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Learn about ByteDance's innovative approach to migrating from MapReduce to Spark in this 33-minute conference talk. Explore the challenges faced by ByteDance's big data infrastructure team as they manage 1.2 million daily Spark jobs alongside 20,000-30,000 MapReduce tasks. Discover the issues with the MapReduce engine, including low ROI for framework updates, poor adaptability to new computing scheduling frameworks, and suboptimal computing performance. Gain insights into ByteDance's smooth migration solution, which allows users to transition legacy jobs to Spark with minimal modifications, significantly reducing migration costs and improving efficiency. Understand how this approach addresses the need for additional Pipeline tools and supports various scripts not natively compatible with Spark.
