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Version: Latest-3.3

Hive catalog

A Hive catalog is a kind of external catalog that is supported by StarRocks from v2.4 onwards. Within Hive catalogs, you can:

  • Directly query data stored in Hive 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 Hive and load the data into StarRocks.
  • Perform operations on StarRocks to create or drop Hive databases and tables, or sink data from StarRocks tables to Parquet-formatted (supported from v3.2 onwards) and ORC- or Textfile-formatted (supported from v3.3 onwards) Hive tables by using INSERT INTO.

To ensure successful SQL workloads on your Hive cluster, your StarRocks cluster must be able to access the storage system and metastore of your Hive cluster. StarRocks supports the following storage systems and metastores:

  • 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 or AWS Glue

    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.

Usage notes​

  • StarRocks supports queries against Hive tables in Parquet, ORC, Textfile, Avro, RCFile, and SequenceFile file formats:

    • Parquet files support the following compression formats: SNAPPY, LZ4, ZSTD, GZIP, and NO_COMPRESSION. From v3.1.5 onwards, Parquet files also support the LZO compression format.
    • ORC files support the following compression formats: ZLIB, SNAPPY, LZO, LZ4, ZSTD, and NO_COMPRESSION.
    • Textfile files support the LZO compression format from v3.1.5 onwards.

  • The data types of Hive that StarRocks does not support are INTERVAL, BINARY, and UNION. Additionally, StarRocks does not support the MAP and STRUCT data types for Textfile-formatted Hive tables.

  • StarRocks supports sinking data to Parquet-formatted (supported from v3.2 onwards) and ORC- or Textfile-formatted (supported from v3.3 onwards) Hive tables:

    • Parquet and ORC files support the following compression formats: NO_COMPRESSION, SNAPPY, LZ4, ZSTD, and GZIP.
    • Textfile files support the NO_COMPRESSION compression format.

    You can use the session variable connector_sink_compression_codec to specify the compression algorithm used for sinking data to Hive tables.

Integration preparations​

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

AWS IAM​

If your Hive 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 or CN 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 or the cn/conf/hadoop_env.sh file of each CN. After you set the username in these files, restart each FE and each BE or CN to make the parameter settings take effect. You can set only one username for each StarRocks cluster.

  • When you query Hive data, the FEs and BEs or CNs 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 or the $CN_HOME/conf path of each CN.
    • 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 or the $CN_HOME/conf path of each CN.
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 or CN 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 or the $CN_HOME/conf/cn.conf file of each CN. 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 a Hive catalog​

Syntax​

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

Parameters​

catalog_name​

The name of the Hive 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 Hive catalog. This parameter is optional.

type​

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

GeneralParams​

A set of general parameters.

The following table describes the parameters you can configure in GeneralParams.

ParameterRequiredDescription
enable_recursive_listingNoSpecifies whether StarRocks reads data from a table and its partitions and from the subdirectories within the physical locations of the table and its partitions. Valid values: true and false. Default value: true. The value true specifies to recursively list subdirectories, and the value false specifies to ignore subdirectories.

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:

"hive.metastore.type" = "hive",
"hive.metastore.uris" = "<hive_metastore_uri>"
note

Before querying Hive 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.

ParameterRequiredDescription
hive.metastore.typeYesThe type of metastore that you use for your Hive cluster. Set the value to hive.
hive.metastore.urisYesThe 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:

    "hive.metastore.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:

    "hive.metastore.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:

    "hive.metastore.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.

ParameterRequiredDescription
hive.metastore.typeYesThe type of metastore that you use for your Hive cluster. Set the value to glue.
aws.glue.use_instance_profileYesSpecifies whether to enable the instance profile-based authentication method and the assumed role-based authentication. Valid values: true and false. Default value: false.
aws.glue.iam_role_arnNoThe 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.regionYesThe region in which your AWS Glue Data Catalog resides. Example: us-west-1.
aws.glue.access_keyNoThe 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_keyNoThe 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.

StorageCredentialParams​

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

If you use HDFS as storage, you do not need to configure StorageCredentialParams.

If you use AWS S3, other S3-compatible storage system, Microsoft Azure Storage, or Google GCS as storage, you must configure StorageCredentialParams.

AWS S3​

If you choose AWS S3 as storage for your Hive 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.

ParameterRequiredDescription
aws.s3.use_instance_profileYesSpecifies 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_arnNoThe 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.regionYesThe region in which your AWS S3 bucket resides. Example: us-west-1.
aws.s3.access_keyNoThe 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_keyNoThe 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​

Hive 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 Hive 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.

