[{"body":"","link":"https://www.oss-db.glossvation.com/en/post/","section":"post","tags":["index"],"title":"Article"},{"body":"","link":"https://www.oss-db.glossvation.com/en/categories/","section":"categories","tags":null,"title":"Categories"},{"body":"","link":"https://www.oss-db.glossvation.com/en/","section":"","tags":null,"title":"GlossVation OSS DB"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/log/","section":"tags","tags":null,"title":"Log"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/oss/","section":"tags","tags":null,"title":"OSS"},{"body":"","link":"https://www.oss-db.glossvation.com/en/categories/oss/","section":"categories","tags":null,"title":"OSS"},{"body":"","link":"https://www.oss-db.glossvation.com/en/series/oss-db/","section":"series","tags":null,"title":"OSS DB"},{"body":"","link":"https://www.oss-db.glossvation.com/en/series/","section":"series","tags":null,"title":"Series"},{"body":"Signoz Signoz is an open-source monitoring tool that provides distributed tracing and metrics. It allows tracking of distributed system-wide tracing, metrics, alerts, and log management. This enables visualization of the overall system performance and facilitates quick identification and resolution of issues.\nKey features of Signoz include:\nDistributed tracing: Tracks how requests or transactions move across multiple microservices. Metrics: Monitors the performance of services and resources, assisting in issue identification and resolution. Alerts: Set user-defined thresholds to receive notifications when issues occur. Log management: Stores event logs that can be used for debugging and issue resolution. Built on popular open-source tools like Jaeger and Prometheus, Signoz balances ease of use and flexibility. It also supports integration with container orchestration platforms like Kubernetes and Docker.\nSignoz's GitHub repository can be found at the following URL: https://github.com/signoz/signoz\nBy using Signoz, collecting distributed system tracing and metrics, monitoring issues, and resolving them becomes easier. Through visualization and analysis of the entire system, it is possible to improve performance and ensure system stability.\nUse Cases for Signoz OSS Signoz OSS is an open-source monitoring tool that supports monitoring and troubleshooting of distributed systems. Below, we explain in detail how Signoz OSS is used in certain software architectures, why it is used in those software architectures.\nUse Cases For example, if a company adopts a microservices architecture, multiple services collaborate to provide services. In such cases, real-time monitoring of the performance and errors of each service is necessary, and quick response is required when issues arise. Signoz OSS is very useful for companies adopting microservices architecture as it can effectively and rapidly monitor the performance of the entire distributed system in such situations.\nUse in Software Architectures Signoz OSS is a monitoring tool based on distributed tracing. Distributed tracing visualizes the flow of requests between services and components cooperating to provide services, enabling the identification of delays or errors. Therefore, it is suitable for use in systems that utilize microservices architecture, containers like Docker, and Kubernetes.\nPurpose of Use Companies that adopt microservices architecture must monitor and troubleshoot services that collaborate to provide services, making monitoring crucial. By using Signoz OSS, visualizing the flow of requests between services in real-time and accurately and swiftly identifying the causes of delays and errors become possible. This helps maintain service quality and reduce response times in case of failures.\nThe Signoz source code includes the following key packages:\nagent:\nThis package contains the code of agents running on systems or servers, collecting real-time metrics and trace data and sending it to the Signoz platform. api:\nThis package contains the code of Signoz's API server, providing endpoints for users to customize dashboards and query data. frontend:\nThis package contains the code of Signoz's frontend application, offering an interface for users to view visualization dashboards and configure alerts. storage:\nThis package contains the code of storage engines used by Signoz to persist data. Currently, PostgreSQL is supported, but other database engines may be supported in the future. utils:\nThis package contains various utility functions and tools used throughout Signoz. This includes data conversion and processing, error handling, and configuration management. These packages provide the necessary components to realize the key functionalities of the Signoz project. Each package is appropriately divided to ensure code maintainability and extensibility. The Signoz source code is available at https://github.com/signoz/signoz, so those interested can check for more details.\n","link":"https://www.oss-db.glossvation.com/en/post/signoz-oss-for-observability-platform/","section":"post","tags":["OSS","Log"],"title":"signoz OSS for observability platform"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/","section":"tags","tags":null,"title":"Tags"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/go/","section":"tags","tags":null,"title":"Go"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/p2p/","section":"tags","tags":null,"title":"P2P"},{"body":"About Syncthing Syncthing is an open-source file synchronization software used to synchronize files between different devices. Data transfer over the network is encrypted, ensuring privacy protection.\nKey Features Cross-Platform: Works on various platforms such as Windows, Mac, Linux, Android, etc. Real-Time Sync: Changes to files are automatically synchronized, allowing for the sharing of latest data without delays. Monitoring: Constantly monitors specified folders for changes and synchronizes them. Versioning: Allows for rollback to older versions and access to past states of files. File Sharing: Enables sharing files with other users and collaborating on tasks. How to Use Download the latest version of Syncthing from Github. Install Syncthing on each device and configure settings. Specify the folders you want to sync and start synchronization with other devices. Any changes or updates to files between devices will be synchronized in real-time by Syncthing. Github URL: https://github.com/syncthing/syncthing\nBy using Syncthing, you can efficiently synchronize files between multiple devices, ensuring secure file sharing, data restoration, and various other functionalities while prioritizing privacy.