ORC

Dependencies & cluster requirements

Dependencies:

ProphecySparkBasicsPython 0.0.1+
ProphecySparkBasicsScala 0.0.1+

Cluster requirements:

UC dedicated clusters 14.3+ supported
UC standard clusters 14.3+ supported
Livy clusters 3.2.0+ supported

The ORC (Optimized Row Columnar) file type:

Is a columnar file format designed for Spark and Hadoop workloads.
Offers high compression ratios, which helps reduce storage costs.
Optimizes for large streaming reads, but with integrated support for finding required rows quickly.
Is type-aware, which means it can choose an encoding for the type and builds an internal index while you write to the file.

Parameters

Parameter	Tab	Description
Location	Location	File path to read from or write to the ORC 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 ORC 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
Recursive File Lookup	Whether to recursively load files and disable partition inferring. If the data source explicitly specifies the `partitionSpec` when the`recursiveFileLookup` is `true`, the Source gem throws an exception.	false

Example

Compiled code

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

def read_orc(spark: SparkSession) -> DataFrame:
 return spark.read\
 .format("orc")\
 .load("dbfs:/FileStore/Users/orc/test.orc")

object read_orc {

 def apply(spark: SparkSession): DataFrame =
 spark.read
 .format("orc")
 .load("dbfs:/FileStore/Users/orc/test.orc")

}

Target

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

Target properties

Property name	Description	Default
Description	Description of your dataset.	None
Write Mode	How to handle existing data. For a list of the possible values, see Supported write modes.	`error`
Partition Columns	List of columns to partition the ORC files by.	None
Compression Codec	Compression codec when writing to the ORC file. The ORC file supports the following codecs: `none`, `snappy`, `zlib`, and `lzo`.	`snappy`

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

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

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

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

Overview

Sources and targets

Data processing

Advanced

Parameters

Source

Source properties

Example

Compiled code

Target

Target properties

Supported write modes

Example

Compiled code

​Parameters

​Source

​Source properties

​Example

​Compiled code

​Target

​Target properties

​Supported write modes

​Example

​Compiled code

Parameters

Source

Source properties

Example

Compiled code

Target

Target properties

Supported write modes

Example

Compiled code