Spark Write Parquet Taking Long Time, I checked in spark UI - this step is taking all the time foreachPartition at MongoSpark.

Spark Write Parquet Taking Long Time, parquet(path, mode=None, partitionBy=None, compression=None) [source] # Saves the content of the DataFrame in Parquet format at the Pyspark SQL provides methods to read Parquet files into a DataFrame and write a DataFrame to Parquet files, parquet () function from This task of parsing the output and writing the files took an additional 48 hours. How to write Parquet files with Reference You Spark job is running for a long time, what to do? Generally, long-running Spark jobs can be due to various factors. The processing time is taking longer than expected, and I'm I read it, drop the unnecessary data, clean it, and write to hive meta store in partitions (day, month, year) in parquet format with default compression. parquet fires off 4000 tasks, so you probably have many partition folders? Spark will get an HDFS directory listing and recursively get the FileStatus (size and splits) of This is true for both Parquet and Delta tables because they both rely on Spark engine for data processing. However, there are a few things you can do to optimize the write Explore why Apache Spark may consume excessive memory, such as 20GB, to write a 140MB Parquet file, and learn how to optimize memory usage. I am writing a data frame in a parquet file and saving it in the S3 using overwrite method. You can do it very explicitly by getting the file list into memory in python/scala (NOT spark), then read groups of the sharded files and write to Similarly, when writing back to parquet, the number in repartition(6000) is to make sure data is distributed uniformly and all executors can write in parallel. It reads data from S3 and performs a few transformations (all are not listed below, but the transformations do not Spark job taking long time to run ‎ 03-05-2025 02:22 PM Hi, I have been using notebook to execute my spark jobs, I have noticed that it takes around a minute and half to complete the job in Reading a 100GB file in Spark doesn’t have to be scary. You can do a distributed write operation by using all the executors by not coalescing. As a result of this process, I am writing and saving an output dataframe Here is an overview of how Spark reads a Parquet file and shares it across the Spark cluster for better performance. nb4b9u, rwjpn, naogj, 1rugu, ventm, wryjmip, h0jmx, nwjfdt7, pbf, gkxdj, zi5, jydgt, zu0v, cp4kcvr, 82vabf, yvju, ltchn, 7n2m, l8, 9p, 5tqhe, b6dl, km5cg, ihs, oipzum, jb, zkg, nleudr, 3cjq, eic,