Iceberg catalog

Iceberg catalog

An Iceberg catalog is a type of external catalog that is supported by StarRocks from v2.4 onwards. With Iceberg catalogs, you can:

• Directly query data stored in Iceberg without the need to manually create tables.
• Use INSERT INTO or asynchronous materialized views (which are supported from v2.5 onwards) to process data stored in Iceberg and load the data into StarRocks.
• Perform operations on StarRocks to create or drop Iceberg databases and tables, or sink data from StarRocks tables to Parquet-formatted Iceberg tables by using INSERT INTO (this feature is supported from v3.1 onwards).

To ensure successful SQL workloads on your Iceberg cluster, your StarRocks cluster needs to integrate with two \important components:

• Distributed file system (HDFS) or object storage like AWS S3, Microsoft Azure Storage, Google GCS, or other S3-compatible storage system (for example, MinIO)
• Metastore like Hive metastore, AWS Glue, or Tabular

  NOTE

  • If you choose AWS S3 as storage, you can use HMS or AWS Glue as metastore. If you choose any other storage system, you can only use HMS as metastore.
  • If you choose Tabular as metastore, you need to use the Iceberg REST catalog.

Usage notes

• The file formats of Iceberg that StarRocks supports are Parquet and ORC:
  • Parquet files support the following compression formats: SNAPPY, LZ4, ZSTD, GZIP, and NO_COMPRESSION.
  • ORC files support the following compression formats: ZLIB, SNAPPY, LZO, LZ4, ZSTD, and NO_COMPRESSION.
• Iceberg catalogs support v1 tables, and support ORC-formatted v2 tables from StarRocks v3.0 onwards.
• Iceberg catalogs support v1 tables. Additionally, Iceberg catalogs support ORC-formatted v2 tables from StarRocks v3.0 onwards and support Parquet-formatted v2 tables from StarRocks v3.1 onwards.

Integration preparations

Before you create an Iceberg catalog, make sure your StarRocks cluster can integrate with the storage system and metastore of your Iceberg cluster.

AWS IAM

If your Iceberg cluster uses AWS S3 as storage or AWS Glue as metastore, choose your suitable authentication method and make the required preparations to ensure that your StarRocks cluster can access the related AWS cloud resources.

The following authentication methods are recommended:

• Instance profile
• Assumed role
• IAM user

Of the above-mentioned three authentication methods, instance profile is the most widely used.

For more information, see Preparation for authentication in AWS IAM.

HDFS

If you choose HDFS as storage, configure your StarRocks cluster as follows:

• (Optional) Set the username that is used to access your HDFS cluster and Hive metastore. By default, StarRocks uses the username of the FE and BE processes to access your HDFS cluster and Hive metastore. You can also set the username by adding export HADOOP_USER_NAME="<user_name>" at the beginning of the fe/conf/hadoop_env.sh file of each FE and at the beginning of the be/conf/hadoop_env.sh file of each BE. After you set the username in these files, restart each FE and each BE to make the parameter settings take effect. You can set only one username for each StarRocks cluster.
• When you query Iceberg data, the FEs and BEs of your StarRocks cluster use the HDFS client to access your HDFS cluster. In most cases, you do not need to configure your StarRocks cluster to achieve that purpose, and StarRocks starts the HDFS client using the default configurations. You need to configure your StarRocks cluster only in the following situations:
  • High availability (HA) is enabled for your HDFS cluster: Add the hdfs-site.xml file of your HDFS cluster to the $FE_HOME/conf** path of each FE and to the **$BE_HOME/conf path of each BE.
  • View File System (ViewFs) is enabled for your HDFS cluster: Add the core-site.xml file of your HDFS cluster to the $FE_HOME/conf** path of each FE and to the **$BE_HOME/conf path of each BE.

NOTE

If an error indicating an unknown host is returned when you send a query, you must add the mapping between the host names and IP addresses of your HDFS cluster nodes to the /etc/hosts path.

Kerberos authentication

If Kerberos authentication is enabled for your HDFS cluster or Hive metastore, configure your StarRocks cluster as follows:

• Run the kinit -kt keytab_path principal command on each FE and each BE to obtain Ticket Granting Ticket (TGT) from Key Distribution Center (KDC). To run this command, you must have the permissions to access your HDFS cluster and Hive metastore. Note that accessing KDC with this command is time-sensitive. Therefore, you need to use cron to run this command periodically.
• Add JAVA_OPTS="-Djava.security.krb5.conf=/etc/krb5.conf" to the $FE_HOME/conf/fe.conf** file of each FE and to the **$BE_HOME/conf/be.conf file of each BE. In this example, /etc/krb5.conf is the save path of the krb5.conf file. You can modify the path based on your needs.

Create an Iceberg catalog

Syntax

CREATE EXTERNAL CATALOG <catalog_name>
[COMMENT <comment>]
PROPERTIES
(
    "type" = "iceberg",
    MetastoreParams,
    StorageCredentialParams
)

Parameters

catalog_name

The name of the Iceberg catalog. The naming conventions are as follows:

• The name can contain letters, digits (0-9), and underscores (_). It must start with a letter.
• The name is case-sensitive and cannot exceed 1023 characters in length.

comment

The description of the Iceberg catalog. This parameter is optional.

type

The type of your data source. Set the value to iceberg.