\nUse Cases of Syncthing OSS Syncthing OSS is open-source software designed for file and data synchronization. Here are some examples of its use cases:\nPersonal File Sync: Utilize Syncthing to automatically sync files across multiple devices. For example, sync files between your home PC, work laptop, smartphone, etc., to keep up-to-date with the latest information.\nCollaborating as a Team: By using Syncthing, team members can share multiple files in real-time, ensuring immediate synchronization across all devices. Edit or update files on any device and have the changes reflected instantly for the entire team.\nSecure File Sharing: With support for end-to-end encryption, Syncthing ensures safe transfer of files. Share sensitive information or confidential data securely using Syncthing for peace of mind.\nSoftware Architecture Syncthing adopts a P2P (Peer to Peer) architecture where devices act as peers on the network, enabling direct data transfer between them. This architecture facilitates fast and efficient file synchronization without the need for a central server.\nBeing serverless, Syncthing balances communication among devices, enhancing reliability and increasing overall system fault tolerance. Users have control over their data and can ensure privacy, enabling secure file sharing practices.\nReasons to Use Syncthing OSS The main reasons for using Syncthing OSS include:\nSecurity: Supporting end-to-end encryption ensures secure data transfer. Users can manage their data and prevent unauthorized access.\nOpen Source: Syncthing being open-source allows users to review software mechanisms and code freely. Community monitoring and improvements make Syncthing a highly reliable solution.\nFlexibility: Available across various platforms, Syncthing allows customization to suit individual environments. Users can configure settings for various purposes as needed.\nSyncthing OSS offers security, open-source nature, and flexibility, making it a reliable solution widely used for file synchronization and sharing needs.\nAbout Syncthing Source Code Packages cmd The cmd package defines Syncthing's Command Line Interface (CLI). It includes functionalities such as Syncthing startup, configuration loading, and log settings.\nlib The lib package houses Syncthing's core function: file synchronization processes. This package includes file synchronization algorithms, network communication, data encryption, and database management.\nmodel In the model package, definitions for Syncthing's data structures and models are included. Models such as nodes, repositories, and file information are defined and used for data handling.\nprotocol The protocol package implements Syncthing's protocol specifications for communication and data sharing between different nodes.\ngui The gui package implements Syncthing's Graphical User Interface (GUI). It includes GUI features for managing devices, sharing settings, and monitoring synchronization status.\nauto The auto package manages Syncthing's automatic update functionality. It handles new release detection, automatic downloads, and installation of updates.\nupdater The updater package controls Syncthing's software update process, including update checks, downloads, installations, and platform-specific update procedures.\nAbove are descriptions of each package in Syncthing's source code. Detailed implementations corresponding to each package can be found on the GitHub repository https://github.com/syncthing/syncthing](https://github.com/syncthing/syncthing.\nYear of initial release for Syncthing OSS: 2013 Contributor information: Approximately 160 current contributors, with over 400 contributors in total. Current Github stars count: Approximately 44,000 stars Syncthing, released in 2013 as open-source file synchronization software, continues to be widely used due to its security features and privacy-centric approach to file synchronization. The project boasts around 160 active contributors and over 400 contributors in total, with roughly 44,000 stars on GitHub.\n","link":"https://www.oss-db.glossvation.com/en/post/syncthing-oss-for-file-synchronization/","section":"post","tags":["OSS","Go","P2P"],"title":"Syncthing OSS for file synchronization"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/distributed/","section":"tags","tags":null,"title":"Distributed"},{"body":"OpenSearch OpenSearch is a distribution for search engines and log analysis, and is an open-source project forked from Elasticsearch and Kibana.\nOpenSearch can be used for various purposes, such as implementing search functionality within websites or applications, log aggregation and analysis, monitoring, security auditing, and more, being utilized in many scenarios.\nThe OpenSearch project is actively developed and supported by the community. It provides a wide range of plugins and extensions, allowing users to customize it according to their needs.\nThe GitHub repository for OpenSearch can be found at the following URL: https://github.com/opensearch-project/OpenSearch\nOpenSearch OSS is an open-source software that combines search engine and analytics engine functionalities, developed as a fork of Elasticsearch. It is used in various software architectures for purposes such as:\nSearch engine functionalities: OpenSearch OSS is used to implement search functionalities in various web applications like corporate websites or online shops. It can process large amounts of data quickly and execute flexible search queries, allowing users to find the desired information promptly.\nLog analysis and monitoring: OpenSearch OSS is used to collect and analyze vast amounts of log data such as server logs or application logs. Real-time log monitoring, aggregation, and visualization help in troubleshooting system issues and optimizing resources.\nSecurity information search: OpenSearch OSS is used to manage and search for security information and vulnerabilities in companies. By collecting information from multiple sources and executing appropriate queries, it can be utilized as a decision-making tool for security measures.\nThe benefits of using OpenSearch OSS include high scalability and flexibility. It can adapt flexibly to cloud environments and handle large-scale data processing, allowing for easy adaptation to system growth and changes. Being open-source, users can customize and extend it themselves, enabling the construction of search and analytics systems tailored to their needs.\nOpenSearch OSS, as the open-source version of Elasticsearch, provides a fast and scalable search engine and data processing engine. OpenSearch offers tools and functionalities that enable the search, analysis, and visualization of large amounts of data.\nThe source code for OpenSearch is publicly available on GitHub and is divided into the following packages:\nopensearch-core: Contains the core functionalities of OpenSearch, such as query execution, data retrieval, and distributed processing.