Flink Stream Big Data Parallel Operator Design and Topsis Performance Evaluation

The process of streamlining big data processing encounters several challenges, including managing extensive data volumes and ensuring high real-time performance. Flink has emerged as the favored technical solution in the realm of big data streaming processing due to its provision of numerous parallel operators. However, the effective design of these parallel operators and the performance evaluation process are pivotal for achieving efficient streaming data processing. Consequently, this study has formulated a series of parallel operators and assessed their performance using the Technique for Order of Preference by Similarity to Ideal Solution (Topsis) algorithm. The findings of this investigation emphasize the significance of operator design in streamlining big data processing. This research offers substantial support and serves as a valuable reference for resource optimization and performance evaluation. These outcomes have profound implications, not only in managing vast real-time data streams but also in the development of applications and systems aimed at addressing the challenges posed by large-scale data