Membuat tabel Apache Iceberg dengan metadata di metastore BigLake

Dokumen ini menunjukkan cara menjalankan Dataproc Serverless untuk Spark SQL dan workload batch PySpark guna membuat tabel Apache Iceberg dengan metadata yang disimpan di metastore BigLake. Untuk informasi tentang cara lain menjalankan kode Spark, lihat Menjalankan kode PySpark di notebook BigQuery dan Menjalankan beban kerja Apache Spark

Sebelum memulai

Jika Anda belum melakukannya, buat Google Cloud project dan bucket Cloud Storage.

  1. Menyiapkan project

    1. 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.

    2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Go to project selector

    3. Enable the Dataproc, BigQuery, and Cloud Storage APIs.

      Enable the APIs

      4.

    4. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

      Go to project selector

    5. Enable the Dataproc, BigQuery, and Cloud Storage APIs.

      Enable the APIs

      6.

    6. Buat bucket Cloud Storage di project Anda.

      1. In the Google Cloud console, go to the Cloud Storage Buckets page.

        Go to Buckets

      2. Click Create.
      3. On the Create a bucket page, enter your bucket information. To go to the next step, click Continue.
        1. In the Get started section, do the following:
          • Enter a globally unique name that meets the bucket naming requirements.
          • To add a bucket label, expand the Labels section (), click Add label, and specify a key and a value for your label.
        2. In the Choose where to store your data section, do the following:
          1. Select a Location type.
          2. Choose a location where your bucket's data is permanently stored from the Location type drop-down menu.
          3. To set up cross-bucket replication, select Add cross-bucket replication via Storage Transfer Service and follow these steps:

            Set up cross-bucket replication

            1. In the Bucket menu, select a bucket.

            2. In the Replication settings section, click Configure to configure settings for the replication job.

              The Configure cross-bucket replication pane appears.

              • To filter objects to replicate by object name prefix, enter a prefix that you want to include or exclude objects from, then click Add a prefix.
              • To set a storage class for the replicated objects, select a storage class from the Storage class menu. If you skip this step, the replicated objects will use the destination bucket's storage class by default.
              • Click Done.
        3. In the Choose how to store your data section, do the following:
          1. Select a default storage class for the bucket or Autoclass for automatic storage class management of your bucket's data.
          2. To enable hierarchical namespace, in the Optimize storage for data-intensive workloads section, select Enable hierarchical namespace on this bucket.
        4. In the Choose how to control access to objects section, select whether or not your bucket enforces public access prevention, and select an access control method for your bucket's objects.
        5. In the Choose how to protect object data section, do the following:
          • Select any of the options under Data protection that you want to set for your bucket.
            • To enable soft delete, click the Soft delete policy (For data recovery) checkbox, and specify the number of days you want to retain objects after deletion.
            • To set Object Versioning, click the Object versioning (For version control) checkbox, and specify the maximum number of versions per object and the number of days after which the noncurrent versions expire.
            • To enable the retention policy on objects and buckets, click the Retention (For compliance) checkbox, and then do the following:
              • To enable Object Retention Lock, click the Enable object retention checkbox.
              • To enable Bucket Lock, click the Set bucket retention policy checkbox, and choose a unit of time and a length of time for your retention period.
          • To choose how your object data will be encrypted, expand the Data encryption section (), and select a Data encryption method.
      4. Click Create.

    7. Berikan peran BigQuery Data Editor (roles/bigquery.dataEditor) ke akun layanan default Compute Engine, PROJECT_NUMBER[email protected]. Untuk mengetahui petunjuknya, lihat Memberikan satu peran.

      Contoh Google Cloud CLI:

      gcloud projects add-iam-policy-binding PROJECT_ID \
    --member PROJECT_NUMBER[email protected] \
--role roles/bigquery.dataEditor

      Catatan:

      • PROJECT_ID dan PROJECT_NUMBER tercantum di bagian Project info di Dasbor konsol Google Cloud .

      8. Pemetaan resource OSS ke resource BigQuery

      Perhatikan pemetaan berikut antara istilah resource open source dan resource BigQuery:

      Referensi OSS Resource BigQuery
      Namespace, Database Set data
      Tabel Berpartisi atau Tidak Berpartisi Tabel
      Lihat Lihat

      Membuat tabel Iceberg

      Bagian ini menunjukkan cara membuat tabel Iceberg dengan metadata di metastore BigLake menggunakan workload batch Spark SQL dan PySpark Dataproc Serverless.

      Spark SQL

      Menjalankan beban kerja Spark SQL untuk membuat tabel Iceberg

      Langkah-langkah berikut menunjukkan cara menjalankan beban kerja batch SQL Spark Serverless Dataproc untuk membuat tabel Iceberg dengan metadata tabel yang disimpan di metastore BigLake.

      1. Salin perintah Spark SQL berikut secara lokal atau di Cloud Shell ke dalam file iceberg-table.sql.

        USE CATALOG_NAME;
CREATE NAMESPACE IF NOT EXISTS 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;

        Ganti kode berikut:

        • CATALOG_NAME: Nama katalog Iceberg.
        • BUCKET dan WAREHOUSE_FOLDER: Bucket dan folder Cloud Storage yang digunakan sebagai direktori penyimpanan Iceberg.