\nopensearch-dashboards: Includes features for visualization, dashboard creation, and tools for data visualization and analysis.\nopensearch-job-scheduler: Contains functionalities for scheduling jobs and batch processing to execute regular tasks.\nopensearch-sql: Provides functionalities for executing SQL queries to retrieve data.\nopensearch-security: Contains security-related functionalities, offering access control and authentication features.\nThe development team of OpenSearch regularly updates these packages, working collaboratively with the community to enhance functionalities and fix bugs. OpenSearch is widely used as a highly flexible and customizable search engine suitable for various use cases.\nDistributed Search Engine A distributed search engine refers to a search engine that adopts a mechanism to distribute and store information across multiple computers for processing. This allows for the scalable processing of large amounts of data.\nOverview: A distributed search engine achieves load balancing and fault tolerance by distributing data among multiple nodes. When a user sends a search query, each node simultaneously performs a search and integrates the results to return them. This approach enables fast searching of large-scale data.\nDetails: Distributed search engines are commonly implemented using open-source software (OSS) such as Apache Solr and Elasticsearch. These OSS provide functionalities for distributed processing and indexing, allowing the construction of a flexible and extensible search engine.\nFor example, Apache Solr is a fast and flexible full-text search engine based on Apache Lucene, demonstrating its power in processing large datasets. Elasticsearch, known as a distributed search engine, provides real-time search and analysis functionalities.\nDistributed search engines are utilized to achieve high performance and availability in systems with large amounts of data or complex search requirements. As a result, they are widely used in various fields, including enterprises and research institutions.\n","link":"https://www.oss-db.glossvation.com/en/post/opensearch-of-search-engine/","section":"post","tags":["OSS","Search","Distributed"],"title":"OpenSearch of search engine"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/search/","section":"tags","tags":null,"title":"Search"},{"body":"OpenGFW OpenGFW is a tool designed to bypass internet censorship, primarily used to circumvent China's Great Firewall (GFW). This tool was developed to bypass filtering of TCP/UDP packets, unlike conventional methods such as VPNs or proxies.\nOpenGFW is designed to be user-friendly, with easy setup and usage. Additionally, being open-source and freely available, anyone can use it.\nGithub URL: https://github.com/apernet/OpenGFW\nOpenGFW helps evade restrictions at a network level, allowing users to bypass website blocks and communication monitoring. By using this tool, one can have more freedom to access information on the internet.\nOpenGFW stands out for enabling faster and more effective circumvention compared to other methods like VPNs or proxies. It is highly customizable and allows users to tailor it to their needs.\nIf you feel constrained by internet censorship, consider using OpenGFW to bypass restrictions and achieve unrestricted internet access.\nOpenGFW OSS Use Cases OpenGFW OSS is utilized in web development to enhance security and access control. Common use cases include:\nUse Case 1: Enhancing Web Application Security By utilizing OpenGFW OSS, one can prevent unauthorized access and attacks on web applications. For example, OpenGFW OSS is used to protect web applications from DDoS attacks and SQL injections.\nUse Case 2: Security Management in Microservices Architecture In the increasingly popular microservices architecture, where multiple small services interact, security management is crucial. OpenGFW OSS can restrict communication and access between services, ensuring security.\nUse Case 3: Part of a Multi-Layer Defense System OpenGFW OSS is integrated as part of a multi-layer defense system to enhance security. Combining it with other security measures like firewalls and vulnerability scans can achieve higher security levels.\nReasons for using OpenGFW OSS include its flexibility, open-source nature, and community support that offer information and assistance easily.\nThis outlines detailed use cases and features of OpenGFW OSS.\nThe Firewall feature in OpenGFW OSS monitors network traffic and enforces permission or denial based on set policy rules.\nOpenGFW's source code is primarily divided into different packages each with specific functions:\nengine Contains firewall engine code for packet analysis and policy rule application. drivers Provides drivers for hardware and software, enabling integration in different environments. utils Offers general utility functions for common processing. tests Contains test code to ensure proper functionality of each package. documentation Includes documents detailing OpenGFW usage and development guides. For more information and code details, refer to the GitHub repository (https://github.com/apernet/OpenGFW).\n","link":"https://www.oss-db.glossvation.com/en/post/opengfw-for-security-firewall/","section":"post","tags":["OSS","Security"],"title":"OpenGFW for Security firewall"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/security/","section":"tags","tags":null,"title":"Security"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/docker/","section":"tags","tags":null,"title":"docker"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/kubernetes/","section":"tags","tags":null,"title":"kubernetes"},{"body":"Kubeshark Kubeshark is a tool for visualizing network traffic in Kubernetes clusters. By using Kubeshark, users can analyze the traffic patterns between applications running in a Kubernetes cluster and identify issues or bottlenecks.\nKubeshark provides the following key features:\nVisualization of network traffic within a Kubernetes cluster Interactive dashboard Monitoring and analysis of performance Integration with third-party tools By using Kubeshark, users can visually understand the flow of network traffic within a Kubernetes cluster. This allows users to understand communication patterns of applications and the network situation, which can be useful for troubleshooting and optimization.\nThe Github URL for Kubeshark is as follows:\nhttps://github.com/kubeshark/kubeshark\nKubeshark is a useful tool for visualizing network and performing analysis of performance when operating Kubernetes clusters, making it convenient for developers and system administrators.\nExample of using kubeshark OSS Kubeshark OSS is an open-source tool that enables visualization and monitoring of resources within Kubernetes clusters.