{"id":19532435,"url":"https://github.com/rain1024/pyarrow_flight_toy","last_synced_at":"2025-06-12T23:05:07.379Z","repository":{"id":187348260,"uuid":"676752704","full_name":"rain1024/pyarrow_flight_toy","owner":"rain1024","description":"PyArrow Flight Toy","archived":false,"fork":false,"pushed_at":"2023-08-11T02:17:42.000Z","size":46,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-26T03:26:31.415Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/rain1024.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-08-09T23:52:38.000Z","updated_at":"2023-08-09T23:54:25.000Z","dependencies_parsed_at":"2024-02-16T02:00:44.522Z","dependency_job_id":null,"html_url":"https://github.com/rain1024/pyarrow_flight_toy","commit_stats":null,"previous_names":["rain1024/pyarrow_flight_toy"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/rain1024/pyarrow_flight_toy","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rain1024%2Fpyarrow_flight_toy","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rain1024%2Fpyarrow_flight_toy/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rain1024%2Fpyarrow_flight_toy/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rain1024%2Fpyarrow_flight_toy/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/rain1024","download_url":"https://codeload.github.com/rain1024/pyarrow_flight_toy/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/rain1024%2Fpyarrow_flight_toy/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":259546428,"owners_count":22874562,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-11T01:51:08.255Z","updated_at":"2025-06-12T23:05:07.355Z","avatar_url":"https://github.com/rain1024.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Getting Started with Arrow Flight\n\n## Introduction to Apache Arrow Flight\n\n![](images/arrow.png)\n\nApache Arrow Flight is a framework for high-performance data services. It's part of the Apache Arrow project, which provides a standardized, language-independent columnar memory format optimized for analytics. Arrow Flight builds on this foundation to enable efficient over-the-network data transfer, making it a powerful tool for building data servers and clients that can communicate with minimal overhead.\n\n**Key Features**\n\n1. **Efficient Data Transfer**: Arrow Flight uses Apache Arrow's columnar format to enable fast, efficient data serialization and deserialization. This reduces the overhead typically associated with data transfer, especially for large datasets.\n2. **gRPC-Based Communication**: Arrow Flight relies on gRPC, a high-performance, open-source, and universal remote procedure call (RPC) framework. This allows for robust, scalable communication between Flight servers and clients.\n3. **Language Agnostic**: Since Apache Arrow provides libraries for various programming languages (including C++, Java, Python, and more), Arrow Flight can be used to build servers and clients in different languages that can communicate seamlessly.\n4. **Custom Actions**: Arrow Flight allows the definition of custom actions that clients can call on the server. This provides a flexible way to implement specific functionality tailored to your application's needs.\n5. **Authentication and Encryption**: Arrow Flight supports pluggable authentication and encryption, allowing for secure data transfer and access control.\n6. **Integration with Popular Tools**: Arrow Flight can be used with popular data processing and analytics tools, making it easier to build end-to-end data pipelines.\n1. **Efficient Data Transfer**: Arrow Flight uses Apache Arrow's columnar format to enable fast, efficient data serialization and deserialization. This reduces the overhead typically associated with data transfer, especially for large datasets.\n2. **gRPC-Based Communication**: Arrow Flight relies on gRPC, a high-performance, open-source, and universal remote procedure call (RPC) framework. This allows for robust, scalable communication between Flight servers and clients.\n3. **Language Agnostic**: Since Apache Arrow provides libraries for various programming languages (including C++, Java, Python, and more), Arrow Flight can be used to build servers and clients in different languages that can communicate seamlessly.\n4. **Custom Actions**: Arrow Flight allows the definition of custom actions that clients can call on the server. This provides a flexible way to implement specific functionality tailored to your application's needs.\n5. **Authentication and Encryption**: Arrow Flight supports pluggable authentication and encryption, allowing for secure data transfer and access control.\n6. **Integration with Popular Tools**: Arrow Flight can be used with popular data processing and analytics tools, making it easier to build end-to-end data pipelines.\n\n**Arrow Flight can be applied in various scenarios, including:**\n\n1. **Data Sharing Between Organizations**: Facilitate efficient data exchange between different organizations or departments within a large enterprise.\n2. **Real-Time Analytics**: Enable real-time analytics by providing fast access to large datasets stored across different locations.\n3. **Data Lake or Data Warehouse Access**: Expose data stored in a data lake or data warehouse to clients for querying and analysis.\n4. **Machine Learning and Data Science**: Allow data scientists and ML engineers to access large datasets for training models and performing analysis.\n5. **Cloud-Based Data Services**: Build scalable cloud-based data services that can serve multiple clients simultaneously.