The first countermeasure is to confine sensors in separate pools. Even when enough slots are available, workers may be hogged by tons of sleeping processes. Too many “sensors” busy waiting may, if not well dimensioned, use all the worker’s slots and bring to starvation and deadlocks (if TaskExternalSensor were used for example). Sensors are a fundamental building block to create pipelines in Airflow however, historically, as they share the Operator’s main execution method, they were (and by default still are) synchronous.īy default, they busy-wait for an event to occur consuming a worker’s slot. ![]() Sensors are a special type of Operators designed to wait for an event to occur and then succeed so that their downstream tasks can run. In this article we will discuss sensors and tasks controlling external systems and, in particular, the internals of some of the (relatively) new most interesting features, Reschedule sensors, SmartSensors and Deferrable Operators. transform data, load it, etc.), or “sensors”, a task waiting for some event to happen (i.e. ![]() It is based on directed acyclic graphs (DAGs) concept, where all the different steps (tasks) of the data processing (wait for a file, transform it, ingest it, join with other datasets, process it, etc.) are represented as graph’s nodes.Įach node can be either an “operator”, that is a task doing some actual job (i.e. It allows developers to programmatically define and schedule data workflows and monitor them using Python. ![]() Apache Airflow is one of the most used workflow management tools for data pipelines - both AWS and GCP have a managed Airflow solution in addition to other SaaS offerings (notably Astronomer).
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