I have a MySQL RDBMS table (3 Million rows, only 209K returned) like this that I need to Python to load into a Spark dataframe. The issue is that I need to load it concurrently as it is REALLY slow (1.5H min), but as you can see I have no way to set an “upperbound” and “lowerbound” that JDBC needs. So my question is how to load this table concurrently. I can’t change the table and can’t find an example of such a table being loaded into a dataframe with concurrency.

I have a MySQL RDBMS table (3 Million rows, only 209K returned) like this that I need to Python to load into a Spark dataframe. The issue is that I need to load it concurrently as it is REALLY slow (1.5H min), but as you can see I have no way to set an “upperbound” and “lowerbound” that JDBC needs. So my question is how to load this table concurrently. I can’t change the table and can’t find an example of such a table being loaded into a dataframe with concurrency.

I have a MySQL RDBMS table (3 Million rows, only 209K returned) like this that I need to Python to load into a Spark dataframe. The issue is that I need to load it concurrently as it is REALLY slow (1.5H min), but as you can see I have no way to set an “upperbound” and “lowerbound” that JDBC needs. So my question is how to load this table concurrently. I can’t change the table and can’t find an example of such a table being loaded into a dataframe with concurrency.

I have a MySQL RDBMS table (3 Million rows, only 209K returned) like this that I need to Python to load into a Spark dataframe. The issue is that I need to load it concurrently as it is REALLY slow (1.5H min), but as you can see I have no way to set an “upperbound” and “lowerbound” that JDBC needs. So my question is how to load this table concurrently. I can’t change the table and can’t find an example of such a table being loaded into a dataframe with concurrency.

When installing PySpark from pypi.org

When installing PySpark from pypi.org

When installing PySpark from pypi.org

Context I have a job that generates a csv based on some data in the datalake of my company. This job is triggered once a day with some predefined configuration. This job is implemented using spark and python and executed in an Airflow pipeline. The csv is later on uploaded to a particular customer. Case […]

I am trying to make some joints, groupings,… more efficiently with pyspark, by trying to avoid unnecessary exchanges. I have a situation where first I need to join a dataframe by columns (a, b, c), and later another join by columns (a, b, d).

I have a pyspark dataframe in which I need to add “translate” for a column.
I have the below code