Скачать или смотреть How to Solve the Spark HDFS File Loading Issue with -ext-10000 Subdirectory

Discover how to fix the issue of Apache Spark unable to load HDFS file paths with `-ext-10000` subdirectories when working with Hive.
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Resolving Spark Loading Issues with HDFS Directories

When working with Apache Spark and Hadoop, data loading from HDFS (Hadoop Distributed File System) can often present challenges, especially when dealing with nested directories or unconventional directory names. One common issue arises when trying to load data from Hive, particularly when you encounter a directory labeled -ext-10000. In this guide, we'll explore this problem and its solution in an easy-to-understand manner.

The Problem: Loading Data from HDFS

Imagine you are attempting to load data from Hive using Spark and need to access files located in a subdirectory named -ext-10000. You successfully fetch data recursively from one directory labeled dt=2022-10-11, yet when you try to read from dt=2022-10-12/-ext-10000, nothing happens, and no error message appears. You have already configured several Spark settings to streamline the process, including:

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Despite these configurations, the issue persists.

Solution: Adjusting Spark Configuration

To successfully read data from the -ext-10000 subdirectory in Hive using Spark, an additional configuration change needs to be implemented. After analysis, the following configuration proved to be the key:

[[See Video to Reveal this Text or Code Snippet]]

Step-by-Step Configuration

To resolve the issue, follow these steps:

Open Your Spark Configuration: Locate the Spark configuration file or your Spark session setup where you specify various --conf settings.

Add the New Configuration: Introduce the following line to disable converting Metastore ORC files:

[[See Video to Reveal this Text or Code Snippet]]

Re-attempt Data Loading: Once you've added the configuration, try loading the data again from the (dt=2022-10-12/-ext-10000) path.

Benefits of This Approach

Effective Data Access: This configuration allows Spark to properly recognize the .orc file format coming from Hive, streamlining the access path.

Improved Compatibility: By disabling the conversion, you ensure compatibility with different types of Hive tables and their subdirectory structures.

Conclusion

Loading data in Apache Spark from HDFS can sometimes be tricky, especially with directories that may not conform to typical naming conventions. If you find yourself facing difficulties with loading data from directories like -ext-10000, remember that configurations are critical to overcoming these challenges. By following the steps outlined above, you should now be able to efficiently load data into Spark from complex HDFS directory structures. Happy coding!