File metadata column

You can get metadata information for input files with the _metadata column. The _metadata column is a hidden column, and is available for all input file formats. To include the _metadata column in the returned DataFrame, you must explicitly reference it in your query.

If the data source contains a column named _metadata, queries return the column from the data source, and not the file metadata.

Warning

New fields might be added to the _metadata column in future releases. To prevent schema evolution errors if the _metadata column is updated, Databricks recommends selecting specific fields from the column in your queries. See examples.

Supported metadata

The _metadata column is a STRUCT containing the following fields:

Name	Type	Description	Example	Minimum Databricks Runtime release
file_path	`STRING`	File path of the input file.	`file:/tmp/f0.csv`	10.5
file_name	`STRING`	Name of the input file along with its extension.	`f0.csv`	10.5
file_size	`LONG`	Length of the input file, in bytes.	628	10.5
file_modification_time	`TIMESTAMP`	Last modification timestamp of the input file.	`2021-12-20 20:05:21`	10.5
file_block_start	`LONG`	Start offset of the block being read, in bytes.	0	13.0
file_block_length	`LONG`	Length of the block being read, in bytes.	628	13.0

Examples

Use in a basic file-based data source reader

df = spark.read \
  .format("csv") \
  .schema(schema) \
  .load("dbfs:/tmp/*") \
  .select("*", "_metadata")

display(df)

'''
Result:
+---------+-----+----------------------------------------------------+
|   name  | age |                 _metadata                          |
+=========+=====+====================================================+
|         |     | {                                                  |
|         |     |    "file_path": "dbfs:/tmp/f0.csv",                |
| Debbie  | 18  |    "file_name": "f0.csv",                          |
|         |     |    "file_size": 12,                                |
|         |     |    "file_block_start": 0,                          |
|         |     |    "file_block_length": 12,                        |
|         |     |    "file_modification_time": "2021-07-02 01:05:21" |
|         |     | }                                                  |
+---------+-----+----------------------------------------------------+
|         |     | {                                                  |
|         |     |    "file_path": "dbfs:/tmp/f1.csv",                |
| Frank   | 24  |    "file_name": "f1.csv",                          |
|         |     |    "file_size": 12,                                |
|         |     |    "file_block_start": 0,                          |
|         |     |    "file_block_length": 12,                        |
|         |     |    "file_modification_time": "2021-12-20 02:06:21" |
|         |     | }                                                  |
+---------+-----+----------------------------------------------------+
'''

val df = spark.read
  .format("csv")
  .schema(schema)
  .load("dbfs:/tmp/*")
  .select("*", "_metadata")

display(df_population)

/* Result:
+---------+-----+----------------------------------------------------+
|   name  | age |                 _metadata                          |
+=========+=====+====================================================+
|         |     | {                                                  |
|         |     |    "file_path": "dbfs:/tmp/f0.csv",                |
| Debbie  | 18  |    "file_name": "f0.csv",                          |
|         |     |    "file_size": 12,                                |
|         |     |    "file_block_start": 0,                          |
|         |     |    "file_block_length": 12,                        |
|         |     |    "file_modification_time": "2021-07-02 01:05:21" |
|         |     | }                                                  |
+---------+-----+----------------------------------------------------+
|         |     | {                                                  |
|         |     |    "file_path": "dbfs:/tmp/f1.csv",                |
| Frank   | 24  |    "file_name": "f1.csv",                          |
|         |     |    "file_size": 10,                                |
|         |     |    "file_block_start": 0,                          |
|         |     |    "file_block_length": 12,                        |
|         |     |    "file_modification_time": "2021-12-20 02:06:21" |
|         |     | }                                                  |
+---------+-----+----------------------------------------------------+
*/

Select specific fields

spark.read \
  .format("csv") \
  .schema(schema) \
  .load("dbfs:/tmp/*") \
  .select("_metadata.file_name", "_metadata.file_size")

spark.read
  .format("csv")
  .schema(schema)
  .load("dbfs:/tmp/*")
  .select("_metadata.file_name", "_metadata.file_size")

Use in filters

spark.read \
  .format("csv") \
  .schema(schema) \
  .load("dbfs:/tmp/*") \
  .select("*") \
  .filter(col("_metadata.file_name") == lit("test.csv"))

spark.read
  .format("csv")
  .schema(schema)
  .load("dbfs:/tmp/*")
  .select("*")
  .filter(col("_metadata.file_name") === lit("test.csv"))

Use in COPY INTO

COPY INTO my_delta_table
FROM (
  SELECT *, _metadata FROM 's3://my-bucket/csvData'
)
FILEFORMAT = CSV

Use in Auto Loader

Note

When writing the _metadata column, we rename it to source_metadata. Writing it as _metadata would make it impossible to access the metadata column in the target table, because if the data source contains a column named _metadata, queries will return the column from the data source, and not the file metadata.

spark.readStream \
  .format("cloudFiles") \
  .option("cloudFiles.format", "csv") \
  .schema(schema) \
  .load("s3://my-bucket/csvData") \
  .selectExpr("*", "_metadata as source_metadata") \
  .writeStream \
  .option("checkpointLocation", checkpointLocation) \
  .start(targetTable)

spark.readStream
  .format("cloudFiles")
  .option("cloudFiles.format", "csv")
  .schema(schema)
  .load("s3://my-bucket/csvData")
  .selectExpr("*", "_metadata as source_metadata")
  .writeStream
  .option("checkpointLocation", checkpointLocation)
  .start(targetTable)