docker Compose are used to bundle the application as well as its dependencies.ResultsThe Pixel Web App offers researchers an intuitive way to annotate, store, explore and mine their multi-omics results. It can be installed on a personal computer or on a server to fit the needs of many users. In addition, anyone can enhance the application to better suit their needs, either by contributing directly on GitHub (encouraged) or by extending Pixel on their own. The Pixel Web App does not provide any computational programs to analyze the data. Still, it helps to rapidly explore and mine existing results and holds a strategic position in the management of research data.

Using Docker Compose for the Simple Deployment of an Integrated Drug Target Screening Platform.

Docker virtualization allows for software tools to be executed in an isolated and controlled environment referred to as a container. In Docker containers, dependencies are provided exactly as intended by the developer and, consequently, they simplify the distribution of scientific software and foster reproducible research. The Docker paradigm is that each container encapsulates one particular software tool. However, to analyze complex biomedical data sets, it is often necessary to combine several software tools into elaborate workflows. To address this challenge, several Docker containers need to be instantiated and properly integrated, which complicates the software deployment process unnecessarily. Here, we demonstrate how an extension to Docker, Docker compose, can be used to mitigate these problems by providing a unified setup routine that deploys several tools in an integrated fashion. We demonstrate the power of this approach by example of a Docker compose setup for a drug target screening platform consisting of five integrated web applications and shared infrastructure, deployable in just two lines of codes.