\nUse cases for software architecture: Kubeshark OSS is primarily used in systems that adopt microservices architecture. In microservices architecture, multiple small services collaborate to provide functionality, requiring monitoring and debugging in complex environments. Kubeshark OSS helps visualize the performance and errors of each service on a Kubernetes cluster and assists in troubleshooting.\nReasons for use: Resource monitoring: Using kubeshark OSS makes it easier to monitor various resources (Pods, services, networks, etc.) within a Kubernetes cluster. Through real-time data display and graphed information, it becomes possible to identify issues and conduct detailed analysis.\nDebugging support: In cases of challenging debugging, kubeshark OSS supports debugging by enabling request tracing and visualization of logs, allowing tracking of the root cause of problems.\nImproving operational efficiency: Leveraging kubeshark OSS can streamline the operational management of the entire Kubernetes environment. By utilizing anomaly detection and alert features, it is possible to enhance system stability and availability.\nIn this way, kubeshark OSS is used in systems adopting microservices architecture for resource monitoring, debugging support, and operational efficiency improvement.\nPackage structure of kubeshark source code api package:\nDefines the API endpoints for kubeshark and handles interactions with the API server. cmd package:\nProvides the CLI tool for kubeshark. Contains commands to operate kubeshark from the command line. controller package:\nManages the controllers of kubeshark. Contains logic to process requests and return appropriate responses. pkg package:\nSupports the functionality of kubeshark. Contains utility functions and helper functions. web package:\nProvides the web UI for kubeshark. Contains frontend functionality for users to operate kubeshark through a browser. The source code of kubeshark is well-organized into packages based on functionality, with each package serving a specific role.\n","link":"https://www.oss-db.glossvation.com/en/post/kubeshark-for-kubernetes/","section":"post","tags":["OSS","docker","kubernetes"],"title":"kubeshark for Kubernetes"},{"body":"Harbor Harbor is an open-source container image registry project that provides management and security for Docker images.\nOverview Harbor is a platform that facilitates the management of container images within enterprises, providing features for security and policy management. Users can use Harbor to set up private registries, store, manage, and share container images.\nDetails Harbor offers several enterprise features, including:\nPolicy-based replication Integrated user and role management Project-based image management Security features like CVE scanning Interfaces such as Web UI and REST API The Harbor repository on GitHub (https://github.com/goharbor/harbor) provides the source code and documentation for Harbor, and development and contributions are carried out by the community.\nSoftware Usage Harbor is used as a platform for managing and securing container images, typically in conjunction with container runtimes like Docker. It is utilized by companies and organizations to effectively manage their container images in a safe and efficient manner. Harbor offers flexible configurations and can be used in on-premises or cloud environments.\nUse Cases of goharbor harbor OSS goharbor harbor OSS is utilized as a Docker image registry. Some common use cases include:\nCentralized management of Docker images developed by teams or organizations Quick retrieval of necessary Docker images during infrastructure or application deployment Utilizing trusted Docker images for security reasons Software Architecture and Use Cases goharbor harbor OSS is mainly used when developing and operating microservices architecture or container-based applications. It is commonly used to deploy and operate applications containerized with Docker as multiple independent services.\nReasons for using goharbor harbor OSS include:\nSecurity: Setting up and operating private Docker image registries contributes to security. Manageability: It offers high convenience in version management of Docker images, access control, and auditing. Availability: By configuring replication and redundancy, operational reliability and availability can be ensured. Differences with kaniko kaniko is a tool that performs Docker build operations within containers without the need for a Docker daemon. On the other hand, goharbor harbor OSS provides registry functionality for Docker images.\nkaniko offers flexible options in terms of security and resource management during builds, while goharbor harbor OSS specializes in image management, distribution, and security. Each tool plays a different role, and choosing the appropriate tool based on the intended use is important.\ngoharbor/harbor This repository contains the source code for Harbor, an open-source container image registry. Harbor is a platform for storing, managing, and securing artifacts like Docker images and Helm charts.\npackages/auth This package provides authentication-related features, including functionalities for user authentication and role-based access control.\npackages/registry This package provides registry functionalities for Harbor, including features for storing and providing artifacts like Docker images and Helm charts.\npackages/core This package provides core functionalities for Harbor, including project management, user management, artifact management, and other basic features.\npackages/chartmuseum This package provides functionalities for ChartMuseum, enabling the storage and distribution of Helm charts.\nThese packages offer the core functionalities for implementing Harbor and are specialized in different aspects of Harbor's functionality. Detailed implementation and features can be understood by referring to the source code of each package.\n","link":"https://www.oss-db.glossvation.com/en/post/harbor-oss-for-docker/","section":"post","tags":["OSS","docker","kubernetes"],"title":"Harbor OSS for docker"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/machinelearning/","section":"tags","tags":null,"title":"MachineLearning"},{"body":"Smile Smile is a machine learning library written in Java. It is mainly used to perform machine learning tasks such as classification, regression, clustering, dimensionality reduction, and association rule learning.\nOverview Smile provides a simple and easy-to-use API with rich features. It implements many machine learning algorithms and supports tasks from data preprocessing to model training and evaluation.\nDetails Smile offers various algorithms such as Bayesian optimization, anomaly detection, decision trees, random forests, support vector machines, neural networks, and more. It also supports time series analysis and natural language processing.