Hi @Policepatil , The issue you're experiencing with missing records could be due to a variety of reasons. It could be related to how Spark handles data partitioning, or it might be due to some data quality issues in your input files.
One possible explanation could be related to the use of multithreading. Apache Spark is designed to be thread-safe and it uses its own mechanism for parallel processing. If you're running multiple threads in parallel, each thread may have its own SparkContext, which could cause inconsistencies in the data processing.
Another possible reason could be due to bad records or corrupted data in your input files. Spark provides several options for dealing with files that contain bad records. For instance, you can use the badRecordsPath
option to specify a path to record exceptions for bad records or files encountered during data loading.
Here is a code snippet to handle bad records:
python
df = spark.read \
.option("badRecordsPath", "/tmp/badRecordsPath") \
.format("parquet").load("/input/parquetFile")
In the above example, Spark creates an exception file in JSON format to record the error if it is unable to find the input file or if the file contains bad records. Without more detailed information about your code and data, it's hard to provide a more specific solution. I would recommend checking your input files for data quality issues and also reconsider the use of multithreading.