ParameterRequiredDescription
aws.s3.enable_sslYesSpecifies whether to enable SSL connection.
Valid values: true and false. Default value: true.
aws.s3.enable_path_style_accessYesSpecifies 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.endpointYesThe endpoint that is used to connect to your S3-compatible storage system instead of AWS S3.
aws.s3.access_keyYesThe access key of your IAM user.
aws.s3.secret_keyYesThe secret key of your IAM user.
Microsoft Azure Storage​

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

Azure Blob Storage​

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

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

    "azure.blob.storage_account" = "<storage_account_name>",
    "azure.blob.shared_key" = "<storage_account_shared_key>"

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

    ParameterRequiredDescription
    azure.blob.storage_accountYesThe username of your Blob Storage account.
    azure.blob.shared_keyYesThe shared key of your Blob Storage account.

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

    "azure.blob.storage_account" = "<storage_account_name>",
    "azure.blob.container" = "<container_name>",
    "azure.blob.sas_token" = "<storage_account_SAS_token>"

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

    ParameterRequiredDescription
    azure.blob.storage_accountYesThe username of your Blob Storage account.
    azure.blob.containerYesThe name of the blob container that stores your data.
    azure.blob.sas_tokenYesThe SAS token that is used to access your Blob Storage account.
Azure Data Lake Storage Gen2​

If you choose Data Lake Storage Gen2 as storage for your Hive 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.

    ParameterRequiredDescription
    azure.adls2.oauth2_use_managed_identityYesSpecifies whether to enable the Managed Identity authentication method. Set the value to true.
    azure.adls2.oauth2_tenant_idYesThe ID of the tenant whose data you want to access.
    azure.adls2.oauth2_client_idYesThe 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" = "<storage_account_shared_key>"

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

    ParameterRequiredDescription
    azure.adls2.storage_accountYesThe username of your Data Lake Storage Gen2 storage account.
    azure.adls2.shared_keyYesThe 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.

    ParameterRequiredDescription
    azure.adls2.oauth2_client_idYesThe client (application) ID of the service principal.
    azure.adls2.oauth2_client_secretYesThe value of the new client (application) secret created.
    azure.adls2.oauth2_client_endpointYesThe OAuth 2.0 token endpoint (v1) of the service principal or application.
Azure Data Lake Storage Gen1​

If you choose Data Lake Storage Gen1 as storage for your Hive 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.

    ParameterRequiredDescription
    azure.adls1.use_managed_service_identityYesSpecifies 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.

    ParameterRequiredDescription
    azure.adls1.oauth2_client_idYesThe client (application) ID of the service principal.
    azure.adls1.oauth2_credentialYesThe value of the new client (application) secret created.
    azure.adls1.oauth2_endpointYesThe OAuth 2.0 token endpoint (v1) of the service principal or application.
Google GCS​

Hive catalogs support Google GCS from v3.0 onwards.

If you choose Google GCS as storage for your Hive 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.

    ParameterDefault valueValue exampleDescription
    gcp.gcs.use_compute_engine_service_accountfalsetrueSpecifies 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.

    ParameterDefault valueValue exampleDescription
    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.

      ParameterDefault valueValue exampleDescription
      gcp.gcs.use_compute_engine_service_accountfalsetrueSpecifies 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.

      ParameterDefault valueValue exampleDescription
      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.

MetadataUpdateParams​

A set of parameters about how StarRocks updates the cached metadata of Hive. This parameter set is optional.

StarRocks implements the automatic asynchronous update policy by default.

In most cases, you can ignore MetadataUpdateParams and do not need to tune the policy parameters in it, because the default values of these parameters already provide you with an out-of-the-box performance.

However, if the frequency of data updates in Hive is high, you can tune these parameters to further optimize the performance of automatic asynchronous updates.

note

In most cases, if your Hive data is updated at a granularity of 1 hour or less, the data update frequency is considered high.

ParameterRequiredDescription
enable_metastore_cacheNoSpecifies whether StarRocks caches the metadata of Hive tables. Valid values: true and false. Default value: true. The value true enables the cache, and the value false disables the cache.
enable_remote_file_cacheNoSpecifies whether StarRocks caches the metadata of the underlying data files of Hive tables or partitions. Valid values: true and false. Default value: true. The value true enables the cache, and the value false disables the cache.
metastore_cache_refresh_interval_secNoThe time interval at which StarRocks asynchronously updates the metadata of Hive tables or partitions cached in itself. Unit: seconds. Default value: 60, which is one minute. Since v3.3.0, the default value of this property is changed from 7200 to 60.
remote_file_cache_refresh_interval_secNoThe time interval at which StarRocks asynchronously updates the metadata of the underlying data files of Hive tables or partitions cached in itself. Unit: seconds. Default value: 60.
metastore_cache_ttl_secNoThe time interval at which StarRocks automatically discards the metadata of Hive tables or partitions cached in itself. Unit: seconds. Default value: 86400, which is 24 hours.
remote_file_cache_ttl_secNoThe time interval at which StarRocks automatically discards the metadata of the underlying data files of Hive tables or partitions cached in itself. Unit: seconds. Default value: 129600, which is 36 hours.
enable_cache_list_namesNoSpecifies whether StarRocks caches Hive partition names. Valid values: true and false. Default value: true. The value true enables the cache, and the value false disables the cache.