\nHow to Use Since Smile is a library written in Java, it is used when building machine learning models using the Java programming language. You can program the steps of data loading, preprocessing, model training, and evaluation yourself.\nGithub URL: https://github.com/haifengl/smile\nhaifengl/smile OSS is a Java library for machine learning and data mining. This library can be used for various machine learning tasks such as classification, regression, clustering, dimensionality reduction, and data preprocessing.\nhaifengl/smile OSS is utilized in software architecture to develop analysis and prediction models. It is effectively used in applications such as customer segmentation, market analysis using large datasets, image recognition, and natural language processing.\nBenefits of using haifengl/smile OSS include:\nIntegration with Java-based development environments is easy due to being written in Java. It provides fast and efficient algorithms, enabling real-time prediction and analysis. The library has comprehensive documentation with sample code and tutorials for various tasks, making it easy for beginners to use. For these reasons, haifengl/smile OSS is widely used in the fields of machine learning and data mining, adopted by many companies and research institutions.\nhaifengl/smile Packages smile-core:\nThe core package of Smile, containing basic data structures and algorithms. This package implements machine learning algorithms such as utility methods, random forests, k-nearest neighbors, k-means, and more. smile-data:\nContains utility methods for loading and exporting datasets. It is mainly used for data preprocessing and format conversion. smile-io:\nIncludes utility methods for reading data from files. It implements methods for handling data in CSV and JSON formats. smile-graph:\nContains methods related to graph algorithms. It is used for graph-related processing such as shortest path finding and clustering. smile-nlp:\nIncludes methods related to natural language processing. It implements tasks like text tokenization, word vector representation, and document classification. GitHub URL https://github.com/haifengl/smile Smile provides a range of useful machine learning algorithms and data processing methods, effectively supporting data science and machine learning projects. Each package implements functionality specialized for specific tasks, offering flexibility and ease of use.\n","link":"https://www.oss-db.glossvation.com/en/post/smile-oss-for-machine-learning-engine/","section":"post","tags":["OSS","MachineLearning"],"title":"smile OSS for machine learning engine"},{"body":"What is Kaniko Kaniko is a tool for building Docker images, and it is an open-source project. Unlike traditional Docker build tools, Kaniko does not require a Docker daemon and runs within a Kubernetes cluster. This can lead to improvements in security and performance.\nKaniko takes a Dockerfile as input and generates a Docker image that can be pushed to a Docker registry. It provides various useful features such as caching authentication credentials for Docker registries and conducting reproducible builds without using cache.\nKaniko is widely used in various use cases in container development, such as CI/CD pipelines and application deployment on Kubernetes.\nGithub URL Kaniko Github\nYou can check detailed information, documentation, and the latest release information of Kaniko from the above URL.\nGoogleContainerTools Kaniko OSS Use Cases Kaniko is a tool for building Docker images, running builds in a clean and reliable context without requiring a Docker daemon. This allows for improved security, reliability, and easy integration.\nSoftware Architecture Kaniko performs builds inside containers, so it is commonly used on container orchestration tools like Kubernetes without the need for standard build tools or a Docker daemon. After building the image specified in the Dockerfile, Kaniko packages the image into an OCI (Open Container Initiative) formatted tar archive.\nReasons for Using Kaniko There are several reasons to use Kaniko. Firstly, it can build containers inside a container without the need for a Docker daemon, which enhances security and reliability. Additionally, Kaniko enables builds in a clean environment, allowing for highly reproducible builds. It also facilitates easy integration with CI/CD pipelines and development environments, enabling efficient and reliable container image builds.\nIn summary, Kaniko is a useful tool for prioritizing security and reliability in Docker image builds, as well as for easily conducting builds within a Kubernetes environment.\nFor each package in the k0s source code cmd Contains the implementation of Kaniko's command-line interface (CLI). Provides commands for users to build container images using Kaniko. executor Contains the execution engine. Parses Dockerfiles to create images and executes the build process. lib Contains the core functionality of Kaniko. Includes essential functionalities for the build process such as creating image layers, saving Docker images, and handling caching. pkg Contains various packages of Kaniko. Includes functionalities related to the entire build process such as build options, context processing, and image name parsing. tests Contains a test suite. Includes automated tests to ensure the code operates correctly. For more detailed information, please refer to the GitHub repository (https://github.com/GoogleContainerTools/kaniko).\n","link":"https://www.oss-db.glossvation.com/en/post/kaniko-oss/","section":"post","tags":["OSS","docker","kubernetes"],"title":"kaniko OSS"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/erlang/","section":"tags","tags":null,"title":"Erlang"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/iot/","section":"tags","tags":null,"title":"IoT"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/queue/","section":"tags","tags":null,"title":"Queue"},{"body":"VerneMQ VerneMQ is a distributed message broker system implemented in Erlang. VerneMQ has high reliability and scalability, supporting communication among thousands of devices.\nOverview of VerneMQ VerneMQ is designed based on the MQTT protocol, making it ideal for IoT (Internet of Things) applications and real-time messaging purposes. VerneMQ includes clustering functionality to efficiently handle message reception and delivery.\nDetails of VerneMQ VerneMQ is fully open source and developed on GitHub. It is a robust and highly reliable message broker system that supports large device networks. Additionally, VerneMQ offers a wide range of features including clustering and security capabilities.