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This content focuses on the use of the explode function in PySpark, especially within the context of Databricks and Azure Databricks. It covers various related functions such as split(), array(), and array_contains(), which are often used in conjunction with explode() to manipulate and analyze data in Spark. The content also mentions explode_outer and posexplode, providing a comprehensive guide to handling complex data structures in Spark. It is suitable for Spark interview preparation, particularly for scenario-based questions.

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Risk warning: Digital asset trading is an emerging industry with bright prospects, but it also comes with huge risks as it is a new market. The risk is especially high in leveraged trading since leverage magnifies profits and amplifies risks at the same time. Please make sure you have a thorough understanding of the industry, the leveraged trading models, and the rules of trading before opening a position. Additionally, we strongly recommend that you identify your risk tolerance and only accept the risks you are willing to take. All trading involves risks, so you must be cautious when entering the market.

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