Interactive SparkΒΆ

This section demonstrates how to make connections to the Data Catalog Metastore and Object Storage. It uses Spark to load data from a public Object Storage file and creates a database. The metadata for the database is managed by the Data Catalog Metastore and the data is copied to your data warehouse bucket. Finally, Spark is used to make a Spark SQL query on the database.

Specify the bucket URI that will act as the data warehouse. Use the warehouse_uri variable and it should have the following format oci://<bucket_name>@<namespace_name>/<prefix>. Update the variable metastore_id with the OCID of the Data Catalog Metastore.

Create a Spark session that connects to the Data Catalog Metastore and the Object Storage that will act as the data warehouse.

from pyspark.sql import SparkSession

warehouse_uri = "<warehouse_uri>"
metastore_id = "<metastore_id>"

spark = SparkSession \
    .builder \
    .appName("Python Spark SQL Hive integration example") \
    .config("spark.sql.warehouse.dir", warehouse_uri) \
    .config("", metastore_id) \
    .enableHiveSupport() \

Load a data file from Object Storage into a Spark DataFrame. Create a database in the Data Catalog Metastore and then save the dataframe as a table. This will write the files to the location specified by the warehouse_uri variable.

database_name = "ODSC_DEMO"
file_path = "oci://hosted-ds-datasets@bigdatadatasciencelarge/synthetic/orcl_attrition.csv"

input_dataframe ="header", "true").csv(file_path)
spark.sql(f"DROP DATABASE IF EXISTS {database_name} CASCADE")
spark.sql(f"CREATE DATABASE {database_name}")

Use Spark SQL to read from the database.

spark_df = spark.sql(f"""
                     SELECT EducationField, SalaryLevel, JobRole FROM {database_name}.{table_name} limit 10