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sparkContext.textFile() method is used to read a text file from S3 (use this method you can also read from several data sources) and any Hadoop supported file system, this method takes the path as an argument and optionally takes a number of partitions as the second argument. pyspark read zip file from s3ġ.1 textFile() – Read text file from S3 into RDD. A SparkSession can be used create DataFrame, register DataFrame as tables, execute SQL over tables, cache tables, and read parquet files. The entry point to programming Spark with the Dataset and DataFrame API.
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TextFile() – Read single or multiple text, csv files and returns a single Spark RDD wholeTextFiles() – Reads single or multiple files and returns a single RDD], where first value (_1) in a tuple is a file name and second value (_2) is content of the file.Ĭlass (sparkContext, jsparkSession=None) ¶. This module does not currently handle multi-disk ZIP files. Any advanced use of this module will require an understanding of the format, as defined in PKZIP Application Note. This module provides tools to create, read, write, append, and list a ZIP file. The ZIP file format is a common archive and compression standard. While a text file in GZip, BZip2, and other supported compression formats can be configured to be automatically decompressed in Apache Spark as long as it has the right file extension, you must perform additional steps to read zip files.
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Hadoop does not have support for zip files as a compression codec.
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Return dict (zip (files, )) zips = sc.binaryFiles ("dbfs:/mnt/vedant-demo/ONG/data/las_raw/D-Dfiles.zip") files_data = zips.map (zip_extract) Sign up for free to join this conversation on GitHub. First we will build the basic Spark Session which will be needed in all the code blocks. We will use SparkSQL to load the file, read it and then print some data of it. This post explains Sample Code – How To Read Various File Formats in PySpark (Json, Parquet, ORC, Avro).