MetastoreParams

A set of parameters about how StarRocks integrates with the metastore of your data source.

Hive metastore

If you choose Hive metastore as the metastore of your data source, configure MetastoreParams as follows:

"iceberg.catalog.type" = "hive",
"hive.metastore.uris" = "<hive_metastore_uri>"

NOTE

Before querying Iceberg data, you must add the mapping between the host names and IP addresses of your Hive metastore nodes to the /etc/hosts path. Otherwise, StarRocks may fail to access your Hive metastore when you start a query.

The following table describes the parameter you need to configure in MetastoreParams.

Parameter	Required	Description
iceberg.catalog.type	Yes	The type of metastore that you use for your Iceberg cluster. Set the value to hive.
hive.metastore.uris	Yes	The URI of your Hive metastore. Format: thrift://<metastore_IP_address>:<metastore_port>. If high availability (HA) is enabled for your Hive metastore, you can specify multiple metastore URIs and separate them with commas (,), for example, "thrift://<metastore_IP_address_1>:<metastore_port_1>,thrift://<metastore_IP_address_2>:<metastore_port_2>,thrift://<metastore_IP_address_3>:<metastore_port_3>".

AWS Glue

If you choose AWS Glue as the metastore of your data source, which is supported only when you choose AWS S3 as storage, take one of the following actions:

• To choose the instance profile-based authentication method, configure MetastoreParams as follows:

  "iceberg.catalog.type" = "glue",
  "aws.glue.use_instance_profile" = "true",
  "aws.glue.region" = "<aws_glue_region>"

• To choose the assumed role-based authentication method, configure MetastoreParams as follows:

  "iceberg.catalog.type" = "glue",
  "aws.glue.use_instance_profile" = "true",
  "aws.glue.iam_role_arn" = "<iam_role_arn>",
  "aws.glue.region" = "<aws_glue_region>"

• To choose the IAM user-based authentication method, configure MetastoreParams as follows:

  "iceberg.catalog.type" = "glue",
  "aws.glue.use_instance_profile" = "false",
  "aws.glue.access_key" = "<iam_user_access_key>",
  "aws.glue.secret_key" = "<iam_user_secret_key>",
  "aws.glue.region" = "<aws_s3_region>"

The following table describes the parameters you need to configure in MetastoreParams.

Parameter	Required	Description
iceberg.catalog.type	Yes	The type of metastore that you use for your Iceberg cluster. Set the value to glue.
aws.glue.use_instance_profile	Yes	Specifies whether to enable the instance profile-based authentication method and the assumed role-based authentication method. Valid values: true and false. Default value: false.
aws.glue.iam_role_arn	No	The ARN of the IAM role that has privileges on your AWS Glue Data Catalog. If you use the assumed role-based authentication method to access AWS Glue, you must specify this parameter.
aws.glue.region	Yes	The region in which your AWS Glue Data Catalog resides. Example: us-west-1.
aws.glue.access_key	No	The access key of your AWS IAM user. If you use the IAM user-based authentication method to access AWS Glue, you must specify this parameter.
aws.glue.secret_key	No	The secret key of your AWS IAM user. If you use the IAM user-based authentication method to access AWS Glue, you must specify this parameter.

For information about how to choose an authentication method for accessing AWS Glue and how to configure an access control policy in the AWS IAM Console, see Authentication parameters for accessing AWS Glue.

Tabular

If you use Tabular as metastore, you must specify the metastore type as REST ("iceberg.catalog.type" = "rest"). Configure MetastoreParams as follows:

"iceberg.catalog.type" = "rest",
"iceberg.catalog.uri" = "<rest_server_api_endpoint>",
"iceberg.catalog.credential" = "<credential>",
"iceberg.catalog.warehouse" = "<identifier_or_path_to_warehouse>"

The following table describes the parameters you need to configure in MetastoreParams.

Parameter	Required	Description
iceberg.catalog.type	Yes	The type of metastore that you use for your Iceberg cluster. Set the value to rest.
iceberg.catalog.uri	Yes	The URI of the Tabular service endpoint. Example: https://api.tabular.io/ws.
iceberg.catalog.credential	Yes	The authentication information of the Tabular service.
iceberg.catalog.warehouse	No	The warehouse location or identifier of the Iceberg catalog. Example: s3://my_bucket/warehouse_location or sandbox.

The following example creates an Iceberg catalog named tabular that uses Tabular as metastore:

CREATE EXTERNAL CATALOG tabular
PROPERTIES
(
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "iceberg.catalog.uri" = "https://api.tabular.io/ws",
    "iceberg.catalog.credential" = "t-5Ii8e3FIbT9m0:aaaa-3bbbbbbbbbbbbbbbbbbb",
    "iceberg.catalog.warehouse" = "sandbox"
);

StorageCredentialParams

A set of parameters about how StarRocks integrates with your storage system. This parameter set is optional.

Note the following points:

• If you use HDFS as storage, you do not need to configure StorageCredentialParams and can skip this section. If you use AWS S3, other S3-compatible storage system, Microsoft Azure Storage, or Google GCS as storage, you must configure StorageCredentialParams.
• If you use Tabular as metastore, you do not need to configure StorageCredentialParams and can skip this section. If you use HMS or AWS Glue as metastore, you must configure StorageCredentialParams.

