sql-data-sources-generic-options.md

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• Table of contents {:toc}

These generic options/configurations are effective only when using file-based sources: parquet, orc, avro, json, csv, text.

Please note that the hierarchy of directories used in examples below are:

{% highlight text %}

dir1/ ├── dir2/ │ └── file2.parquet (schema: <file: string>, content: "file2.parquet") └── file1.parquet (schema: <file, string>, content: "file1.parquet") └── file3.json (schema: <file, string>, content: "{'file':'corrupt.json'}")

{% endhighlight %}

Ignore Corrupt Files

Spark allows you to use spark.sql.files.ignoreCorruptFiles to ignore corrupt files while reading data from files. When set to true, the Spark jobs will continue to run when encountering corrupted files and the contents that have been read will still be returned.

To ignore corrupt files while reading data files, you can use:

{% include_example ignore_corrupt_files scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example ignore_corrupt_files java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% include_example ignore_corrupt_files python/sql/datasource.py %}

{% include_example ignore_corrupt_files r/RSparkSQLExample.R %}

Ignore Missing Files

Spark allows you to use spark.sql.files.ignoreMissingFiles to ignore missing files while reading data from files. Here, missing file really means the deleted file under directory after you construct the DataFrame. When set to true, the Spark jobs will continue to run when encountering missing files and the contents that have been read will still be returned.

Path Global Filter

pathGlobFilter is used to only include files with file names matching the pattern. The syntax follows org.apache.hadoop.fs.GlobFilter. It does not change the behavior of partition discovery.

To load files with paths matching a given glob pattern while keeping the behavior of partition discovery, you can use:

{% include_example load_with_path_glob_filter scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example load_with_path_glob_filter java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% include_example load_with_path_glob_filter python/sql/datasource.py %}

{% include_example load_with_path_glob_filter r/RSparkSQLExample.R %}

Recursive File Lookup

recursiveFileLookup is used to recursively load files and it disables partition inferring. Its default value is false. If data source explicitly specifies the partitionSpec when recursiveFileLookup is true, exception will be thrown.

To load all files recursively, you can use:

{% include_example recursive_file_lookup scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}

{% include_example recursive_file_lookup java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}

{% include_example recursive_file_lookup python/sql/datasource.py %}

{% include_example recursive_file_lookup r/RSparkSQLExample.R %}