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sql-data-sources-generic-options.md

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Generic File Source Options
Generic File Source Options
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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 the configuration spark.sql.files.ignoreCorruptFiles or the data source option 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 the configuration spark.sql.files.ignoreMissingFiles or the data source option 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 %}

Modification Time Path Filters

modifiedBefore and modifiedAfter are options that can be applied together or separately in order to achieve greater granularity over which files may load during a Spark batch query. (Note that Structured Streaming file sources don't support these options.)

  • modifiedBefore: an optional timestamp to only include files with modification times occurring before the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)
  • modifiedAfter: an optional timestamp to only include files with modification times occurring after the specified time. The provided timestamp must be in the following format: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00)

When a timezone option is not provided, the timestamps will be interpreted according to the Spark session timezone (spark.sql.session.timeZone).

To load files with paths matching a given modified time range, you can use:

{% include_example load_with_modified_time_filter scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
{% include_example load_with_modified_time_filter java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
{% include_example load_with_modified_time_filter python/sql/datasource.py %}
{% include_example load_with_modified_time_filter r/RSparkSQLExample.R %}