The modern digital era demands applications that can process a vast number of transactions efficiently, making task management critical. A single job queue can create performance bottlenecks as applications grow and become complex. Distributed job queues address this challenge by spreading tasks across multiple nodes, increasing processing capacity, enhancing fault tolerance, and ensuring system availability. By understanding and implementing distributed job queues, developers can improve their infrastructure's robustness, scalability, and efficiency. A basic Jobs table is created with details about each item, including ID, creation timestamp, current state, and associated data. An index on the status and create_ts columns is used to identify the earliest inserted item, which becomes the head of the queue. A view is created to retrieve data from all application-level buckets while retaining order. The executor hint is provided to mitigate performance degradation over time by giving a hint about where to start searching for the next item.