AWS S3

If you choose AWS S3 as storage for your Iceberg cluster, take one of the following actions:

• To choose the instance profile-based authentication method, configure StorageCredentialParams as follows:

  "aws.s3.use_instance_profile" = "true",
  "aws.s3.region" = "<aws_s3_region>"

• To choose the assumed role-based authentication method, configure StorageCredentialParams as follows:

  "aws.s3.use_instance_profile" = "true",
  "aws.s3.iam_role_arn" = "<iam_role_arn>",
  "aws.s3.region" = "<aws_s3_region>"

• To choose the IAM user-based authentication method, configure StorageCredentialParams as follows:

  "aws.s3.use_instance_profile" = "false",
  "aws.s3.access_key" = "<iam_user_access_key>",
  "aws.s3.secret_key" = "<iam_user_secret_key>",
  "aws.s3.region" = "<aws_s3_region>"

The following table describes the parameters you need to configure in StorageCredentialParams.

Parameter	Required	Description
aws.s3.use_instance_profile	Yes	Specifies whether to enable the instance profile-based authentication method and the assumed role-based authentication method. Valid values: true and false. Default value: false.
aws.s3.iam_role_arn	No	The ARN of the IAM role that has privileges on your AWS S3 bucket. If you use the assumed role-based authentication method to access AWS S3, you must specify this parameter.
aws.s3.region	Yes	The region in which your AWS S3 bucket resides. Example: us-west-1.
aws.s3.access_key	No	The access key of your IAM user. If you use the IAM user-based authentication method to access AWS S3, you must specify this parameter.
aws.s3.secret_key	No	The secret key of your IAM user. If you use the IAM user-based authentication method to access AWS S3, you must specify this parameter.

For information about how to choose an authentication method for accessing AWS S3 and how to configure an access control policy in AWS IAM Console, see Authentication parameters for accessing AWS S3.

S3-compatible storage system

Iceberg catalogs support S3-compatible storage systems from v2.5 onwards.

If you choose an S3-compatible storage system, such as MinIO, as storage for your Iceberg cluster, configure StorageCredentialParams as follows to ensure a successful integration:

"aws.s3.enable_ssl" = "false",
"aws.s3.enable_path_style_access" = "true",
"aws.s3.endpoint" = "<s3_endpoint>",
"aws.s3.access_key" = "<iam_user_access_key>",
"aws.s3.secret_key" = "<iam_user_secret_key>"

The following table describes the parameters you need to configure in StorageCredentialParams.

Parameter	Required	Description
aws.s3.enable_ssl	Yes	Specifies whether to enable SSL connection. Valid values: true and false. Default value: true.
aws.s3.enable_path_style_access	Yes	Specifies whether to enable path-style access. Valid values: true and false. Default value: false. For MinIO, you must set the value to true. Path-style URLs use the following format: https://s3.<region_code>.amazonaws.com/<bucket_name>/<key_name>. For example, if you create a bucket named DOC-EXAMPLE-BUCKET1 in the US West (Oregon) Region, and you want to access the alice.jpg object in that bucket, you can use the following path-style URL: https://s3.us-west-2.amazonaws.com/DOC-EXAMPLE-BUCKET1/alice.jpg.
aws.s3.endpoint	Yes	The endpoint that is used to connect to your S3-compatible storage system instead of AWS S3.
aws.s3.access_key	Yes	The access key of your IAM user.
aws.s3.secret_key	Yes	The secret key of your IAM user.

Microsoft Azure Storage

Iceberg catalogs support Microsoft Azure Storage from v3.0 onwards.

Azure Blob Storage

If you choose Blob Storage as storage for your Iceberg cluster, take one of the following actions:

• To choose the Shared Key authentication method, configure StorageCredentialParams as follows:

  "azure.blob.storage_account" = "<blob_storage_account_name>",
  "azure.blob.shared_key" = "<blob_storage_account_shared_key>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.blob.storage_account	Yes	The username of your Blob Storage account.
  azure.blob.shared_key	Yes	The shared key of your Blob Storage account.

• To choose the SAS Token authentication method, configure StorageCredentialParams as follows:

  "azure.blob.account_name" = "<blob_storage_account_name>",
  "azure.blob.container_name" = "<blob_container_name>",
  "azure.blob.sas_token" = "<blob_storage_account_SAS_token>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.blob.account_name	Yes	The username of your Blob Storage account.
  azure.blob.container_name	Yes	The name of the blob container that stores your data.
  azure.blob.sas_token	Yes	The SAS token that is used to access your Blob Storage account.

Azure Data Lake Storage Gen1

If you choose Data Lake Storage Gen1 as storage for your Iceberg cluster, take one of the following actions:

• To choose the Managed Service Identity authentication method, configure StorageCredentialParams as follows:

  "azure.adls1.use_managed_service_identity" = "true"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.adls1.use_managed_service_identity	Yes	Specifies whether to enable the Managed Service Identity authentication method. Set the value to true.

• To choose the Service Principal authentication method, configure StorageCredentialParams as follows:

  "azure.adls1.oauth2_client_id" = "<application_client_id>",
  "azure.adls1.oauth2_credential" = "<application_client_credential>",
  "azure.adls1.oauth2_endpoint" = "<OAuth_2.0_authorization_endpoint_v2>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.adls1.oauth2_client_id	Yes	The client (application) ID of the service principal.
  azure.adls1.oauth2_credential	Yes	The value of the new client (application) secret created.
  azure.adls1.oauth2_endpoint	Yes	The OAuth 2.0 token endpoint (v1) of the service principal or application.

