Create an Apache Iceberg table with metadata in BigQuery Metastore
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This document shows you how to create an Apache Iceberg table with metadata in
BigQuery Metastore using the Dataproc Jobs service,
the Spark SQL CLI or
the Zeppelin web interface
running on a Dataproc cluster.
Before you begin
If you haven't done so, create a Google Cloud project, a
Cloud Storage bucket,
and a Dataproc cluster.
Set up your project
Sign in to your Google Cloud account. If you're new to
Google Cloud,
create an account to evaluate how our products perform in
real-world scenarios. New customers also get $300 in free credits to
run, test, and deploy workloads.
In the Google Cloud console, on the project selector page,
select or create a Google Cloud project.
If you want to run the Zeppelin web interface example in this guide,
you must use or create a Dataproc cluster with
the Zeppelin optional component enabled.
Note the following mapping between open source database and BigQuery
dataset terms:
OSS database
BigQuery dataset
Namespace, Database
Dataset
Partitioned or Unpartitioned Table
Table
View
View
Create an Iceberg table
This section shows you how to create an Iceberg table with metadata in
BigQuery Metastore by submitting a Spark SQL code to the
Dataproc service,
the Spark SQL CLI,
and the Zeppelin component web interface,
which run on a Dataproc cluster.
The examples in this section show you how to submit a Dataproc
Spark SQL job to the Dataproc
service to create an Iceberg table with metadata in BigQuery
using the gcloud CLI, Google Cloud console, or
Dataproc REST API.
Prepare job files
Perform the following steps to create a Spark SQL job file. The file contains
Spark SQL commands to create and update an Iceberg table.
In a local terminal window or in Cloud Shell,
use a text editor, such as the vi or nano, to copy the
following commands into an iceberg-table.sql file, then save the
file in the current directory.
USE CATALOG_NAME;
CREATE NAMESPACE IF NOT EXISTS example_namespace;
USE example_namespace;
DROP TABLE IF EXISTS example_table;
CREATE TABLE example_table (id int, data string) USING ICEBERG LOCATION 'gs://BUCKET/WAREHOUSE_FOLDER';
INSERT INTO example_table VALUES (1, 'first row');
ALTER TABLE example_table ADD COLUMNS (newDoubleCol double);
DESCRIBE TABLE example_table;
Replace the following:
CATALOG_NAME: Iceberg catalog name.
BUCKET and WAREHOUSE_FOLDER: Cloud Storage bucket
and folder used for the Iceberg warehouse.
Use the gsutil tool to copy the local
iceberg-table.sql to your bucket in Cloud Storage.
gsutil cp iceberg-table.sql gs://BUCKET/
Next, download and copy the iceberg-spark-runtime-3.5_2.12-1.5.2 JAR file to
Cloud Storage.
In a local terminal window or in Cloud Shell,
run the following curl command to download the iceberg-spark-runtime-3.5_2.12-1.5.2
JAR file to the current directory.
Select a tab to follow the instructions to submit the Spark SQL job to the
Dataproc service using the gcloud CLI,
Google Cloud console, or Dataproc
REST API.
Perform the following steps to use the Google Cloud console to submit
the Spark SQL job to the Dataproc service to create an
Iceberg table with metadata in BigQuery Metastore.
In the Google Cloud console, go to the Dataproc
Submit a job.
Properties: Click addAdd Property
five times to create a list of five keyvalue input fields, then
copy the following Key and Value pairs to define
five properties.
PROJECT_ID: Your Google Cloud project ID.
Project IDs are listed in the Project info section on
the Google Cloud console Dashboard.
region where your cluster is located.
LOCATION: A supported BigQuery location.
The default location is "US".
BUCKET and WAREHOUSE_FOLDER: Cloud Storage bucket
and folder used for the Iceberg warehouse.
Click Submit
To monitor job progress and view job output, go to the Dataproc
Jobs page in the Google Cloud console,
then click the Job ID to open the Job details page.
To view table metadata in BigQuery
In the Google Cloud console, go to the BigQuery page.
You can use the Dataproc
jobs.submit API
to submit the Spark SQL job to the Dataproc service to create an
Iceberg table with metadata in BigQuery Metastore.
Before using any of the request data,
make the following replacements:
PROJECT_ID: Your Google Cloud project ID.
Project IDs are listed in the Project info section on
the Google Cloud console Dashboard.
To monitor job progress and view job output, go to the Dataproc
Jobs page in the Google Cloud console,
then click the Job ID to open the Job details page.
To view table metadata in BigQuery
In the Google Cloud console, go to the BigQuery page.
The following steps show you how to create an Iceberg table with table metadata
stored in BigQuery Metastore using the Spark SQL CLI running on the
master node of a Dataproc cluster.
Use SSH to connect to the master node
of your Dataproc cluster.
