Common data loading patterns

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Auto Loader simplifies a number of common data ingestion tasks. This quick reference provides examples for several popular patterns.

Filtering directories or files using glob patterns

Glob patterns can be used for filtering directories and files when provided in the path.

Pattern Description
? Matches any single character
* Matches zero or more characters
[abc] Matches a single character from character set {a,b,c}.
[a-z] Matches a single character from the character range {a…z}.
[^a] Matches a single character that is not from character set or range {a}. Note that the ^ character must occur immediately to the right of the opening bracket.
{ab,cd} Matches a string from the string set {ab, cd}.
{ab,c{de, fh}} Matches a string from the string set {ab, cde, cfh}.

Use the path for providing prefix patterns, for example:

Python

df = spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", <format>) \
  .schema(schema) \
  .load("<base-path>/*/files")

Scala

val df = spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", <format>)
  .schema(schema)
  .load("<base-path>/*/files")

Important

You need to use the option pathGlobFilter for explicitly providing suffix patterns. The path only provides a prefix filter.

For example, if you would like to parse only png files in a directory that contains files with different suffixes, you can do:

Python

df = spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "binaryFile") \
  .option("pathGlobfilter", "*.png") \
  .load(<base-path>)

Scala

val df = spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "binaryFile")
  .option("pathGlobfilter", "*.png")
  .load(<base-path>)

Note

The default globbing behavior of Auto Loader is different than the default behavior of other Spark file sources. Add .option("cloudFiles.useStrictGlobber", "true") to your read to use globbing that matches default Spark behavior against file sources. See the following table for more on globbing:

Pattern File path Default globber Strict globber
/a/b /a/b/c/file.txt Yes Yes
/a/b /a/b_dir/c/file.txt No No
/a/b /a/b.txt No No
/a/b/ /a/b.txt No No
/a/*/c/ /a/b/c/file.txt Yes Yes
/a/*/c/ /a/b/c/d/file.txt Yes Yes
/a/*/c/ /a/b/x/y/c/file.txt Yes No
/a/*/c /a/b/c_file.txt Yes No
/a/*/c/ /a/b/c_file.txt Yes No
/a/*/c/ /a/*/cookie/file.txt Yes No
/a/b* /a/b.txt Yes Yes
/a/b* /a/b/file.txt Yes Yes
/a/{0.txt,1.txt} /a/0.txt Yes Yes
/a/*/{0.txt,1.txt} /a/0.txt No No
/a/b/[cde-h]/i/ /a/b/c/i/file.txt Yes Yes

Enable easy ETL

An easy way to get your data into Delta Lake without losing any data is to use the following pattern and enabling schema inference with Auto Loader. Databricks recommends running the following code in an Azure Databricks job for it to automatically restart your stream when the schema of your source data changes. By default, the schema is inferred as string types, any parsing errors (there should be none if everything remains as a string) will go to _rescued_data, and any new columns will fail the stream and evolve the schema.

Python

spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "json") \
  .option("cloudFiles.schemaLocation", "<path-to-schema-location>") \
  .load("<path-to-source-data>") \
  .writeStream \
  .option("mergeSchema", "true") \
  .option("checkpointLocation", "<path-to-checkpoint>") \
  .start("<path_to_target")

Scala

spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "json")
  .option("cloudFiles.schemaLocation", "<path-to-schema-location>")
  .load("<path-to-source-data>")
  .writeStream
  .option("mergeSchema", "true")
  .option("checkpointLocation", "<path-to-checkpoint>")
  .start("<path_to_target")

Prevent data loss in well-structured data

When you know your schema, but want to know whenever you receive unexpected data, Databricks recommends using the rescuedDataColumn.