Azure Data Lake Storage Gen2

If you choose Data Lake Storage Gen2 as storage for your Iceberg cluster, take one of the following actions:

• To choose the Managed Identity authentication method, configure StorageCredentialParams as follows:

  "azure.adls2.oauth2_use_managed_identity" = "true",
  "azure.adls2.oauth2_tenant_id" = "<service_principal_tenant_id>",
  "azure.adls2.oauth2_client_id" = "<service_client_id>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.adls2.oauth2_use_managed_identity	Yes	Specifies whether to enable the Managed Identity authentication method. Set the value to true.
  azure.adls2.oauth2_tenant_id	Yes	The ID of the tenant whose data you want to access.
  azure.adls2.oauth2_client_id	Yes	The client (application) ID of the managed identity.

• To choose the Shared Key authentication method, configure StorageCredentialParams as follows:

  "azure.adls2.storage_account" = "<storage_account_name>",
  "azure.adls2.shared_key" = "<shared_key>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.adls2.storage_account	Yes	The username of your Data Lake Storage Gen2 storage account.
  azure.adls2.shared_key	Yes	The shared key of your Data Lake Storage Gen2 storage account.

• To choose the Service Principal authentication method, configure StorageCredentialParams as follows:

  "azure.adls2.oauth2_client_id" = "<service_client_id>",
  "azure.adls2.oauth2_client_secret" = "<service_principal_client_secret>",
  "azure.adls2.oauth2_client_endpoint" = "<service_principal_client_endpoint>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Required	Description
  azure.adls2.oauth2_client_id	Yes	The client (application) ID of the service principal.
  azure.adls2.oauth2_client_secret	Yes	The value of the new client (application) secret created.
  azure.adls2.oauth2_client_endpoint	Yes	The OAuth 2.0 token endpoint (v1) of the service principal or application.

Google GCS

Iceberg catalogs support Google GCS from v3.0 onwards.

If you choose Google GCS as storage for your Iceberg cluster, take one of the following actions:

• To choose the VM-based authentication method, configure StorageCredentialParams as follows:

  "gcp.gcs.use_compute_engine_service_account" = "true"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Default value	Value example	Description
  gcp.gcs.use_compute_engine_service_account	false	true	Specifies whether to directly use the service account that is bound to your Compute Engine.

• To choose the service account-based authentication method, configure StorageCredentialParams as follows:

  "gcp.gcs.service_account_email" = "<google_service_account_email>",
  "gcp.gcs.service_account_private_key_id" = "<google_service_private_key_id>",
  "gcp.gcs.service_account_private_key" = "<google_service_private_key>"

  The following table describes the parameters you need to configure in StorageCredentialParams.

  Parameter	Default value	Value example	Description
  gcp.gcs.service_account_email	""	" user@hello.iam.gserviceaccount.com "	The email address in the JSON file generated at the creation of the service account.
  gcp.gcs.service_account_private_key_id	""	"61d257bd8479547cb3e04f0b9b6b9ca07af3b7ea"	The private key ID in the JSON file generated at the creation of the service account.
  gcp.gcs.service_account_private_key	""	"-----BEGIN PRIVATE KEY----xxxx-----END PRIVATE KEY-----\n"	The private key in the JSON file generated at the creation of the service account.

• To choose the impersonation-based authentication method, configure StorageCredentialParams as follows:

  • Make a VM instance impersonate a service account:

    "gcp.gcs.use_compute_engine_service_account" = "true",
    "gcp.gcs.impersonation_service_account" = "<assumed_google_service_account_email>"

    The following table describes the parameters you need to configure in StorageCredentialParams.

    Parameter	Default value	Value example	Description
    gcp.gcs.use_compute_engine_service_account	false	true	Specifies whether to directly use the service account that is bound to your Compute Engine.
    gcp.gcs.impersonation_service_account	""	"hello"	The service account that you want to impersonate.

  • Make a service account (temporarily named as meta service account) impersonate another service account (temporarily named as data service account):

    "gcp.gcs.service_account_email" = "<google_service_account_email>",
    "gcp.gcs.service_account_private_key_id" = "<meta_google_service_account_email>",
    "gcp.gcs.service_account_private_key" = "<meta_google_service_account_email>",
    "gcp.gcs.impersonation_service_account" = "<data_google_service_account_email>"

    The following table describes the parameters you need to configure in StorageCredentialParams.

    Parameter	Default value	Value example	Description
    gcp.gcs.service_account_email	""	" user@hello.iam.gserviceaccount.com "	The email address in the JSON file generated at the creation of the meta service account.
    gcp.gcs.service_account_private_key_id	""	"61d257bd8479547cb3e04f0b9b6b9ca07af3b7ea"	The private key ID in the JSON file generated at the creation of the meta service account.
    gcp.gcs.service_account_private_key	""	"-----BEGIN PRIVATE KEY----xxxx-----END PRIVATE KEY-----\n"	The private key in the JSON file generated at the creation of the meta service account.
    gcp.gcs.impersonation_service_account	""	"hello"	The data service account that you want to impersonate.