Examples​

The following examples create a Hive catalog named hive_catalog_hms or hive_catalog_glue, depending on the type of metastore you use, to query data from your Hive cluster.

HDFS​

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

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

AWS S3​

Instance profile-based authentication​

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

    CREATE EXTERNAL CATALOG hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.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 Hive cluster, run a command like below:

    CREATE EXTERNAL CATALOG hive_catalog_glue
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.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"
    );
Assumed role-based authentication​

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

    CREATE EXTERNAL CATALOG hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.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 Hive cluster, run a command like below:

    CREATE EXTERNAL CATALOG hive_catalog_glue
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.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"
    );
IAM user-based authentication​

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

    CREATE EXTERNAL CATALOG hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.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 Hive cluster, run a command like below:

    CREATE EXTERNAL CATALOG hive_catalog_glue
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.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 hive_catalog_hms
PROPERTIES
(
    "type" = "hive",
    "hive.metastore.type" = "hive",
    "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx:9083",
        "azure.blob.storage_account" = "<blob_storage_account_name>",
        "azure.blob.container" = "<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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx:9083",
        "azure.adls1.use_managed_service_identity" = "true"    
    );

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

    CREATE EXTERNAL CATALOG hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
    PROPERTIES
    (
        "type" = "hive",
        "hive.metastore.type" = "hive",
        "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
      PROPERTIES
      (
          "type" = "hive",
          "hive.metastore.type" = "hive",
          "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 hive_catalog_hms
      PROPERTIES
      (
          "type" = "hive",
          "hive.metastore.type" = "hive",
          "hive.metastore.uris" = "thrift://xx.xx.xx.xx: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 Hive 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 a Hive catalog named hive_catalog_glue:

SHOW CREATE CATALOG hive_catalog_glue;

Switch to a Hive Catalog and a database in it​

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

  • Use SET CATALOG to specify a Hive 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 a Hive catalog and a database in it:

    USE <catalog_name>.<db_name>

Drop a Hive catalog​

You can use DROP CATALOG to drop an external catalog.

The following example drops a Hive catalog named hive_catalog_glue:

DROP Catalog hive_catalog_glue;

View the schema of a Hive table​

You can use one of the following syntaxes to view the schema of a Hive 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 a Hive table​

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

    SHOW DATABASES FROM <catalog_name>

  2. Switch to a Hive 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

Load data from Hive​

Suppose you have an OLAP table named olap_tbl, you can transform and load data like below:

INSERT INTO default_catalog.olap_db.olap_tbl SELECT * FROM hive_table

Grant privileges on Hive tables and views​

You can use the GRANT statement to grant the privileges on all tables and views within a Hive catalog to a specific role. The command syntax is as follows:

GRANT SELECT ON ALL TABLES IN ALL DATABASES TO ROLE <role_name>

For example, use the following commands to create a role named hive_role_table, switch to the Hive catalog hive_catalog, and then grant the role hive_role_table the privilege to query all tables and views within the Hive catalog hive_catalog:

-- Create a role named hive_role_table.
CREATE ROLE hive_role_table;

-- Switch to the Hive catalog hive_catalog.
SET CATALOG hive_catalog;

-- Grant the role hive_role_table the privilege to query all tables and views within the Hive catalog hive_catalog.
GRANT SELECT ON ALL TABLES IN ALL DATABASES TO ROLE hive_role_table;

Create a Hive database​

Similar to the internal catalog of StarRocks, if you have the CREATE DATABASE privilege on a Hive catalog, you can use the CREATE DATABASE statement to create a database in that Hive catalog. This feature is supported from v3.2 onwards.

note

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

note

Switch to a Hive catalog, and then use the following statement to create a Hive database in that catalog:

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

The location parameter specifies the file path in which you want to create the database, which can be in either HDFS or cloud storage.

