Parquet

ProphecySparkBasicsPython0.0.1+ProphecySparkBasicsScala0.0.1+Databricks UC Single Cluster14.3+Databricks UC Shared14.3+Livy3.2.0+

The Parquet file type:

• Is an open-source columnar file format designed for efficient data storage and retrieval.
• Handles large volumes of data by supporting complex predicate pushdown, nested schemas, and a wide variety of column encoding types.

Parameters

Parameter	Tab	Description
Location	Location	File path to read from or write to the Parquet file.
Schema	Properties	Schema to apply on the loaded data. In the Source gem, you can define or edit the schema visually or in JSON code. In the Target gem, you can view the schema visually or as JSON code.

Source

The Source gem reads data from Parquet files and allows you to optionally specify the following additional properties.

Source properties

| Property name | Description | Default | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | --------------------------------------------------------------------- | --- | | Description | Description of your dataset. | None | | Use user-defined schema | Whether to use the schema you define. | false | | Merge schema | Whether the Target gem should merge schemas from all the Parquet part-files collected. This overrides spark.sql.parquet.mergeSchema. | (value of spark.sql.
parquet.
mergeSchema) | | | Datetime Rebase Mode | Specify the rebasing mode for the values of the DATE, TIMESTAMP_MILLIS, TIMESTAMP_MICROS logical types from the Julian to Proleptic Gregorian calendar.
For a list of the possible values, see Supported Datetime rebase modes. | (value of spark.sql.
parquet
.datetimeRebaseModeInRead) | | Int96 Rebase Mode | Specify the rebasing mode for INT96 timestamps from the Julian to Proleptic Gregorian calendar. For a list of the possible values, see Supported Int96 rebase modes. | (value of spark.sql.
parquet
.int96RebaseModeInRead) | | Recursive File Lookup | Whether to recursively load files and disable partition inferring. If the data source explicitly specifies the partitionSpec when therecursiveFileLookup is true, the Source gem throws an exception. | false | | Path Global Filter | Glob pattern to only include files with paths matching the pattern. The syntax follows GlobFilter and does not change the behavior of partition discovery. | None | | Modified Before | Timestamp to only include files with modification times occurring before the time you specify. The timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00). | None | | Modified After | Timestamp to only include files with modification times occurring after the time you specify. The timestamp must be in the following form: YYYY-MM-DDTHH:mm:ss (e.g. 2020-06-01T13:00:00). | None |

Supported Int96 rebase modes

Int96 rebase mode	Description
EXCEPTION	Fails in reads of ancient INT96 timestamps that are ambiguous between two calendars.
CORRECTED	Loads INT96 timestamps without rebasing.
LEGACY	Rebases ancient INT96 timestamps from the Julian to Proleptic Gregorian.

Supported Datetime rebase modes

Datetime rebase mode	Description
EXCEPTION	Fails in reads of ancient dates and timestamps that are ambiguous between two calendars.
CORRECTED	Loads dates and timestamps without rebasing.
LEGACY	Rebases ancient dates and timestamps from the Julian to Proleptic Gregorian.

Example

Compiled code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def read_parquet(spark: SparkSession) -> DataFrame:
    return spark.read\
        .format("parquet")\
        .option("mergeSchema", True)\
        .load("dbfs:/FileStore/Users/parquet/test.parquet")

Scala

object read_parquet {

  def apply(spark: SparkSession): DataFrame =
    spark.read
        .format("parquet")
        .option("mergeSchema", true)
        .load("dbfs:/FileStore/Users/parquet/test.parquet")

}

---

Target

The Target gem writes data to Parquet files and allows you to optionally specify the following additional properties.

Target properties

| Property name | Description | Default | | ----------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------- | ---- | | Description | Description of your dataset. | None | | Partition Columns | List of columns to partition the Parquet file by. | | None | | Compression Codec | Compression codec when writing to the Parquet file.
The Parquet file supports the following codecs: none, uncompressed, gzip, lz4, snappy, lzo, brotli, and zstd. This overrides the spark.sql.parquet.compression.codec parameter. | snappy | | Write Mode | How to handle existing data. For a list of the possible values, see Supported write modes. | error |

Supported write modes

Write mode	Description
error	If the data already exists, throw an exception.
overwrite	If the data already exists, overwrite the data with the contents of the DataFrame.
append	If the data already exists, append the contents of the DataFrame.
ignore	If the data already exists, do nothing with the contents of the DataFrame. This is similar to the CREATE TABLE IF NOT EXISTS clause in SQL.

Example

Compiled code

tip

To see the compiled code of your project, switch to the Code view in the project header.

Python

def write_parquet(spark: SparkSession, in0: DataFrame):
    in0.write\
        .format("parquet")\
        .mode("overwrite")\
        .save("dbfs:/data/test_output.parquet")

Scala

object write_parquet {
  def apply(spark: SparkSession, in: DataFrame): Unit =
    in.write
        .format("parquet")
        .mode("overwrite")
        .save("dbfs:/data/test_output.parquet")
}