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Improving Join Performance with Skewed Datasets in Spark

Ajay Gupta gets into the topic of join performance:

Performing Joins on Skewed DatasetsA Dataset is considered to be skewed for a Join operation when the distribution of join keys across the records in the dataset is skewed towards a small subset of keys. For example when 80% of records in the datasets contribute to only 20% of Join keys.

Implications of Skewed Datasets for Join: Skewed Datasets, if not handled appropriately, can lead to stragglers in the Join stage (Read this linked story to know more about Stragglers). This brings down the overall execution efficiency of the Spark job. Also, skewed datasets can cause memory overruns on certain executors leading to the failure of the Spark job. Therefore, it is important to identify and address Join-based stages where large skewed datasets are involved.

Read on for five techniques which may help you out.

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