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Tag Archive for pysparkdatabricks

Schema validation json

What is the best way to do schema validation of a complex nested json in pyspark in databricks. My current input is a dataframe with one of the columns as a json.

Cannot convert column into bool with simple filter condition with any operator

I am trying to form filter condition dynamically from dict structure in python and it is very simple condition which is giving
below error
PySparkValueError: [CANNOT_CONVERT_COLUMN_INTO_BOOL] Cannot convert column into bool: please use ‘&’ for ‘and’, ‘|’ for ‘or’, ‘~’ for ‘not’ when building DataFrame boolean expressions.
I have data and column as below

Appending Spark dataframe iteratively using PySpark in databricks

I have a list of header keys that I need to iterate through and get data from an API.
I am creating a temporary dataframe to hold API response and using union to append data from temp dataframe to final dataframe. This code works but it is very slow. Please help me find an efficient solution.

Spark Dataframe and RDD’s are making my code slower

d2v_rdd = spark.sparkContext.textFile(“”) for row in d2v_rdd.collect(): row_elements = row.split(“t”) vector_dict[row_elements[0]] = np.array(row_elements[1:][0]) #Getting the dim features from the products file products_rdd = spark.sparkContext.textFile(“”) for row in products_rdd.collect(): row_elements = row.split(“t”) The dataset has 431907 rows I have the above lines of code implemented in three different forms: the python with open(“”) method reading it […]