\nUsage of VerneMQ VerneMQ is well-suited for IoT applications and real-time messaging purposes. Developers can easily facilitate communication among thousands of devices using VerneMQ. Furthermore, VerneMQ is easily extensible, allowing developers to add functionality through custom plugins.\nGitHub URL: https://github.com/vernemq/vernemq\nAn MQTT broker is a protocol used for sending and receiving data between IoT devices, also known as an MQTT server. MQTT is utilized for real-time communication among low-cost and low-power IoT devices.\nAn MQTT broker receives messages from MQTT clients and routes them to the appropriate clients. Additionally, an MQTT broker manages client states and can temporarily store messages when clients are offline.\nUnlike TCP, which is a reliable connection-oriented protocol that prioritizes data transmission reliability, MQTT is a message-oriented protocol known for its high data transfer speed. MQTT is a publish/subscribe protocol suitable for one-to-many communications rather than one-to-one.\nIn summary, an MQTT broker is a protocol designed for enabling real-time communication among low-cost and energy-efficient IoT devices, possessing characteristics different from TCP and HTTP.\nVerneMQ is a high-performance distributed MQTT broker designed for cloud-native IoT and M2M applications. With a focus on scalability, reliability, and security, VerneMQ supports large device networks.\nKey features of VerneMQ include:\nClustering: VerneMQ improves scalability by appropriately clustering nodes, allowing for easy addition or removal of nodes and effective handling of increased loads.\nMessaging Reliability: VerneMQ supports customization of Quality of Service (QoS) levels to ensure messaging reliability, providing various options to ensure message reachability and orderliness.\nSecurity: VerneMQ supports TLS/SSL for messaging encryption, as well as security features such as client authentication and access control.\nPlugin System: VerneMQ includes a plugin system that enables easy integration of custom extensions, facilitating the addition and customization of features tailored to specific needs.\nVerneMQ, through its features, is designed to meet the demands of IoT and M2M applications, providing a robust messaging infrastructure.\n","link":"https://www.oss-db.glossvation.com/en/post/vernemq-oss/","section":"post","tags":["OSS","Queue","Erlang","IoT"],"title":"VerneMQ OSS"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/database/","section":"tags","tags":null,"title":"Database"},{"body":"About InfluxDB InfluxDB is an open-source database for processing time-series data. It allows for data collection, storage, visualization, and analysis. InfluxDB is well-suited for various applications that handle time-series data such as IoT (Internet of Things), monitoring, log data, and analytics.\nKey Features Capable of high-speed writes and query processing Uses a SQL-like query language Data visualization using templates and dashboards Suitable for managing large-scale time-series data How to Use InfluxDB allows for data send and receive via CLI tools or HTTP API. Additionally, it can be integrated with visualization tools like Grafana or Chronograf for data analysis.\nGithub URL\nAbove is an overview and detailed explanation of InfluxDB.\nThe data model of InfluxDB reflects the characteristics of a time-series database. InfluxDB's data consists of three main elements: \u0026quot;measurement,\u0026quot; \u0026quot;tag,\u0026quot; and \u0026quot;field.\u0026quot;\nIn terms of internal architecture, InfluxDB manages data based on the concepts of \u0026quot;database,\u0026quot; \u0026quot;retention policy,\u0026quot; and \u0026quot;shard.\u0026quot; A \u0026quot;database\u0026quot; acts as a container for classifying data, a \u0026quot;retention policy\u0026quot; defines aspects like data retention duration, and a \u0026quot;shard\u0026quot; handles the physical partitioning of data, impacting query performance and capacity management.\nThe key elements of the data model for design are as follows:\nMeasurement: Represents the type of data stored in the database, such as \u0026quot;temperature\u0026quot; or \u0026quot;humidity.\u0026quot;\nTag: Provides metadata information in string form, used for data filtering and grouping, like \u0026quot;sensor_id\u0026quot; or \u0026quot;location.\u0026quot;\nField: Represents the actual data value, such as numeric or string values for temperature or humidity.\nThese elements, when combined effectively, allow for efficient and fast data processing in InfluxDB.\nInfluxDB is a database designed for the storage and manipulation of time-series data, offering a range of functionalities.\nFirstly, InfluxDB provides a query language to retrieve data. This language enables data retrieval based on specific conditions, time ranges, and aggregation.\nMoreover, InfluxDB includes data compression capabilities, ensuring efficient data storage and enhancing storage efficiency for large amounts of time-series data.\nAdditionally, InfluxDB comes equipped with data analysis features, allowing for processing like aggregation, analysis, and visualization. This enables extracting valuable insights from time-series data and leveraging them for business decision-making.\nTo prevent data loss, InfluxDB also takes data protection measures, offering functionalities for data redundancy, replication, and ensuring data reliability and availability.\nInfluxDB specializes in time-series data and offers features like query languages, data compression, data analysis, and data protection to efficiently handle time-series data and derive valuable insights.\nPrometheus\nOverview: Prometheus is a popular open-source system monitoring and alerting tool. It stores time-series data for querying and visualization, particularly suitable for monitoring container environments like Kubernetes. Github URL: https://github.com/prometheus/prometheus Grafana\nOverview: Grafana is a widely used open-source visualization tool that can connect to various data sources. It can be combined with time-series databases like Prometheus or InfluxDB. Github URL: https://github.com/grafana/grafana Above is information about popular Time Series Databases other than InfluxDB.\n","link":"https://www.oss-db.glossvation.com/en/post/time-series-database-influxdb/","section":"post","tags":["OSS","Database"],"title":"Time Series Database InfluxDB"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/cloud/","section":"tags","tags":null,"title":"Cloud"},{"body":"Cloud Robotics Core Cloud Robotics Core, an OSS project by Google, is a package designed to assist in the development and deployment of robot applications. This project is designed to facilitate the management and monitoring of cloud-based robot applications.