Andriessen Property

andriessen property represents a topic that has garnered significant attention and interest. Optimising Spark read and write performance - Stack Overflow. How to read xlsx or xls files as spark dataframe - Stack Overflow. Can anyone let me know without converting xlsx or xls files how can we read them as a spark dataframe I have already tried to read with pandas and then tried to convert to spark dataframe but got... Read all files in a nested folder in Spark - Stack Overflow. But what if I have a folder folder containing even more folders named datewise, like, 03, 04, ..., which further contain some .log files.

How do I read these in Spark? In my case, the structure is even more nested & complex, so a general answer is preferred. Dealing with a large gzipped file in Spark - Stack Overflow.

Spark cannot parallelize reading a single gzip file. The best you can do split it in chunks that are gzipped. However, Spark is really slow at reading gzip files.

You can do this to speed it up: file_names_rdd = sc.parallelize(list_of_files, 100) lines_rdd = file_names_rdd.flatMap(lambda _: gzip.open(_).readlines()) Going through Python is twice has fast as reading the native Spark gzip reader. Similarly, pyspark - Spark reading CSV with bad records - Stack Overflow. Spark will try to parse an additional column after the last delimeter at the end of the line and populate that column with nulls. The original data would be validated by the schema provided and bad records will be moved to quarantine. After reading the data, extra column can be dropped. Edit: adding an example

Fetching data from REST API to Spark Dataframe using Pyspark. 7 Check Spark Rest API Data source. One advantage with this library is it will use multiple executors to fetch data rest api & create data frame for you. In relation to this, in your code, you are fetching all data into the driver & creating DataFrame, It might fail with heap space if you have very huge data.

apache spark - Reading Millions of Small JSON Files from S3 Bucket in .... 5 Apache Spark is very good at handling large files but when you have tens of thousands of small files (millions in your case), in a directory/distributed in several directories, that will have a severe impact on processing time (potentially 10s of minutes to hours) since it has to read each of these tiny files. Read existing delta table with Spark SQL - Stack Overflow. In this context, you don’t want a DataFrame; you want a DeltaTable.

DataFrame is a generic API, and a DeltaTable is the specific API for Delta-specific stuff. So DeltaTable.forName or DeltaTable.forPath instead of spark. In order to access the Delta table from SQL you have to register it in the metabase, eg sdf.write.format("delta").mode("overwrite").saveAsTable("ProductModelProductDescription ... In relation to this, reading parquet files from multiple directories in Pyspark.

df=spark.read.option("basePath",basePath).parquet(*paths) This is cool cause you don't need to list all the files in the basePath, and you still get partition inference.