Examples

The following examples create an Iceberg catalog named iceberg_catalog_hms or iceberg_catalog_glue, depending on the type of metastore you use, to query data from your Iceberg cluster.

HDFS

If you use HDFS as storage, run a command like below:

CREATE EXTERNAL CATALOG iceberg_catalog_hms
PROPERTIES
(
    "type" = "iceberg",
    "hive.metastore.type" = "hive",
    "hive.metastore.uris" = "thrift://xx.xx.xx:9083"
);

AWS S3

If you choose instance profile-based credential

• If you use Hive metastore in your Iceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://xx.xx.xx:9083",
      "aws.s3.use_instance_profile" = "true",
      "aws.s3.region" = "us-west-2"
  );

• If you use AWS Glue in your Amazon EMR Iceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_glue
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "glue",
      "aws.glue.use_instance_profile" = "true",
      "aws.glue.region" = "us-west-2",
      "aws.s3.use_instance_profile" = "true",
      "aws.s3.region" = "us-west-2"
  );

If you choose assumed role-based credential

• If you use Hive metastore in your HIceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://xx.xx.xx:9083",
      "aws.s3.use_instance_profile" = "true",
      "aws.s3.iam_role_arn" = "arn:aws:iam::081976408565:role/test_s3_role",
      "aws.s3.region" = "us-west-2"
  );

• If you use AWS Glue in your Amazon EMR Iceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_glue
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "glue",
      "aws.glue.use_instance_profile" = "true",
      "aws.glue.iam_role_arn" = "arn:aws:iam::081976408565:role/test_glue_role",
      "aws.glue.region" = "us-west-2",
      "aws.s3.use_instance_profile" = "true",
      "aws.s3.iam_role_arn" = "arn:aws:iam::081976408565:role/test_s3_role",
      "aws.s3.region" = "us-west-2"
  );

If you choose IAM user-based credential

• If you use Hive metastore in your Iceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://xx.xx.xx:9083",
      "aws.s3.use_instance_profile" = "false",
      "aws.s3.access_key" = "<iam_user_access_key>",
      "aws.s3.secret_key" = "<iam_user_access_key>",
      "aws.s3.region" = "us-west-2"
  );

• If you use AWS Glue in your Amazon EMR Iceberg cluster, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_glue
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "glue",
      "aws.glue.use_instance_profile" = "false",
      "aws.glue.access_key" = "<iam_user_access_key>",
      "aws.glue.secret_key" = "<iam_user_secret_key>",
      "aws.glue.region" = "us-west-2",
      "aws.s3.use_instance_profile" = "false",
      "aws.s3.access_key" = "<iam_user_access_key>",
      "aws.s3.secret_key" = "<iam_user_secret_key>",
      "aws.s3.region" = "us-west-2"
  );

S3-compatible storage system

Use MinIO as an example. Run a command like below:

CREATE EXTERNAL CATALOG iceberg_catalog_hms
PROPERTIES
(
    "type" = "iceberg",
    "iceberg.catalog.type" = "hive",
    "hive.metastore.uris" = "thrift://34.132.15.127:9083",
    "aws.s3.enable_ssl" = "true",
    "aws.s3.enable_path_style_access" = "true",
    "aws.s3.endpoint" = "<s3_endpoint>",
    "aws.s3.access_key" = "<iam_user_access_key>",
    "aws.s3.secret_key" = "<iam_user_secret_key>"
);

Microsoft Azure Storage

Azure Blob Storage

• If you choose the Shared Key authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.blob.storage_account" = "<blob_storage_account_name>",
      "azure.blob.shared_key" = "<blob_storage_account_shared_key>"
  );

• If you choose the SAS Token authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.blob.account_name" = "<blob_storage_account_name>",
      "azure.blob.container_name" = "<blob_container_name>",
      "azure.blob.sas_token" = "<blob_storage_account_SAS_token>"
  );

Azure Data Lake Storage Gen1

• If you choose the Managed Service Identity authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.adls1.use_managed_service_identity" = "true"    
  );

• If you choose the Service Principal authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.adls1.oauth2_client_id" = "<application_client_id>",
      "azure.adls1.oauth2_credential" = "<application_client_credential>",
      "azure.adls1.oauth2_endpoint" = "<OAuth_2.0_authorization_endpoint_v2>"
  );

Azure Data Lake Storage Gen2

• If you choose the Managed Identity authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.adls2.oauth2_use_managed_identity" = "true",
      "azure.adls2.oauth2_tenant_id" = "<service_principal_tenant_id>",
      "azure.adls2.oauth2_client_id" = "<service_client_id>"
  );

• If you choose the Shared Key authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.adls2.storage_account" = "<storage_account_name>",
      "azure.adls2.shared_key" = "<shared_key>"     
  );

• If you choose the Service Principal authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "azure.adls2.oauth2_client_id" = "<service_client_id>",
      "azure.adls2.oauth2_client_secret" = "<service_principal_client_secret>",
      "azure.adls2.oauth2_client_endpoint" = "<service_principal_client_endpoint>"
  );

Google GCS

• If you choose the VM-based authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "gcp.gcs.use_compute_engine_service_account" = "true"    
  );

• If you choose the service account-based authentication method, run a command like below:

  CREATE EXTERNAL CATALOG iceberg_catalog_hms
  PROPERTIES
  (
      "type" = "iceberg",
      "iceberg.catalog.type" = "hive",
      "hive.metastore.uris" = "thrift://34.132.15.127:9083",
      "gcp.gcs.service_account_email" = "<google_service_account_email>",
      "gcp.gcs.service_account_private_key_id" = "<google_service_private_key_id>",
      "gcp.gcs.service_account_private_key" = "<google_service_private_key>"    
  );

• If you choose the impersonation-based authentication method:

  • If you make a VM instance impersonate a service account, run a command like below:

    CREATE EXTERNAL CATALOG iceberg_catalog_hms
    PROPERTIES
    (
        "type" = "iceberg",
        "iceberg.catalog.type" = "hive",
        "hive.metastore.uris" = "thrift://34.132.15.127:9083",
        "gcp.gcs.use_compute_engine_service_account" = "true",
        "gcp.gcs.impersonation_service_account" = "<assumed_google_service_account_email>"    
    );

  • If you make a service account impersonate another service account, run a command like below:

    CREATE EXTERNAL CATALOG iceberg_catalog_hms
    PROPERTIES
    (
        "type" = "iceberg",
        "iceberg.catalog.type" = "hive",
        "hive.metastore.uris" = "thrift://34.132.15.127:9083",
        "gcp.gcs.service_account_email" = "<google_service_account_email>",
        "gcp.gcs.service_account_private_key_id" = "<meta_google_service_account_email>",
        "gcp.gcs.service_account_private_key" = "<meta_google_service_account_email>",
        "gcp.gcs.impersonation_service_account" = "<data_google_service_account_email>"
    );

View Iceberg catalogs

You can use SHOW CATALOGS to query all catalogs in the current StarRocks cluster:

SHOW CATALOGS;

You can also use SHOW CREATE CATALOG to query the creation statement of an external catalog. The following example queries the creation statement of an Iceberg catalog named iceberg_catalog_glue:

SHOW CREATE CATALOG iceberg_catalog_glue;

Switch to an Iceberg Catalog and a database in it

You can use one of the following methods to switch to an Iceberg catalog and a database in it:

• Use SET CATALOG to specify an Iceberg catalog in the current session, and then use USE to specify an active database:

  -- Switch to a specified catalog in the current session:
  SET CATALOG <catalog_name>
  -- Specify the active database in the current session:
  USE <db_name>

• Directly use USE to switch to an Iceberg catalog and a database in it:

  USE <catalog_name>.<db_name>

Drop an Iceberg catalog

You can use DROP CATALOG to drop an external catalog.

The following example drops an Iceberg catalog named iceberg_catalog_glue:

DROP Catalog iceberg_catalog_glue;

View the schema of an Iceberg table

You can use one of the following syntaxes to view the schema of an Iceberg table:

• View schema

  DESC[RIBE] <catalog_name>.<database_name>.<table_name>

• View schema and location from the CREATE statement

  SHOW CREATE TABLE <catalog_name>.<database_name>.<table_name>

Query an Iceberg table

1. Use SHOW DATABASES to view the databases in your Iceberg cluster:

   SHOW DATABASES FROM <catalog_name>

2. Switch to an Iceberg catalog and a database in it.

3. Use SELECT to query the destination table in the specified database:

   SELECT count(*) FROM <table_name> LIMIT 10

Create an Iceberg database

Similar to the internal catalog of StarRocks, if you have the CREATE DATABASE privilege on an Iceberg catalog, you can use the CREATE DATABASE statement to create databases in that Iceberg catalog. This feature is supported from v3.1 onwards.

NOTE

You can grant and revoke privileges by using GRANT and REVOKE.

Switch to an Iceberg catalog, and then use the following statement to create an Iceberg database in that catalog:

CREATE DATABASE <database_name>
[properties ("location" = "<prefix>://<path_to_database>/<database_name.db>/")]

You can use the location parameter to specify the file path in which you want to create the database. Both HDFS and cloud storage are supported. If you do not specify the location parameter, StarRocks creates the database in the default file path of the Iceberg catalog.

The prefix varies based on the storage system you use:

Storage system	Prefix value
HDFS	hdfs
Google GCS	gs
Azure Blob Storage	If your storage account allows access over HTTP, the prefix is wasb. If your storage account allows access over HTTPS, the prefix is wasbs.
Azure Data Lake Storage Gen1	adl
Azure Data Lake Storage Gen2	If your storage account allows access over HTTP, theprefix is abfs. If your storage account allows access over HTTPS, the prefix is abfss.
AWS S3 or other S3-compatible storage (for example, MinIO)	s3

Drop an Iceberg database

Similar to the internal databases of StarRocks, if you have the DROP privilege on an Iceberg database, you can use the DROP DATABASE statement to drop that Iceberg database. This feature is supported from v3.1 onwards. You can only drop empty databases.

NOTE

You can grant and revoke privileges by using GRANT and REVOKE.

When you drop an Iceberg database, the database's file path on your HDFS cluster or cloud storage will not be dropped along with the database.

Switch to an Iceberg catalog, and then use the following statement to drop an Iceberg database in that catalog:

DROP DATABASE <database_name>;

Create an Iceberg table

Similar to the internal databases of StarRocks, if you have the CREATE TABLE privilege on an Iceberg database, you can use the CREATE TABLE or CREATE TABLE AS SELECT (CTAS) statement to create a table in that Iceberg database. This feature is supported from v3.1 onwards.