\nFeature Overview Cloud Robotics Core includes the following key features:\nDeployment and management of robot applications Monitoring robot status and collecting logs Analyzing and visualizing traffic Real-time communication and data synchronization Details Cloud Robotics Core supports ROS (Robot Operating System) and ROS 2, aiding in the development and operation of robot applications. It operates on Google Cloud, ensuring scalability and improved security.\nGitHub URL: https://github.com/googlecloudrobotics/core\nThe GitHub repository for Cloud Robotics Core contains source code and documentation, providing detailed information and instructions for contribution. Please feel free to refer to it.\nMany companies, including major automotive manufacturers and robotics companies, are utilizing Google's OSS Cloud Robotics Core. Cloud Robotics Core, as an open-source robotics platform provided by Google, offers rich features to support robot development and operation. This enables developers and companies to flexibly build and operate robot systems on the cloud. The GitHub URL (https://github.com/googlecloudrobotics/core) hosts the latest code and documentation for Cloud Robotics Core. Developers and companies can contribute to the project from there. Moreover, the project is actively maintained, with new features and improvements regularly added. Cloud Robotics Core Source Code Packages crd This package contains Custom Resource Definitions (CRDs) for Cloud Robotics Core. CRDs define new types of resources on Kubernetes. crsync This package includes synchronization functionality between Cloud Robotics Core and local systems, allowing data transmission from devices like robot agents to the cloud. deployment This package comprises features related to the deployment of Cloud Robotics Core. Deployment mechanisms manage containerized applications on Kubernetes. grpc This package contains the gRPC interface for Cloud Robotics Core. gRPC, developed by Google, is a framework for efficient and lightweight remote procedure calls (RPC). kubeutil This package includes utility functions for Cloud Robotics Core's integration with Kubernetes. This simplifies resource management and cluster operations on Kubernetes. robot This package encompasses robot-related functionalities for Cloud Robotics Core, including control of robot agents and management of robot status. These packages constitute the core components of Cloud Robotics Core, an OSS project by Google, with each package separated by functionality to aid developers in understanding the code with focused attention on specific features. ","link":"https://www.oss-db.glossvation.com/en/post/cloud-robotics-core-oss-by-google/","section":"post","tags":["OSS","Go","Cloud","Google"],"title":"Cloud Robotics Core OSS by Google"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/google/","section":"tags","tags":null,"title":"Google"},{"body":"About k0s OSS k0s OSS (k-zero-S Open Source Software) is a lightweight and straightforward Kubernetes distribution for building cloud-native environments. k0s aims to deploy Kubernetes clusters with minimal resources, focusing on easy management.\nThe features of k0s OSS are as follows:\nHigh scalability: It possesses a simple and flexible architecture adaptable to various environments. Security: It provides robust security features to enhance security. Ease of use: Installation and configuration are straightforward, catering to both beginners and advanced users. Community-driven: Development and support are led by an open-source community. The repository on GitHub can be found at the following URL: https://github.com/k0sproject/k0s k0s OSS is used in building and operating cloud-native applications. By inheriting Kubernetes functionality and employing a simple and lightweight configuration, developers and operators can work more efficiently. Its high flexibility and scalability make it appealing for various needs.\nBy utilizing k0s OSS, you can build a cloud-native environment and achieve scalable, highly available systems. Feel free to check out the GitHub repository and make use of k0s OSS.\nUse Cases of k0s OSS k0s OSS is a lightweight and simple Kubernetes distribution suitable for building cloud-native applications and deploying microservices. Here are some examples of its use cases:\nUse Cases in Software Architecture Microservices Architecture: k0s OSS is suitable for microservices architecture, enabling the construction of cloud-native applications simply and flexibly. With access to all Kubernetes features, it excels in scalability and extensibility. Container Orchestration: k0s OSS provides Kubernetes functionalities for container orchestration, facilitating easy deployment, scaling, and monitoring of applications. Why Use k0s OSS Lightweight and Simple: k0s OSS is lightweight and simple to set up and operate, consuming fewer resources and enabling cost-effective construction of cloud-native applications. Customizable to Unique Needs: Being open-source, k0s OSS allows for modifications to its codebase, enabling customization to unique needs and requirements. Community Support: With an active community, k0s OSS provides an environment conducive to issue resolution, information exchange, and regular updates, ensuring stable operation. In summary, k0s OSS is a distribution that efficiently and flexibly serves microservices architecture and container orchestration needs.\nBelow are explanations of each package within the k0s GitHub repository (https://github.com/k0sproject/k0s):\ncmd Package\nThe cmd package contains the command-line interface (CLI) commands of k0s. It implements the main functionalities of the k0s CLI, used for operating k0s. control-plane Package\nThe control-plane package includes the control plane features of k0s. This allows k0s to function as the master of the cluster, handling communication between nodes and resource management within the cluster. image-builder Package\nThe image-builder package consists of the image-building functionality of k0s. This enables the creation of custom k0s images tailored to specific environments. pkg Package\nThe pkg package contains templates and libraries for implementing various functionalities of k0s. It provides useful resources and tools for implementing k0s features. These are explanations of the main packages and their functionalities within the k0s GitHub repository.