docs/sql-data-sources-generic-options.md

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title: 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:

<div class="codetabs">

<div data-lang="python"  markdown="1">
{% include_example ignore_corrupt_files python/sql/datasource.py %}
</div>

<div data-lang="scala"  markdown="1">
{% include_example ignore_corrupt_files scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example ignore_corrupt_files java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="r"  markdown="1">
{% include_example ignore_corrupt_files r/RSparkSQLExample.R %}
</div>
</div>

### 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 Glob Filter

`pathGlobFilter` is used to only include files with file names matching the pattern. The syntax follows
<code>org.apache.hadoop.fs.GlobFilter</code>. 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:

<div class="codetabs">

<div data-lang="python"  markdown="1">
{% include_example load_with_path_glob_filter python/sql/datasource.py %}
</div>

<div data-lang="scala"  markdown="1">
{% include_example load_with_path_glob_filter scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example load_with_path_glob_filter java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="r"  markdown="1">
{% include_example load_with_path_glob_filter r/RSparkSQLExample.R %}
</div>
</div>

### 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:

<div class="codetabs">

<div data-lang="python"  markdown="1">
{% include_example recursive_file_lookup python/sql/datasource.py %}
</div>

<div data-lang="scala"  markdown="1">
{% include_example recursive_file_lookup scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example recursive_file_lookup java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="r"  markdown="1">
{% include_example recursive_file_lookup r/RSparkSQLExample.R %}
</div>
</div>

### 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:

<div class="codetabs">

<div data-lang="python"  markdown="1">
{% include_example load_with_modified_time_filter  python/sql/datasource.py %}
</div>

<div data-lang="scala"  markdown="1">
{% include_example load_with_modified_time_filter scala/org/apache/spark/examples/sql/SQLDataSourceExample.scala %}
</div>

<div data-lang="java"  markdown="1">
{% include_example load_with_modified_time_filter  java/org/apache/spark/examples/sql/JavaSQLDataSourceExample.java %}
</div>

<div data-lang="r"  markdown="1">
{% include_example load_with_modified_time_filter  r/RSparkSQLExample.R %}
</div>
</div>