Python

spark.readStream.format("cloudFiles") \
  .schema(expected_schema) \
  .option("cloudFiles.format", "json") \
  # will collect all new fields as well as data type mismatches in _rescued_data
  .option("cloudFiles.schemaEvolutionMode", "rescue") \
  .load("<path-to-source-data>") \
  .writeStream \
  .option("checkpointLocation", "<path-to-checkpoint>") \
  .start("<path_to_target")

Scala

spark.readStream.format("cloudFiles")
  .schema(expected_schema)
  .option("cloudFiles.format", "json")
  // will collect all new fields as well as data type mismatches in _rescued_data
  .option("cloudFiles.schemaEvolutionMode", "rescue")
  .load("<path-to-source-data>")
  .writeStream
  .option("checkpointLocation", "<path-to-checkpoint>")
  .start("<path_to_target")

If you want your stream to stop processing if a new field is introduced that doesn't match your schema, you can add:

.option("cloudFiles.schemaEvolutionMode", "failOnNewColumns")

Enable flexible semi-structured data pipelines

When you're receiving data from a vendor that introduces new columns to the information they provide, you may not be aware of exactly when they do it, or you may not have the bandwidth to update your data pipeline. You can now leverage schema evolution to restart the stream and let Auto Loader update the inferred schema automatically. You can also leverage schemaHints for some of the "schemaless" fields that the vendor may be providing.

Python

spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "json") \
  # will ensure that the headers column gets processed as a map
  .option("cloudFiles.schemaHints",
          "headers map<string,string>, statusCode SHORT") \
  .load("/api/requests") \
  .writeStream \
  .option("mergeSchema", "true") \
  .option("checkpointLocation", "<path-to-checkpoint>") \
  .start("<path_to_target")

Scala

spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "json")
  // will ensure that the headers column gets processed as a map
  .option("cloudFiles.schemaHints",
          "headers map<string,string>, statusCode SHORT")
  .load("/api/requests")
  .writeStream
  .option("mergeSchema", "true")
  .option("checkpointLocation", "<path-to-checkpoint>")
  .start("<path_to_target")

Transform nested JSON data

Because Auto Loader infers the top level JSON columns as strings, you can be left with nested JSON objects that require further transformations. You can use the semi-structured data access APIs to further transform complex JSON content.

Python

spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "json") \
  # The schema location directory keeps track of your data schema over time
  .option("cloudFiles.schemaLocation", "<path-to-checkpoint>") \
  .load("<source-data-with-nested-json>") \
  .selectExpr(
    "*",
    "tags:page.name",    # extracts {"tags":{"page":{"name":...}}}
    "tags:page.id::int", # extracts {"tags":{"page":{"id":...}}} and casts to int
    "tags:eventType"     # extracts {"tags":{"eventType":...}}
  )

Scala

spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "json")
  // The schema location directory keeps track of your data schema over time
  .option("cloudFiles.schemaLocation", "<path-to-checkpoint>")
  .load("<source-data-with-nested-json>")
  .selectExpr(
    "*",
    "tags:page.name",     // extracts {"tags":{"page":{"name":...}}}
    "tags:page.id::int",  // extracts {"tags":{"page":{"id":...}}} and casts to int
    "tags:eventType"      // extracts {"tags":{"eventType":...}}
  )

Infer nested JSON data

When you have nested data, you can use the cloudFiles.inferColumnTypes option to infer the nested structure of your data and other column types.

Python

spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "json") \
  # The schema location directory keeps track of your data schema over time
  .option("cloudFiles.schemaLocation", "<path-to-checkpoint>") \
  .option("cloudFiles.inferColumnTypes", "true") \
  .load("<source-data-with-nested-json>")

Scala

spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "json")
  // The schema location directory keeps track of your data schema over time
  .option("cloudFiles.schemaLocation", "<path-to-checkpoint>")
  .option("cloudFiles.inferColumnTypes", "true")
  .load("<source-data-with-nested-json>")

Load CSV files without headers

Python

df = spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "csv") \
  .option("rescuedDataColumn", "_rescued_data") \ # makes sure that you don't lose data
  .schema(<schema>) \ # provide a schema here for the files
  .load(<path>)

