Flink is designed to achieve high performance and low latency. Unlike other streaming frameworks such as Spark, you don't need to do many manual configurations to get the best performance. Flink's pipelined data processing gives better performance compared to its counterparts.

As we discussed in the previous section, Flink's distributed checkpoint processing helps to guarantee processing each record exactly once. In the case of high-throughput applications, Flink provides us with a switch to allow at least once processing.

Flink supports data-driven windows. This means we can design a window based on time, counts, or sessions. A window can also be customized which allows us to detect specific patterns in event streams.

Flink's distributed, lightweight snapshot mechanism helps in achieving a great degree of fault tolerance. It allows Flink to provide high-throughput performance with guaranteed delivery.

Flink is supplied with its own memory management inside a JVM which makes it independent of Java's default garbage collector. It efficiently does memory management by using hashing, indexing, caching, and sorting.

Flink's batch data processing API is optimized in order to avoid memory-consuming operations such as shuffle, sort, and so on. It also makes sure that caching is used in order to avoid heavy disk IO operations.

Flink provides APIs for both batch and stream data processing. So once you set up the Flink environment, it can host stream and batch processing applications easily. In fact Flink works on Streaming first principle and considers batch processing as the special case of streaming.

Flink has a rich set of libraries to do machine learning, graph processing, relational data processing, and so on. Because of its architecture, it is very easy to perform complex event processing and alerting. We are going to see more about these libraries in subsequent chapters.

Flink supports event time semantics. This helps in processing streams where events arrive out of order. Sometimes events may come delayed. Flink's architecture allows us to define windows based on time, counts, and sessions, which helps in dealing with such scenarios.