In Part 3, we developed a Beam pipeline that tracks sport activities of users and outputs their speeds periodically. While reporting such values is useful for users on its own, we can provide more engaging information to users if we have a pipeline that reports pacing of their activities over periods. For example, we can send a message to encourage a user to work harder if he/she has a performance goal and is underperforming for some periods. In this post, we develop a new pipeline that tracks user activities and reports pacing details by comparing short term metrics to their long term counterparts.

We develop an Apache Beam pipeline that separates droppable elements from the rest of the data. Droppable elements are those that come later when the watermark passes the window max timestamp plus allowed lateness. Using a timer in a Stateful DoFn, droppable data is separated from normal data and dispatched into a side output rather than being discarded silently, which is the default behaviour. Note that this pipeline works in a situation where droppable elements do not appear often, and thus the chance that a droppable element is delivered as the first element in a particular window is low.

In the previous post, we continued discussing an Apache Beam pipeline that arguments input data by calling a Remote Procedure Call (RPC) service. A pipeline was developed that makes a single RPC call for a bundle of elements. The bundle size is determined by the runner, however, we may encounter an issue e.g. if an RPC service becomes quite slower if many elements are included in a single request. We can improve the pipeline using stateful DoFn where the number elements to process and maximum wait seconds can be controlled by state and timers. Note that, although the stateful DoFn used in this post solves the data augmentation task well, in practice, we should use the built-in transforms such as BatchElements and GroupIntoBatches whenever possible.

In the previous post, we developed an Apache Beam pipeline where the input data is augmented by a Remote Procedure Call (RPC) service. Each input element performs an RPC call and the output is enriched by the response. This is not an efficient way of accessing an external service provided that the service can accept more than one element. In this post, we discuss how to enhance the pipeline so that a single RPC call is made for a bundle of elements, which can save a significant amount time compared to making a call for each element.

In this post, we develop an Apache Beam pipeline where the input data is augmented by a Remote Procedure Call (RPC) service. Each input element performs an RPC call and the output is enriched by the response. This is not an efficient way of accessing an external service provided that the service can accept more than one element. In the subsequent two posts, we will discuss updated pipelines that make RPC calls more efficiently. We begin with illustrating how to manage development resources followed by demonstrating the RPC service that we use in this series. Finally, we develop a Beam pipeline that accesses the external service to augment the input elements.