In the SSH session terminal, use the vi or nano text editor to copy the
following commands into an iceberg-table.sql file.
SET CATALOG_NAME = `CATALOG_NAME`;
SET BUCKET = `BUCKET`;
SET WAREHOUSE_FOLDER = `WAREHOUSE_FOLDER`;
USE `${CATALOG_NAME}`;
CREATE NAMESPACE IF NOT EXISTS `${CATALOG_NAME}`.example_namespace;
DROP TABLE IF EXISTS `${CATALOG_NAME}`.example_namespace.example_table;
CREATE TABLE `${CATALOG_NAME}`.example_namespace.example_table (id int, data string) USING ICEBERG LOCATION 'gs://${BUCKET}/${WAREHOUSE_FOLDER}';
INSERT INTO `${CATALOG_NAME}`.example_namespace.example_table VALUES (1, 'first row');
ALTER TABLE `${CATALOG_NAME}`.example_namespace.example_table ADD COLUMNS (newDoubleCol double);
DESCRIBE TABLE `${CATALOG_NAME}`.example_namespace.example_table;
Replace the following:
CATALOG_NAME: Iceberg catalog name.
BUCKET and WAREHOUSE_FOLDER: Cloud Storage bucket
and folder used for the Iceberg warehouse.
In the SSH session terminal, run the following spark-sql command to create
the iceberg table.
The following steps show you how to to create an Iceberg table with table
metadata stored in BigQuery Metastore using the Zeppelin web
interface running on the master node of a Dataproc cluster .
In the Google Cloud console, go to the Dataproc Clusters
page.
Select your cluster name to open the Cluster details page.
Click the Web Interfaces tab display a list of Component Gateway
links to the web interfaces of default and optional components
installed on the cluster.
Click the Zeppelin link to open the Zeppelin web interface.
In the Zeppelin web interface, click the anonymous menu, then click
Interpreter to open the Interpreters page.
Add two jars to the Zeppelin Spark interpreter, as follows:
Type "Spark" in Search interpreters box to scroll to the Spark
interpreter section.
Click Save at the bottom of the Spark interpreter section, then
click OK to update the interpreter and restart the Spark interpreter
with the new settings.
From the Zeppelin notebook menu, click Create new note.
In the Create new note dialog, input a name for the notebook, and
accept the default spark interpreter. Click Create to open The
notebook.
Copy the following PySpark code into your Zeppelin notebook
after filling in the variables.
%pyspark
frompyspark.sqlimportSparkSession project_id="PROJECT_ID"catalog="CATALOG_NAME"namespace="NAMESPACE"location="LOCATION"warehouse_dir="gs://BUCKET/WAREHOUSE_DIRECTORY" spark=SparkSession.builder\
.appName("BigQuery Metastore Iceberg")\
.config(f"spark.sql.catalog.{catalog}","org.apache.iceberg.spark.SparkCatalog")\
.config(f"spark.sql.catalog.{catalog}.catalog-impl","org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog")\
.config(f"spark.sql.catalog.{catalog}.gcp_project",f"{project_id}")\
.config(f"spark.sql.catalog.{catalog}.gcp_location",f"{location}")\
.config(f"spark.sql.catalog.{catalog}.warehouse",f"{warehouse_dir}")\
.getOrCreate()
spark.sql(f"USE `{catalog}`;")
spark.sql(f"CREATE NAMESPACE IF NOT EXISTS `{namespace}`;")
spark.sql(f"USE `{namespace}`;") \#Createtableanddisplayschema(withoutLOCATION)
spark.sql("DROP TABLE IF EXISTS example_iceberg_table")
spark.sql("CREATE TABLE example_iceberg_table (id int, data string) USING ICEBERG")
spark.sql("DESCRIBE example_iceberg_table;") \#Inserttabledata.
spark.sql("INSERT INTO example_iceberg_table VALUES (1, 'first row');") \#Altertable,thendisplayschema.
spark.sql("ALTER TABLE example_iceberg_table ADD COLUMNS (newDoubleCol double);") \#Selectanddisplaythecontentsofthetable.
spark.sql("SELECT * FROM example_iceberg_table").show()
Replace the following:
PROJECT_ID: Your Google Cloud project ID.
Project IDs are listed in the Project info section on
the Google Cloud console Dashboard.
CATALOG_NAME and NAMESPACE: The Iceberg catalog name
and namespace combine to identify the Iceberg table (catalog.namespace.table_name).
LOCATION: A supported BigQuery location.
The default location is "US".
BUCKET and WAREHOUSE_DIRECTORY: Cloud Storage bucket
and folder used as Iceberg warehouse directory.
Click the run icon or press Shift-Enter to run the code. When the job
completes, the status message shows "Spark Job Finished", and the output
displays the table contents:
View table metadata in BigQuery
In the Google Cloud console, go to the BigQuery page.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-02-19 UTC."],[],[]]
