Deploying a streaming pipeline requires more than just functional code; it demands a configuration strategy that balances throughput against latency. Default settings in Apache Flink and Kafka often prioritize compatibility over raw speed, which can lead to resource underutilization or stability issues when processing loads increase. This unit addresses the specific configurations and operational practices necessary to sustain performance in high-velocity environments.

We begin by examining backpressure. In distributed systems, a slow downstream operator propagates pressure up the graph, eventually halting the source. You will learn to interpret Flink's backpressure metrics to pinpoint the exact bottleneck in your topology. Following this, we look at memory management. Flink operates with a complex memory hierarchy, managing data in both the JVM heap and off-heap direct memory. Understanding how to size network buffers and allocate memory slots is necessary for preventing OutOfMemoryError crashes and reducing garbage collection pauses.

State access latency is another common performance limiter. Since we use RocksDB for large state management, we will adjust its compaction styles and block cache settings to minimize disk I/O. On the ingestion side, we measure Kafka consumer lag, technically defined as:

$Lag = \text{LogEndOffset} - \text{CurrentOffset}$

Monitoring this metric ensures the application keeps pace with the input rate. The chapter concludes with a practical exercise on handling data skew, where you will redistribute keys to eliminate "hot" partitions that throttle the entire cluster.