spark-instrumented-optimizer/docs/sql-data-sources-binaryFile.md

---
layout: global
title: Binary File Data Source
displayTitle: Binary File Data Source
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Since Spark 3.0, Spark supports binary file data source,
which reads binary files and converts each file into a single record that contains the raw content
and metadata of the file.
It produces a DataFrame with the following columns and possibly partition columns:
* `path`: StringType
* `modificationTime`: TimestampType
* `length`: LongType
* `content`: BinaryType

To read whole binary files, you need to specify the data source `format` as `binaryFile`.
To load files with paths matching a given glob pattern while keeping the behavior of partition discovery,
you can use the general data source option `pathGlobFilter`.
For example, the following code reads all PNG files from the input directory:

<div class="codetabs">
<div data-lang="scala" markdown="1">
{% highlight scala %}

spark.read.format("binaryFile").option("pathGlobFilter", "*.png").load("/path/to/data")

{% endhighlight %}
</div>

<div data-lang="java" markdown="1">
{% highlight java %}

spark.read().format("binaryFile").option("pathGlobFilter", "*.png").load("/path/to/data");

{% endhighlight %}
</div>
<div data-lang="python" markdown="1">
{% highlight python %}

spark.read.format("binaryFile").option("pathGlobFilter", "*.png").load("/path/to/data")

{% endhighlight %}
</div>
<div data-lang="r" markdown="1">
{% highlight r %}

read.df("/path/to/data", source = "binaryFile", pathGlobFilter = "*.png")

{% endhighlight %}
</div>
</div>

Binary file data source does not support writing a DataFrame back to the original files.