Optimizing join performance
Performing joins on tables can be a resource-expensive operation. To improve the performance of such operations, we can select a subset of the data or correct possible drawbacks, such as having a disproportionate distribution of file sizes in our data. Solving these issues can improve performance and lead to more efficient use of distributed computing power.
Azure Databricks Delta Lake allows optimization of join operations by providing range filtering and correcting skewness in the distribution of the file size of the data in our tables.
Range join optimization
Joins are used frequently, so optimizing these operations can lead to a great improvement in the performance of our queries. Range join optimization is the process of specifying that a join needs to be performed on a subset of data given by a range.
Range join optimization is applied when join operations have a filtering condition whose type is either a numeric or datetime type, and can...