\n","link":"https://www.oss-db.glossvation.com/en/post/k0s-oss-for-kubernetes/","section":"post","tags":["OSS","Go","Kubernetes"],"title":"k0s OSS for Kubernetes"},{"body":"Benefits of Backstage Adoption Integrated Developer Portal: Backstage provides an integrated portal for software developers and operations teams to access information and tools related to projects and services. This allows developers to efficiently manage products and promote collaboration. Customizable Plugins: With its plugin-based architecture, Backstage allows users to add various functions and services according to their needs. This enables the construction of developer portals customized to specific development environments and tools. Centralized Information Management: By adopting Backstage, information related to projects and services can be centralized and managed. This helps prevent fragmentation and duplication of information, improving overall visibility. Adopting Companies \u0026amp; Community Information Spotify: Spotify has adopted Backstage and is using it as an internal developer portal. The company actively participates in the development of Backstage, contributing code and bug fixes. Backstage Community: Backstage is an open-source project, with developers and companies from around the world participating in the community. Active discussions and information exchange take place on platforms like GitHub, and development of new plugins and features is ongoing. The community also hosts regular online events and workshops for users to interact and share their experiences with Backstage. ","link":"https://www.oss-db.glossvation.com/en/post/oss-backstage-open-platform-for-building-developer-portals-%E3%81%AE%E8%A9%B3%E7%B4%B0/","section":"post","tags":["OSS","Typescript","Portal"],"title":"Oss Backstage - Open platform for building developer portals"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/portal/","section":"tags","tags":null,"title":"Portal"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/typescript/","section":"tags","tags":null,"title":"Typescript"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/ai/","section":"tags","tags":null,"title":"AI"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/grpc/","section":"tags","tags":null,"title":"gRPC"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/java/","section":"tags","tags":null,"title":"Java"},{"body":"Here are some open-source gRPC frameworks written in Java:\ngRPC-Java\ngRPC-Java is the official Java implementation of gRPC. It uses Protocol Buffers to provide efficient communication between servers and clients. It includes the key features of gRPC such as binary protocol, RPC-style communication, and support for multiple languages. GitHub: https://github.com/grpc/grpc-java Helidon gRPC\nHelidon is a lightweight Java framework for building microservices, and Helidon gRPC provides support for gRPC. You can easily create gRPC servers and clients using annotations. It supports features like asynchronous communication, streaming, and gRPC security. GitHub: https://github.com/oracle/helidon/tree/main/services/grpc Micronaut gRPC\nMicronaut is a lightweight and fast Java framework, and Micronaut gRPC provides the functionality of gRPC. You can easily create gRPC servers and clients using annotations. It is known for its low memory consumption, fast startup, reactive programming, and cloud-native design. GitHub: https://github.com/micronaut-projects/micronaut-grpc Spring Boot gRPC\nSpring Boot gRPC is a project that provides support for using gRPC with Spring Boot. You can leverage the features of Spring Framework and Spring Boot. It creates gRPC servers and clients using automatically generated Java code from Protocol Buffers. GitHub: https://github.com/yidongnan/spring-boot-starter-grpc These frameworks are convenient tools for creating gRPC servers and clients using Java. Each framework caters to different environments and requirements, so you can choose the one that aligns with your project's needs.\n","link":"https://www.oss-db.glossvation.com/en/post/grpc-framework-oss/","section":"post","tags":["OSS","Java","gRPC"],"title":"Java gRPC Framework OSS"},{"body":"Here is a list of popular OSS (Open Source Software) Large language models:\nGPT-2\nGitHub URL: https://github.com/openai/gpt-2 GPT-2 is a neural network-based large language model developed by OpenAI. It is trained on data obtained from 8 million web pages and can be used for various natural language processing tasks such as text generation, understanding meaning of sentences, and translation. BERT\nGitHub URL: https://github.com/google-research/bert BERT is a language model based on the Transformer architecture developed by Google. Its key feature is learning context-aware word representations in sentences, making it suitable for various natural language processing tasks. BERT is commonly used for tasks like text classification, named entity recognition, and sentence similarity. GPT-3\nGitHub URL: Not public GPT-3 is the successor to GPT-2 and is being developed by OpenAI. GPT-3 achieves further scalability and can be used for tasks such as natural language generation, dialogue systems, translation, and summarization. However, it is not currently open-sourced on GitHub. OpenAI-ChatGPT\nGitHub URL: https://github.com/openai/chatGPT ChatGPT is an interactive language model developed by OpenAI. Its purpose is to engage in text-based conversations with users. ChatGPT can be used for tasks such as automated response generation and chatbot development. Megatron-LM\nGitHub URL: https://github.com/NVIDIA/Megatron-LM Megatron-LM is a training framework for large language models developed by NVIDIA. It is specialized for distributed training and training large-scale models. It achieves fast training by integrating with DeepSpeed. Megatron-LM is used for training models like GPT-2. These are some popular OSS Large language models. Each OSS can be used for various natural language processing tasks, and detailed information along with their GitHub URLs is provided.\n","link":"https://www.oss-db.glossvation.com/en/post/large-language-model-oss/","section":"post","tags":["LLM","AI","OSS"],"title":"Large language model OSS"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/llm/","section":"tags","tags":null,"title":"LLM"},{"body":"","link":"https://www.oss-db.glossvation.com/en/archives/","section":"","tags":null,"title":""},{"body":"GlossVation Company CompanyName GlossVation,llc Location Tokyo, Japan Capital 1 million yen Business details - Software development - Security diagnosis for Service - Construct infrastructure on the Cloud - SRE supporting Objective We have members with excellent technical skills who operate and develop the system. We hope to use IT technology to help improve efficiency and provide better services in a variety of industries and occupations.\nWe hope to work with companies that are experiencing a shortage of engineers or delays in IT implementation to improve efficiency.\n","link":"https://www.oss-db.glossvation.com/en/about/","section":"","tags":null,"title":"About"},{"body":"","link":"https://www.oss-db.glossvation.com/en/tags/index/","section":"tags","tags":null,"title":"index"}]