3 Repos
Intercepting database execution events to track query frequency and association loading.
Distinct from Query: Specifically intercepts events for the purpose of performance analysis rather than general activity logging.
Explore 3 awesome GitHub repositories matching data & databases · Event Interception. Refine with filters or upvote what's useful.
Bullet is an Active Record performance monitor and query profiler for Ruby on Rails applications. It serves as a diagnostic utility to identify inefficient database access patterns, flag redundant requests, and suggest eager loading strategies to improve response times. The tool specifically detects N+1 queries, missing counter caches, and unused eager loading. It monitors these patterns across both standard web requests and background jobs, identifying records that are fetched but never accessed to reduce memory usage and query overhead. Analysis is supported by a system that intercepts dat
Intercepts database execution events to track query frequency and association loading patterns during a request.
FreeSql is a .NET object-relational mapper and data access layer that translates object-oriented code into SQL for multiple relational database providers. It functions as a fluent SQL query builder and database schema synchronizer, allowing developers to align database table and index structures with entity class definitions. The framework is specifically optimized for .NET Native AOT to ensure reduced memory footprints and faster startup times. It includes a database traffic manager to distribute load through read-write splitting, dynamic table sharding, and tenant-based data isolation. Bro
Triggers custom business logic before or after CRUD operations using AOP-driven event interception.
Gravitino is a federated metadata lake and unified data catalog designed to manage tables, files, and AI models across diverse data sources and cloud storage. It serves as a centralized interface for governing schemas, access controls, and tagging across relational databases, messaging queues, and object stores. The project distinguishes itself by unifying the management of AI assets, such as machine learning models and their version lineages, alongside traditional tabular data. It also implements the Iceberg REST specification to provide a standardized metadata server and proxy for lakehouse
Runs custom logic by listening for pre-event and post-event triggers during table operations.