Scala

val df = spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "csv")
  .option("rescuedDataColumn", "_rescued_data") // makes sure that you don't lose data
  .schema(<schema>) // provide a schema here for the files
  .load(<path>)

Enforce a schema on CSV files with headers

Python

df = spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "csv") \
  .option("header", "true") \
  .option("rescuedDataColumn", "_rescued_data") \ # makes sure that you don't lose data
  .schema(<schema>) \ # provide a schema here for the files
  .load(<path>)

Scala

val df = spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "csv")
  .option("header", "true")
  .option("rescuedDataColumn", "_rescued_data") // makes sure that you don't lose data
  .schema(<schema>) // provide a schema here for the files
  .load(<path>)

Ingest image or binary data to Delta Lake for ML

Once the data is stored in Delta Lake, you can run distributed inference on the data. See Perform distributed inference using pandas UDF.

Python

spark.readStream.format("cloudFiles") \
  .option("cloudFiles.format", "binaryFile") \
  .load("<path-to-source-data>") \
  .writeStream \
  .option("checkpointLocation", "<path-to-checkpoint>") \
  .start("<path_to_target")

Scala

spark.readStream.format("cloudFiles")
  .option("cloudFiles.format", "binaryFile")
  .load("<path-to-source-data>")
  .writeStream
  .option("checkpointLocation", "<path-to-checkpoint>")
  .start("<path_to_target")

Auto Loader syntax for DLT

Delta Live Tables provides slightly modified Python syntax for Auto Loader adds SQL support for Auto Loader.

The following examples use Auto Loader to create datasets from CSV and JSON files:

Python

@dlt.table
def customers():
  return (
    spark.readStream.format("cloudFiles")
      .option("cloudFiles.format", "csv")
      .load("/databricks-datasets/retail-org/customers/")
  )

@dlt.table
def sales_orders_raw():
  return (
    spark.readStream.format("cloudFiles")
      .option("cloudFiles.format", "json")
      .load("/databricks-datasets/retail-org/sales_orders/")
  )

SQL

CREATE OR REFRESH STREAMING TABLE customers
AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv")

CREATE OR REFRESH STREAMING TABLE sales_orders_raw
AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json")

You can use supported format options with Auto Loader. Using the map() function, you can pass options to the cloud_files() method. Options are key-value pairs, where the keys and values are strings. The following describes the syntax for working with Auto Loader in SQL:

CREATE OR REFRESH STREAMING TABLE <table-name>
AS SELECT *
  FROM cloud_files(
    "<file-path>",
    "<file-format>",
    map(
      "<option-key>", "<option_value",
      "<option-key>", "<option_value",
      ...
    )
  )

The following example reads data from tab-delimited CSV files with a header:

CREATE OR REFRESH STREAMING TABLE customers
AS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv", map("delimiter", "\t", "header", "true"))

You can use the schema to specify the format manually; you must specify the schema for formats that do not support schema inference:

Python

@dlt.table
def wiki_raw():
  return (
    spark.readStream.format("cloudFiles")
      .schema("title STRING, id INT, revisionId INT, revisionTimestamp TIMESTAMP, revisionUsername STRING, revisionUsernameId INT, text STRING")
      .option("cloudFiles.format", "parquet")
      .load("/databricks-datasets/wikipedia-datasets/data-001/en_wikipedia/articles-only-parquet")
  )

SQL

CREATE OR REFRESH STREAMING TABLE wiki_raw
AS SELECT *
  FROM cloud_files(
    "/databricks-datasets/wikipedia-datasets/data-001/en_wikipedia/articles-only-parquet",
    "parquet",
    map("schema", "title STRING, id INT, revisionId INT, revisionTimestamp TIMESTAMP, revisionUsername STRING, revisionUsernameId INT, text STRING")
  )

Note

Delta Live Tables automatically configures and manages the schema and checkpoint directories when using Auto Loader to read files. However, if you manually configure either of these directories, performing a full refresh does not affect the contents of the configured directories. Databricks recommends using the automatically configured directories to avoid unexpected side effects during processing.