You don’t have to union multiple RDDs. You can read files from multiple directories in a single read call. Spark will manage partitioning of the data across directories.
From: Kapil Garg <kapi...@flipkart.com.INVALID> Date: Wednesday, May 5, 2021 at 10:45 AM To: spark users <user@spark.apache.org> Subject: [EXTERNAL] How to read multiple HDFS directories CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe. Hi, I am facing issues while reading multiple HDFS directories. Please read the problem statement and current approach below Problem Statement There are N HDFS directories each having K files. We want to read data from all directories such that when we read data from directory D, we map all the data and augment it with additional information specific to that directory. Current Approach In current approach, we are iterating over the directories, reading it in RDD, mapping the RDD and the putting the RDD into a list. After all N directories have been read, we have a list of N RDDs We call spark Union on the list to merge them together. This approach is causing data skewness because there is 1 directory of size 12 GBs whereas other RDDs are less than 1 GB. So when the large RDD's turn comes, spark submits its task on available executors causing the RDD to present on few executors instead of spreading on all. Is there a way to avoid this data skewness ? I couldn't find any RDD API, spark config which could enforce the data reading tasks evenly on all executors. -- Regards Kapil Garg ----------------------------------------------------------------------------------------- This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error, please notify the system manager. This message contains confidential information and is intended only for the individual named. If you are not the named addressee, you should not disseminate, distribute or copy this email. Please notify the sender immediately by email if you have received this email by mistake and delete this email from your system. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited. Any views or opinions presented in this email are solely those of the author and do not necessarily represent those of the organization. Any information on shares, debentures or similar instruments, recommended product pricing, valuations and the like are for information purposes only. It is not meant to be an instruction or recommendation, as the case may be, to buy or to sell securities, products, services nor an offer to buy or sell securities, products or services unless specifically stated to be so on behalf of the Flipkart group. Employees of the Flipkart group of companies are expressly required not to make defamatory statements and not to infringe or authorise any infringement of copyright or any other legal right by email communications. Any such communication is contrary to organizational policy and outside the scope of the employment of the individual concerned. The organization will not accept any liability in respect of such communication, and the employee responsible will be personally liable for any damages or other liability arising. Our organization accepts no liability for the content of this email, or for the consequences of any actions taken on the basis of the information provided, unless that information is subsequently confirmed in writing. If you are not the intended recipient, you are notified that disclosing, copying, distributing or taking any action in reliance on the contents of this information is strictly prohibited. -----------------------------------------------------------------------------------------
