In the realm of data streaming and messaging systems, the concept of delivery semantics is crucial for understanding how messages are transmitted and processed. Sequin, a platform that enhances Postgres with streaming capabilities, engages in a discussion about these semantics, particularly focusing on the distinctions between delivery and processing guarantees. Sequin operates under the premise of "at-least-once delivery" and "exactly-once processing." The differentiation between these terms is essential. At-most-once delivery, which is common in many pub/sub systems, means that messages are transient; if a subscriber is unavailable or if there are network issues, the message may be lost. An example of this is Postgres' LISTEN/NOTIFY feature, where messages can be missed entirely. In contrast, at-least-once delivery ensures that messages are delivered reliably. This is achieved by persisting the message and its delivery state, confirming receipt before considering the message delivered. However, this approach introduces the possibility of redelivery, as interruptions can occur before confirmation is received. This scenario highlights the complexities of the two-phase commit or distributed transaction problem, where the sender must confirm delivery to both the receiver and the database. The challenge lies in ensuring that both confirmations are successfully committed, as failure in either can lead to inconsistencies. The notion of exactly-once delivery is often viewed as an ideal that is difficult to achieve in practice. While it is possible to approach this ideal through careful design—such as ensuring that both systems are ready before committing—perfection remains elusive. Therefore, Sequin refrains from claiming exactly-once delivery, instead emphasizing its at-least-once delivery and exactly-once processing capabilities. This distinction is important because processing encompasses the entire lifecycle of a message, from delivery to successful acknowledgment. Despite the guarantees provided by systems like SQS, Kafka, and Sequin, the two-phase commit problem persists. For instance, if a worker processes a message and performs an action, such as sending an email, a network error could prevent the acknowledgment of that action, leading to potential duplicate processing. This highlights the importance of understanding the mechanics of message delivery and processing. To navigate the challenges posed by reprocessing issues, it is essential to design systems with these considerations in mind. There are three primary strategies: accepting the risks associated with redeliveries, designing the system to be idempotent so that repeated processing does not lead to errors, or opting for at-most-once delivery, which may result in some messages being missed. The choice among these options depends on the specific requirements of the application. For example, a financial institution would prioritize idempotency to ensure that transactions are not lost, while an analytics system might prefer to miss some events rather than risk double counting. Additionally, breaking messages into smaller units of work can help manage the potential for inconsistencies during processing. This approach allows for more granular control over operations and can facilitate the design of idempotent workflows. Proper configuration of timeouts is also critical; for instance, in systems like SQS and Sequin, setting appropriate visibility timeouts ensures that messages are not prematurely considered for redelivery, allowing sufficient time for processing. In summary, understanding the nuances of message delivery and processing is vital for building robust data streaming systems. Sequin's approach to at-least-once delivery and exactly-once processing reflects a commitment to reliability while acknowledging the inherent challenges of achieving perfect delivery guarantees. By designing systems with these principles in mind, developers can create more resilient applications capable of handling the complexities of message processing.