Databricks Runtime supports the 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. The binary file data source produces a DataFrame with the following columns and possibly partition columns:
path (StringType): The path of the file.
modificationTime (TimestampType): The modification time of the file. In some Hadoop FileSystem implementations, this parameter might be unavailable and the value would be set to a default value.
length (LongType): The length of the file in bytes.
content (BinaryType): The contents of the file.
To read binary files, specify the data source
Databricks recommends that you use the binary file data source to load image data. See Reference solution for image applications for the recommended workflow to handle image data.
To load files with paths matching a given glob pattern while keeping the behavior of partition discovery,
you can use the
pathGlobFilter option. The following code reads all JPG files from the
input directory with partition discovery:
df = spark.read.format("binaryFile").option("pathGlobFilter", "*.jpg").load("<path-to-dir>")
If you want to ignore partition discovery and recursively search files under the input directory,
recursiveFileLookup option. This option searches through nested directories
even if their names do not follow a partition naming scheme like
The following code reads all JPG files recursively from the input directory and ignores partition discovery:
df = spark.read.format("binaryFile") \ .option("pathGlobFilter", "*.jpg") \ .option("recursiveFileLookup", "true") \ .load("<path-to-dir>")
Similar APIs exist for Scala, Java, and R.
To improve read performance when you load data back, Databricks recommends turning off compression when you save data loaded from binary files:
spark.conf.set("spark.sql.parquet.compression.codec", "uncompressed") df.write.format("delta").save("<path-to-table>")