Stream Processing with Apache Flink

Chapter 8. Reading from and Writing to External Systems

Data can be stored in many different systems, such as filesystems, object stores, relational database systems, key-value stores, search indexes, event logs, message queues, and so on. Each class of systems has been designed for specific access patterns and excels at serving a certain purpose. Consequently, today’s data infrastructures often consist of many different storage systems. Before adding a new component into the mix, a logical question to ask should be, “How well does it work with the other components in my stack?”

Adding a data processing system, such as Apache Flink, requires careful considerations because it does not include its own storage layer but relies on external storage systems to ingest and persist data. Hence, it is important for data processors like Flink to provide a well-equipped library of connectors to read data from and write data to external systems as well as an API to implement custom connectors. However, just being able to read or write data to external datastores is not sufficient for a stream processor that wants to provide meaningful consistency guarantees in the case of failure.

In this chapter, we discuss how source and sink connectors affect the consistency guarantees of Flink streaming applications and present Flink’s most popular connectors to read and write data. You will learn how to implement custom source and sink connectors and how to implement functions that send asynchronous read ...