Description
This presentation will first introduce the use case, which generates the price adjustments based on the network effect, and the corresponding model relies on the 108 near real-time features computed by Flink pipelines with the raw demand and supply events. Here is the simplified computation logic: -The pipelines need to process the raw real-time events at the rate of 300k/s including both demand and supply -Each event needs to be computed on the geospatial, temporal and other dimensions -Each event contributes to the computation on the original hexagon and the 1K+ neighbours due to the fan-out effect of Kring smooth -Each event contributions to the aggregation on multiple window sizes up to 32 minutes, sliding by 1 minute, or 63 windows in total Next the presentation will briefly go through the DAG of the Flink pipeline before optimization and the issues we faced: the pipeline could not run stably due to OOM and backpressure. The presentation will discuss how to optimize a streaming pipeline with the generic performance tuning framework, which focuses on three areas: Network, CPU and Memory, and five domains: Parallelism, Partition, Remote Call, Algorithm and Garbage Collector. …
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