      2. Jalankan perintah berikut secara lokal atau di Cloud Shell dari direktori yang berisi iceberg-table.sql untuk mengirimkan beban kerja Spark SQL. 

        gcloud dataproc batches submit spark-sql iceberg-table.sql \
    --project=PROJECT_ID \
    --region=REGION \
    --deps-bucket=BUCKET_NAME \
    --version=2.2 \
    --subnet=SUBNET_NAME \
    --properties="spark.sql.catalog.CATALOG_NAME=org.apache.iceberg.spark.SparkCatalog,spark.sql.catalog.CATALOG_NAME.catalog-impl=org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog,spark.sql.catalog.CATALOG_NAME.gcp_project=PROJECT_ID,spark.sql.catalog.CATALOG_NAME.gcp_location=LOCATION,spark.sql.catalog.CATALOG_NAME.warehouse=gs://BUCKET/WAREHOUSE_FOLDER"

        Catatan:

        • PROJECT_ID: Google Cloud Project ID Anda. Project ID tercantum di bagian Project info di Dasbor konsol Google Cloud .
        • REGION: Region Compute Engine yang tersedia untuk menjalankan beban kerja.
        • BUCKET_NAME: Nama bucket Cloud Storage Anda. Spark mengupload dependensi workload ke folder /dependencies di bucket ini sebelum menjalankan workload batch. WAREHOUSE_FOLDER terletak di bucket ini.
        • --version: Runtime Dataproc Serverless versi 2.2 atau yang lebih baru.
        • SUBNET_NAME: Nama subnet VPC di REGION. Jika Anda menghilangkan tanda ini, Dataproc Serverless akan memilih subnet default di region sesi. Dataproc Serverless mengaktifkan Akses Google Pribadi (PGA) di subnet. Untuk persyaratan konektivitas jaringan, lihat Dataproc Serverless untuk konfigurasi jaringan Spark.
        • LOCATION: Lokasi BigQuery yang didukung. Lokasi defaultnya adalah "US".
        • --properties Properti katalog.

      3. Melihat metadata tabel di BigQuery

        1. Di Google Cloud konsol, buka halaman BigQuery.

          Buka BigQuery Studio

        2. Melihat metadata tabel Iceberg.

      PySpark

      Langkah-langkah berikut menunjukkan cara menjalankan beban kerja batch PySpark Serverless Dataproc untuk membuat tabel Iceberg dengan metadata tabel yang disimpan di metastore BigLake.

      1. Salin kode PySpark berikut secara lokal atau di Cloud Shell ke dalam file iceberg-table.py. 
        from pyspark.sql import SparkSession
spark = SparkSession.builder.appName("iceberg-table-example").getOrCreate()

catalog = "CATALOG_NAME"
namespace = "NAMESPACE"

spark.sql(f"USE `{catalog}`;")
spark.sql(f"CREATE NAMESPACE IF NOT EXISTS `{namespace}`;")
spark.sql(f"USE `{namespace}`;")

# Create table and display schema
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;")

# Insert table data.
spark.sql("INSERT INTO example_iceberg_table VALUES (1, 'first row');")

# Alter table, then display schema. 
spark.sql("ALTER TABLE example_iceberg_table ADD COLUMNS (newDoubleCol double);")
spark.sql("DESCRIBE example_iceberg_table;")

        Ganti kode berikut:

        • CATALOG_NAME dan NAMESPACE: Nama katalog dan namespace Iceberg digabungkan untuk mengidentifikasi tabel Iceberg (catalog.namespace.table_name).
      2. Jalankan perintah berikut secara lokal atau di Cloud Shell dari direktori yang berisi iceberg-table.py untuk mengirimkan beban kerja PySpark. 
        gcloud dataproc batches submit pyspark iceberg-table.py \
    --project=PROJECT_ID \
    --region=REGION \
    --deps-bucket=BUCKET_NAME \
    --version=2.2 \
    --subnet=SUBNET_NAME \
    --properties="spark.sql.catalog.CATALOG_NAME=org.apache.iceberg.spark.SparkCatalog,spark.sql.catalog.CATALOG_NAME.catalog-impl=org.apache.iceberg.gcp.bigquery.BigQueryMetastoreCatalog,spark.sql.catalog.CATALOG_NAME.gcp_project=PROJECT_ID,spark.sql.catalog.CATALOG_NAME.gcp_location=LOCATION,spark.sql.catalog.CATALOG_NAME.warehouse=gs://BUCKET/WAREHOUSE_FOLDER"

        Catatan:

        • PROJECT_ID: Google Cloud Project ID Anda. Project ID tercantum di bagian Project info di Google Cloud Dasbor konsol.
        • REGION: Region Compute Engine yang tersedia untuk menjalankan beban kerja.
        • BUCKET_NAME: Nama bucket Cloud Storage Anda. Spark mengupload dependensi workload ke folder /dependencies di bucket ini sebelum menjalankan workload batch.
        • --version: Runtime Dataproc Serverless versi 2.2 atau yang lebih baru.
        • SUBNET_NAME: Nama subnet VPC di REGION. Jika Anda menghilangkan tanda ini, Dataproc Serverless akan memilih subnet default di region sesi. Dataproc Serverless mengaktifkan Akses Google Pribadi (PGA) di subnet. Untuk persyaratan konektivitas jaringan, lihat Dataproc Serverless untuk konfigurasi jaringan Spark.
        • LOCATION: Lokasi BigQuery yang didukung. Lokasi defaultnya adalah "US".
        • BUCKET dan WAREHOUSE_FOLDER: Bucket dan folder Cloud Storage yang digunakan sebagai direktori penyimpanan Iceberg.
        • --properties: Properti katalog.
      3. Lihat skema tabel di BigQuery.
        1. Di Google Cloud konsol, buka halaman BigQuery. Buka BigQuery Studio
        2. Melihat metadata tabel Iceberg.