![]() ![]() #spark read text files from a directory into RDDĬlass .MapPartitionsRDDġ.2 wholeTextFiles() – Read text files from S3 into RDD of Tuple. Here, it reads every line in a "text01.txt" file as an element into RDD and prints below output. Val rddFromFile = ("s3a://sparkbyexamples/csv/text01.txt") ![]() Println("#spark read text files from a directory into RDD") 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. File Nameġ.1 textFile() – Read text file from S3 into RDD We can read a single text file, multiple files and all files from a directory located on S3 bucket into Spark RDD by using below two functions that are provided in SparkContext class.īefore we start, let’s assume we have the following file names and file contents at folder “csv” on S3 bucket and I use these files here to explain different ways to read text files with examples. t("fs.s3n.awsSecretAccessKey", "awsSecretAccessKey value") t("fs.s3n.awsAccessKeyId", "awsAccessKeyId value") In case if you are using s3n: file system Replace Key with your AWS secret key (You can find this on IAM Replace Key with your AWS account key (You can find this on IAM Val spark: SparkSession = SparkSession.builder() Regardless of which one you use, the steps of how to read/write to Amazon S3 would be exactly the same except s3a:\\. You can find more details about these dependencies and use the one which is suitable for you. Below are the Hadoop and AWS dependencies you would need in order Spark to read/write files into Amazon AWS S3 storage. In this example, we will use the latest and greatest Third Generation which is s3a:\\ . S3a – This is a replacement of s3n which supports larger files and improves in performance. ![]() S3n uses native s3 object and makes easy to use it with Hadoop and other files systems. S3 which is also called classic (s3: filesystem for reading from or storing objects in Amazon S3 This has been deprecated and recommends using either the second or third generation library. And this library has 3 different options. In order to interact with Amazon AWS S3 from Spark, we need to use the third party library. Reading files from a directory or multiple directories.Consider this example policy shown on the language overview page.Using these methods we can also read all files from a directory and files with a specific pattern on the AWS S3 bucket. Is it necessary to specify a resource attribute within the statements that make up an S3 bucket policy? The Access Policy Language Overview seems to suggest that the resource attribute is always included, though it doesn't come straight out and say it's required. ![]()
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