These user-defined functions operate one-row-at-a-time, and thus suffer from high serialization and invocation overhead. To enable data scientists to leverage the value of big data, Spark added a Python API in version 0.7, with support for user-defined functions. At the same time, Apache Spark has become the de facto standard in processing big data. Packages such as pandas, numpy, statsmodel, and scikit-learn have gained great adoption and become the mainstream toolkits. Over the past few years, Python has become the default language for data scientists. This article-a version of which originally appeared on the Databricks blog-introduces the Pandas UDFs (formerly Vectorized UDFs) feature in the upcoming Apache Spark 2.3 release, which substantially improves the performance and usability of user-defined functions (UDFs) in Python. Note: This post was updated on March 2, 2018.
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