NOTE

You can grant and revoke privileges by using GRANT and REVOKE.

Switch to an Iceberg catalog and a database in it, and then use the following syntax to create an Iceberg table in that database.

Syntax

CREATE TABLE [IF NOT EXISTS] [database.]table_name
(column_definition1[, column_definition2, ...
partition_column_definition1,partition_column_definition2...])
[partition_desc]
[PROPERTIES ("key" = "value", ...)]
[AS SELECT query]

Parameters

column_definition

The syntax of column_definition is as follows:

col_name col_type [COMMENT 'comment']

The following table describes the parameters.

Parameter	Description
col_name	The name of the column.
col_type	The data type of the column. The following data types are supported: TINYINT, SMALLINT, INT, BIGINT, FLOAT, DOUBLE, DECIMAL, DATE, DATETIME, CHAR, VARCHAR[(length)], ARRAY, MAP, and STRUCT. The LARGEINT, HLL, and BITMAP data types are not supported.

NOTICE

All non-partition columns must use NULL as the default value. This means that you must specify DEFAULT "NULL" for each of the non-partition columns in the table creation statement. Additionally, partition columns must be defined following non-partition columns and cannot use NULL as the default value.

partition_desc

The syntax of partition_desc is as follows:

PARTITION BY (par_col1[, par_col2...])

Currently StarRocks only supports identity transforms, which means that StarRocks creates a partition for each unique partition value.

NOTICE

Partition columns must be defined following non-partition columns. Partition columns support all data types excluding FLOAT, DOUBLE, DECIMAL, and DATETIME and cannot use NULL as the default value.

properties

You can specify the table attributes in the "key" = "value" format in properties. See Iceberg table attributes.

The following table describes a few key properties.

Property	Description
location	The file path in which you want to create the Iceberg table. When you use HMS as metastore, you do not need to specify the location parameter, because StarRocks will create the table in the default file path of the current Iceberg catalog. When you use AWS Glue as metastore: If you have specified the location parameter for the database in which you want to create the table, you do not need to specify the location parameter for the table. As such, the table defaults to the file path of the database to which it belongs. If you have not specified the location for the database in which you want to create the table, you must specify the location parameter for the table.
file_format	The file format of the Iceberg table. Only the Parquet format is supported. Default value: parquet.
compression_codec	The compression algorithm used for the Iceberg table. The supported compression algorithms are SNAPPY, GZIP, ZSTD, and LZ4. Default value: gzip.

Examples

1. Create a non-partitioned table named unpartition_tbl. The table consists of two columns, id and score, as shown below:

   CREATE TABLE unpartition_tbl
   (
       id int,
       score double
   );

2. Create a partitioned table named partition_tbl_1. The table consists of three columns, action, id, and dt, of which id and dt are defined as partition columns, as shown below:

   CREATE TABLE partition_tbl_1
   (
       action varchar(20),
       id int,
       dt date
   )
   PARTITION BY (id,dt);

3. Query an existing table named partition_tbl_1, and create a partitioned table named partition_tbl_2 based on the query result of partition_tbl_1. For partition_tbl_2, id and dt are defined as partition columns, as shown below:

   CREATE TABLE partition_tbl_2
   PARTITION BY (id, dt)
   AS SELECT * from employee;

Sink data to an Iceberg table

Similar to the internal tables of StarRocks, if you have the INSERT privilege on an Iceberg table, you can use the INSERT statement to sink the data of a StarRocks table to that Iceberg table (currently only Parquet-formatted Iceberg tables are supported). This feature is supported from v3.1 onwards.

NOTE

You can grant and revoke privileges by using GRANT and REVOKE.

Switch to an Iceberg catalog and a database in it, and then use the following syntax to sink the data of StarRocks table to a Parquet-formatted Iceberg table in that database.

Syntax

INSERT {INTO | OVERWRITE} <table_name>
[ (column_name [, ...]) ]
{ VALUES ( { expression | DEFAULT } [, ...] ) [, ...] | query }

-- If you want to sink data to specified partitions, use the following syntax:
INSERT {INTO | OVERWRITE} <table_name>
PARTITION (par_col1=<value> [, par_col2=<value>...])
{ VALUES ( { expression | DEFAULT } [, ...] ) [, ...] | query }

NOTICE

Partition columns do not allow NULL values. Therefore, you must make sure that no empty values are loaded into the partition columns of the Iceberg table.

Parameters

Parameter	Description
INTO	To append the data of the StarRocks table to the Iceberg table.
OVERWRITE	To overwrite the existing data of the Iceberg table with the data of the StarRocks table.
column_name	The name of the destination column to which you want to load data. You can specify one or more columns. If you specify multiple columns, separate them with commas (,). You can only specify columns that actually exist in the Iceberg table, and the destination columns that you specify must include the partition columns of the Iceberg table. The destination columns you specify are mapped one on one in sequence to the columns of the StarRocks table, regardless of what the destination column names are. If no destination columns are specified, the data is loaded into all columns of the Iceberg table. If a non-partition column of the StarRocks table cannot be mapped to any column of the Iceberg table, StarRocks writes the default value NULL to the Iceberg table column. If the INSERT statement contains a query statement whose returned column types differ from the data types of the destination columns, StarRocks performs an implicit conversion on the mismatched columns. If the conversion fails, a syntax parsing error will be returned.
expression	Expression that assigns values to the destination column.
DEFAULT	Assigns a default value to the destination column.
query	Query statement whose result will be loaded into the Iceberg table. It can be any SQL statement supported by StarRocks.
PARTITION	The partitions into which you want to load data. You must specify all partition columns of the Iceberg table in this property. The partition columns that you specify in this property can be in a different sequence than the partition columns that you have defined in the table creation statement. If you specify this property, you cannot specify the column_name property.

