2 रिपॉजिटरी
Awesome GitHub RepositoriesBulk Executions
High-throughput processing of multiple SQL statements or queries loaded from files.
Distinct from SQL Query Execution: Distinct from general SQL execution by focusing on mass processing and file-based batching.
Explore 2 awesome GitHub repositories matching data & databases · Bulk Executions. Refine with filters or upvote what's useful.
Awesome Bulk Executions GitHub Repositories
AI के साथ बेहतरीन रिपॉजिटरी खोजें।हम AI का उपयोग करके सबसे सटीक रिपॉजिटरी खोजेंगे।
- kennethreitz/records
kennethreitz/records
7,217GitHub पर देखेंRecords is a SQL database client designed for executing raw queries and managing result sets through a simplified interface. It provides a parameterized SQL executor to bind values to placeholders, ensuring safe data handling and preventing injection attacks, alongside a database transaction manager for grouping operations into atomic units. The project includes a dedicated command-line interface for running database statements and exporting query results directly to local files. This tooling allows for the conversion of SQL result sets into multiple serialization formats, including CSV, JSON
Processes multiple SQL statements in a single operation or loads queries from files to increase data throughput.
Pythonforhumanskennethreitzorm - kennethreitz-archive/records
kennethreitz-archive/records
7,219GitHub पर देखेंRecords is a suite of tools providing a SQL database client library, a transaction manager, and utilities for command line access and data export. It functions as a thin-layer wrapper that executes raw SQL queries against relational databases to retrieve results in simplified data formats. The project includes a command line interface for running database queries and a dedicated data export tool that converts relational query results into CSV, JSON, or Excel files for external analysis. The library covers relational database management through atomic transaction control with commit and rollb
Optimizes performance by running multiple SQL queries in a single operation to reduce network overhead.
Python