\n\n## Usecase: StoreAnalytics Flight Server with Action Healthcheck\n\n**Overview**\n\nThe StoreAnalytics Flight Server is a specialized use case leveraging Apache Arrow Flight's capabilities to serve the analytics needs of a retail chain. One of the essential features of this server is the implementation of a health check action, which ensures that the system is operating correctly and efficiently.\n\n**System Architecture**\n\nHere is system architecture for the StoreAnalytics Flight Serve\n\n![](images/system-architect.svg)\n\n* **Client**: Monitoring System \u0026 Administrative Tools: These clients interact with the server.\n* **StoreAnalytics Flight Server**: The central server that coordinates other services.\n* **Data Aggregation Service**: Collects data from various store locations.\n* **Real-Time Analytics Service**: Provides real-time insights using an analytics engine.\n* **Data Sharing Service**: Facilitates data sharing with regional offices and headquarters.\n\n**Description**\n\n1. System Health Monitoring:\n\n* Action Name: \"health_check\"\n* Purpose: To monitor the health of the StoreAnalytics Flight Server and ensure that all components are functioning correctly.\n* Implementation: The health check action can be implemented to perform various checks, such as database connectivity, availability of essential services, memory usage, CPU load, etc.\n* Response: The action returns a status message, such as \"OK\" if everything is functioning correctly or detailed error messages if there are issues.\n\n**Arrow Flight can be applied in various scenarios, including:**\n\n1. **Data Sharing Between Organizations**: Facilitate efficient data exchange between different organizations or departments within a large enterprise.\n2. **Real-Time Analytics**: Enable real-time analytics by providing fast access to large datasets stored across different locations.\n3. **Data Lake or Data Warehouse Access**: Expose data stored in a data lake or data warehouse to clients for querying and analysis.\n4. **Machine Learning and Data Science**: Allow data scientists and ML engineers to access large datasets for training models and performing analysis.\n5. **Cloud-Based Data Services**: Build scalable cloud-based data services that can serve multiple clients simultaneously.\n\n## Usecase: StoreAnalytics Flight Server with Action Healthcheck\n\n**Overview**\n\nThe StoreAnalytics Flight Server is a specialized use case leveraging Apache Arrow Flight's capabilities to serve the analytics needs of a retail chain. One of the essential features of this server is the implementation of a health check action, which ensures that the system is operating correctly and efficiently.\n\n**System Architecture**\n\nHere is system architecture for the StoreAnalytics Flight Serve\n\n![](images/system-architect.svg)\n\n* **Client**: Monitoring System \u0026 Administrative Tools: These clients interact with the server.\n* **StoreAnalytics Flight Server**: The central server that coordinates other services.\n* **Data Aggregation Service**: Collects data from various store locations.\n* **Real-Time Analytics Service**: Provides real-time insights using an analytics engine.\n* **Data Sharing Service**: Facilitates data sharing with regional offices and headquarters.\n\n**Description**\n\nSystem Health Monitoring:\n\n* Action Name: \"health_check\"\n* Purpose: To monitor the health of the StoreAnalytics Flight Server and ensure that all components are functioning correctly.\n* Implementation: The health check action can be implemented to perform various checks, such as database connectivity, availability of essential services, memory usage, CPU load, etc.\n* Response: The action returns a status message, such as \"OK\" if everything is functioning correctly or detailed error messages if there are issues.\n\nData Exchange Mechanism:\n\n* Action Name: \"do_exchange\"\n* Purpose: To facilitate the exchange of data between various components of the system, ensuring seamless communication and data flow.\n* Implementation: The do_exchange method can accept requests from various clients and services, process the data as required, route it to the appropriate destination, and handle any errors that may arise.\n* Response: The method returns a response indicating the status of the exchange, such as a success message confirming that the data exchange was successful or a detailed error message explaining why the exchange failed.\n\n### Usage\n\nThis section provides instructions on how to run the Flight server and interact with it using the client script.\n\n#### Running the Flight Server\n\nBefore interacting with the Flight server, you need to start the server by running the `server.py` file. This will allow the client to communicate with the server and perform actions.\n\n```bash\ncd server\npython server.py\n```\n\n#### Health Check\n\nTo perform a health check on the Flight server, run the following command:\n\n```bash\npython client_store.py --server grpc://0.0.0.0:5050 --action health_check\n```\n\nReplace `grpc://0.0.0.0:5050` with the URL of your Flight server.\n\n#### Do Exchange\n\nTo perform a data exchange action on the Flight server, run the following command:\n\n```bash\npython client_store.py --server grpc://0.0.0.0:5050 --action do_exchange\n```\n\nReplace `grpc://0.0.0.0:5050` with the URL of your Flight server.\n\n**Note**: The `do_exchange` action is a placeholder in the client script. You should implement the logic for this action as needed.\n\n#### Run Maven Tests\n\nTo execute the unit tests for your application, use the following Maven command:\n\n```bash\ncd client-store\nmvn test\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frain1024%2Fpyarrow_flight_toy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frain1024%2Fpyarrow_flight_toy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frain1024%2Fpyarrow_flight_toy/lists"}