Handling corrupted input files during ETL

Corrupt files are a common issue when working with Apache Spark, one of the most popular open-source big data processing frameworks. Corrupt files refer to data files that contain errors or inconsistencies which prevent them from being processed correctly. Spark’s robust file system can detect these corrupt files and flag them for potential issues. If left unchecked, these corrupt files can cause major problems in data processing pipelines and can render data unusable. It is important to understand how to detect and fix corrupt files in Apache Spark.


Ignoring corrupt files while using Apache Spark is an important task as it helps to improve the performance of your system. Corrupt files may lead to broken data, resulting in errors and degraded performance. The ‘ignoreCorruptFiles’ feature can be used to bypass any corrupt files while loading data into Spark, and thus, prevent any potential errors or performance issues.

By using the ‘ignoreCorruptFiles’ (–conf spark.sql.files.ignoreCorruptFiles=true) feature, Spark will skip all corrupt files, and instead continue processing the data from the next file. This helps to ensure that your data is not corrupted and all your tasks are completed successfully.

When deciding when to use ‘ignoreCorruptFiles’, it is important to consider the sources of the data you are loading. If you are loading data from external sources, you should always use ‘ignoreCorruptFiles’ as it helps to ensure that your data is not affected by any corrupt files. If you are loading data from internal sources, such as a database, then you may not need to use this feature.


The badRecordsPath option in Apache Spark helps users deal with corrupted files by providing a way to deal with records that fail one or more validation or transformation. This option can help keep your data clean, as it allows users to log and store corrupted records in a separate directory. This prevents the corrupted records from affecting the main dataset and allows users to easily identify the failed records and fix the issue.

When this option is enabled (.option(‘badRecordsPath’, ‘tmp/badRecs’)), Spark will store all the records in the badRecordsPath directory and replace them with nulls in the main dataset. This makes it easier for users to find and fix the corrupted records. It also helps users maintain the integrity of the main dataset, as corrupt records are not stored in the main dataset.

Overall, the badRecordsPath option can be a useful tool for dealing with corrupted files in Apache Spark. It allows users to quickly identify corrupt records, store them in a separate directory, and keep the main dataset clean.

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