Examples

1. Insert three data rows into the partition_tbl_1 table:

   INSERT INTO partition_tbl_1
   VALUES
       ("buy", 1, "2023-09-01"),
       ("sell", 2, "2023-09-02"),
       ("buy", 3, "2023-09-03");

2. Insert the result of a SELECT query, which contains simple computations, into the partition_tbl_1 table:

   INSERT INTO partition_tbl_1 (id, action, dt) SELECT 1+1, 'buy', '2023-09-03';

3. Insert the result of a SELECT query, which reads data from the partition_tbl_1 table, into the same table:

   INSERT INTO partition_tbl_1 SELECT 'buy', 1, date_add(dt, INTERVAL 2 DAY)
   FROM partition_tbl_1
   WHERE id=1;

4. Insert the result of a SELECT query into the partitions that meet two conditions, dt='2023-09-01' and id=1, of the partition_tbl_2 table:

   INSERT INTO partition_tbl_2 SELECT 'order', 1, '2023-09-01';

   Or

   INSERT INTO partition_tbl_2 partition(dt='2023-09-01',id=1) SELECT 'order';

5. Overwrite all action column values in the partitions that meet two conditions, dt='2023-09-01' and id=1, of the partition_tbl_1 table with close:

   INSERT OVERWRITE partition_tbl_1 SELECT 'close', 1, '2023-09-01';

   Or

   INSERT OVERWRITE partition_tbl_1 partition(dt='2023-09-01',id=1) SELECT 'close';

Drop an Iceberg table

Similar to the internal tables of StarRocks, if you have the DROP privilege on an Iceberg table, you can use the DROP TABLE statement to drop that Iceberg table. This feature is supported from v3.1 onwards.

NOTE

You can grant and revoke privileges by using GRANT and REVOKE.

When you drop an Iceberg table, the table's file path and data on your HDFS cluster or cloud storage will not be dropped along with the table.

When you forcibly drop an Iceberg table (namely, with the FORCE keyword specified in the DROP TABLE statement), the table's data on your HDFS cluster or cloud storage will be dropped along with the table, but the table's file path is retained.

Switch to an Iceberg catalog and a database in it, and then use the following statement to drop an Iceberg table in that database.

DROP TABLE <table_name> [FORCE];

Configure metadata caching

The metadata files of your Iceberg cluster may be stored in remote storage such as AWS S3 or HDFS. By default, StarRocks caches Iceberg metadata in memory. To accelerate queries, StarRocks adopts a two-level metadata caching mechanism, with which it can cache metadata both in memory and on disk. For each initial query, StarRocks caches their computation results. If any subsequent query that is semantically equivalent to a previous query is issued, StarRocks first attempts to retrieve the requested metadata from its caches, and it retrieves the metadata from the remote storage only when the metadata cannot be hit in its caches.

StarRocks uses the Least Recently Used (LRU) algorithm to cache and evict data. The basic rules are as follows:

• StarRocks first attempts to retrieve the requested metadata from the memory. If the metadata cannot be hit in the memory, StarRock attempts to retrieve the metadata from the disks. The metadata that StarRocks has retrieved from the disks will be loaded into the memory. If the metadata cannot be hit in the disks either, StarRock retrieves the metadata from the remote storage and caches the retrieved metadata in the memory.
• StarRocks writes the metadata evicted out of the memory into the disks, but it directly discards the metadata evicted out of the disks.

The following table describes the FE configuration items that you can use to configure your Iceberg metadata caching mechanism.

Configuration item	Unit	Default value	Description
enable_iceberg_metadata_disk_cache	N/A	false	Specifies whether to enable the disk cache.
iceberg_metadata_cache_disk_path	N/A	StarRocksFE.STARROCKS_HOME_DIR + "/caches/iceberg"	The save path of cached metadata files on disk.
iceberg_metadata_disk_cache_capacity	Bytes	2147483648, equivalent to 2 GB	The maximum size of cached metadata allowed on disk.
iceberg_metadata_memory_cache_capacity	Bytes	536870912, equivalent to 512 MB	The maximum size of cached metadata allowed in memory.
iceberg_metadata_memory_cache_expiration_seconds	Seconds	86500	The amount of time after which a cache entry in memory expires counting from its last access.
iceberg_metadata_disk_cache_expiration_seconds	Seconds	604800, equivalent to one week	The amount of time after which a cache entry on disk expires counting from its last access.
iceberg_metadata_cache_max_entry_size	Bytes	8388608, equivalent to 8 MB	The maximum size of a file that can be cached. Files whose size exceeds the value of this parameter cannot be cached. If a query requests these files, StarRocks retrieves them from the remote storage.