Oracle Cloud Infrastructure Documentation

Data Flow and Delta Lake

Data Flow supports Delta Lake by default when your Applications run Spark 3.2.1 or later.

Delta Lake lets you build a Lakehouse architecture on top of data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing on top of existing data lakes. Delta Lake 3.1.0 is supported with the Data Flow Spark 3.5.0 processing engine, Delta Lake 2.0.1 and 1.2.1 are supported with the Data Flow Spark 3.2.1 processing engine.

To use Delta Lake with Data Flow:
  • The Spark version in Data Flow must be 3.2.1 (or later).
  • Use the delta format.
For more information on Delta Lake and its use, see the Delta Lake release notes and Delta Lake documentation.

Load Delta Lake

Follow these steps to load Delta Lake to use with Data Flow.

Use the Spark configuration property, spark.oracle.deltalake.version, to specify which version of Delta Lake to use. Set it to one of the following values:
Spark.oracle.deltalake.version Values
Spark Version Value of spark.oracle.deltalake.version Binaries loaded
3.5.0 3.1.0 Delta Lake 3.1.0
3.2.1 2.0.1 Delta Lake 2.0.1
3.2.1 1.2.1 Delta Lake 1.2.1
3.5.0, 3.2.1 none No Delta Lake binaries are loaded, you must provide them.
Note

If you don't set a value for spark.oracle.deltalake.version, then the Delta Lake 1.2.1 binaries are loaded by default.

If you set spark.oracle.deltalake.version to none, then you must provide the Delta Lake dependency libraries as part of the application JAR. More information is available in the Delta Lake public documentation.

For example, to load Delta Lake 3.1.0, package these libraries:
  • delta-storage-3.1.0.jar
  • delta-spark_2.12-3.1.0.jar
  • delta-contribs_2.12-3.1.0.jar
and follow these steps:
  1. For Java or Scala applications, provide the Delta Lake 3.1.0 dependency from the maven repository:
    <dependency>
    <groupId>io.delta</groupId>
    <artifactId>delta-spark_2.12</artifactId>
    <version>3.1.0</version>
</dependency>
<dependency>
    <groupId>io.delta</groupId>
    <artifactId>delta-contribs_2.12</artifactId>
    <version>3.1.0</version>
</dependency>
    Or for Python applications, package the Delta Lake library and provide it to the application.
  2. Set the Spark configuration to enable Delta Lake:
    spark.delta.logStore.oci.impl -> io.delta.storage.OracleCloudLogStore
spark.sql.extensions -> io.delta.sql.DeltaSparkSessionExtension
spark.sql.catalog.spark_catalog -> org.apache.spark.sql.delta.catalog.DeltaCatalog
For example, to load Delta Lake 2.0.1, package these libraries:
  • delta-core_2.12-2.0.1.jar
  • delta-contribs_2.12-2.0.1.jar
  • delta-storage-2.0.1.jar
and follow these steps:
  1. For Java or Scala applications, provide the Delta Lake 2.0.1 dependency from the maven repository:
    <dependency>
  <groupId>io.delta</groupId>
  <artifactId>delta-core_2.12</artifactId>
  <version>2.0.1</version>
</dependency> 
<dependency>
  <groupId>io.delta</groupId>
  <artifactId>delta-contribs_2.12</artifactId>
  <version>2.0.1</version>
  </dependency>
    Or for Python applications, package the Delta Lake library and provide it to the application.
  2. Set the Spark configuration to enable Delta Lake:
    spark.delta.logStore.oci.impl -> io.delta.storage.OracleCloudLogStore
spark.sql.extensions -> io.delta.sql.DeltaSparkSessionExtension
spark.sql.catalog.spark_catalog -> org.apache.spark.sql.delta.catalog.DeltaCatalog
Note

You can also enable Delta Lake in the advanced options provided in the Console, when creating an application or running an application.

Sample Use of the Delta Lake API

Samples of using the Delta Lake API with Data Flow.

The Data Flow Spark engine supports the delta format by default. Delta Lake APIs are available for Java, Python, and Scala languages. If you are using Delta Lake Python APIs, use the custom archive.zip dependency packager, include the delta-spark package, as described in Spark-Submit Functionality in Data Flow.

Usage Samples

Java or Scala
spark.read().format("delta").load(<path_to_Delta_table>)
df.write().format("delta").save(<path_to_Delta_table>)
val deltaTable = io.delta.tables.DeltaTable.forPath(spark, <path_to_Delta_table>)
deltaTable.vacuum()
Python
spark.read.format("delta").option("versionAsOf", 1).load(<path_to_Delta_table>)
from delta.tables import *
deltaTable = DeltaTable.forPath(spark, <path_to_Delta_table>)
deltaTable.vacuum()
deltaTable.history()
SQL
spark.sql("CONVERT TO DELTA parquet.`" + <path_to_Parquet_table> + "`");
spark.sql("DESCRIBE HISTORY delta.`" + <path_to_Delta_table> + "`");

Examples

Here are some code examples to help you start using Delta Lake with Data Flow

Examples are available from the Oracle samples on GitHub.