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Officially, you can use spark's sizeestimator in order to get the size of a dataframe Learn best practices, limitations, and performance optimisation techniques for those working with apache spark. But it seems to provide inaccurate results as discussed here and in other so topics.
Similar to python pandas you can get the size and shape of the pyspark (spark with python) dataframe by running count() action to get the number of rows on dataframe and len(df.columns()) to get the number of columns. Discover how to use sizeestimator in pyspark to estimate dataframe size There are several ways to find the size of a dataframe in pyspark
One common approach is to use the count() method, which returns the number of rows in the dataframe
This can be useful to get a sense of the overall size of the dataset. Sometimes it is an important question, how much memory does our dataframe use And there is no easy answer if you are working with pyspark You can try to collect the data sample and run local memory profiler
You can estimate the size of the data in the source (for example, in parquet file). Please see the docs for more details. In this article, we will discuss how we can calculate the size of the spark rdd/dataframe. This code can help you to find the actual size of each column and the dataframe in memory
The output reflects the maximum memory usage, considering spark's internal optimizations.
Parameters col column or str name of column or expression returns column length of the array/map Examples >>> df = spark.createdataframe([([1, 2, 3],),([1],),([],)], ['data']) >>> df.select(size(df.data)).collect() [row(size(data)=3), row(size(data)=1), row(size(data)=0)] In this blog, we’ll demystify why `sizeestimator` fails, explore reliable alternatives to compute dataframe size, and learn how to use these insights to configure optimal partitions.
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