  • When you use Hive metastore as the metastore of your Hive cluster, the location parameter defaults to <warehouse_location>/<database_name.db>, which is supported by Hive metastore if you do not specify that parameter at database creation.
  • When you use AWS Glue as the metastore of your Hive cluster, the location parameter does not have a default value, and therefore you must specify that parameter at database creation.

The prefix varies based on the storage system you use:

Storage systemPrefix value
HDFShdfs
Google GCSgs
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 Gen1adl
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 a Hive database​

Similar to the internal databases of StarRocks, if you have the DROP privilege on a Hive database, you can use the DROP DATABASE statement to drop that Hive database. This feature is supported from v3.2 onwards. You can only drop empty databases.

note

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

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

Switch to a Hive catalog, and then use the following statement to drop a Hive database in that catalog:

DROP DATABASE <database_name>

Create a Hive table​

Similar to the internal databases of StarRocks, if you have the CREATE TABLE privilege on a Hive database, you can use the CREATE TABLE, [CREATE TABLE AS SELECT ../../sql-reference/sql-statements/table_bucket_part_index/CREATE_TABLE_AS_SELECT.mdELECT.md), or [CREATE TABL../../sql-reference/sql-statements/table_bucket_part_index/CREATE_TABLE_LIKE.md_LIKE.md) statement to create a managed table in that Hive database.

This feature is supported since v3.2 in which version StarRocks only supports creating Parquet-formatted Hive tables. From v3.3 onwards, StarRocks also supports creating ORC- and Textfile-formatted Hive tables.

note
  • You can grant and revoke privileges by using GRANT and REVOKE.
  • Hive catalogs support CREATE TABLE LIKE from v3.2.4 onwards.

Switch to a Hive catalog and a database in it, and then use the following syntax to create a Hive managed 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]
[LIKE [database.]<source_table_name>]

Parameters​

column_definition​

The syntax of column_definition is as follows:

col_name col_type [COMMENT 'comment']

The following table describes the parameters.

ParameterDescription
col_nameThe name of the column.
col_typeThe 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. Additionally, the sequence of the partition columns declared in partition_desc must be consistent with the sequence of the columns defined in column_definition.

PROPERTIES​

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

The following table describes a few key properties.

PropertyDescription
locationThe file path in which you want to create the managed 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 Hive catalog. When you use AWS Glue as metadata service:
  • 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_formatThe file format of the managed table. Supported file formats are Parquet, ORC, and Textfile. ORC and Textfile formats are supported from v3.3 onwards. Valid values: parquet, orc, and textfile. Default value: parquet.
compression_codecThe compression algorithm used for the managed table. This property is deprecated in v3.2.3, since which version the compression algorithm used for sinking data to Hive tables is uniformly controlled by the session variable connector_sink_compression_codec.

Examples​

The following DDLs use the default file format Parquet as an example.

  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 (k1, k2)
    AS SELECT * from partition_tbl_1;

Sink data to a Hive table​

Similar to the internal tables of StarRocks, if you have the INSERT privilege on a Hive table (which can be a managed table or an external table), you can use the INSERT statement to sink the data of a StarRocks table to that Hive table.

This feature is supported since v3.2 in which version data can be sunk only to Parquet-formatted Hive tables. From v3.3 onwards, StarRocks also supports sinking data to ORC- and Textfile-formatted Hive tables.

Note that sinking data to external tables is disabled by default. To sink data to external tables, you must set the system variable ENABLE_WRITE_HIVE_EXTERNAL_TABLE to true.

note

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

Parameters​

ParameterDescription
INTOTo append the data of the StarRocks table to the Hive table.
OVERWRITETo overwrite the existing data of the Hive table with the data of the StarRocks table.
column_nameThe 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 Hive table, and the destination columns that you specify must include the partition columns of the Hive 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 Hive table. If a non-partition column of the StarRocks table cannot be mapped to any column of the Hive table, StarRocks writes the default value NULL to the Hive 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.
expressionExpression that assigns values to the destination column.
DEFAULTAssigns a default value to the destination column.
queryQuery statement whose result will be loaded into the Hive table. It can be any SQL statement supported by StarRocks.
PARTITIONThe partitions into which you want to load data. You must specify all partition columns of the Hive 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​

The following DMLs use the default file format Parquet as an example.

  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 a Hive table​

Similar to the internal tables of StarRocks, if you have the DROP privilege on a Hive table, you can use the DROP TABLE statement to drop that Hive table. This feature is supported from v3.1 onwards. Note that currently StarRocks supports dropping only managed tables of Hive.

note

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

When you drop a Hive table, you must specify the FORCE keyword in the DROP TABLE statement. After the operation is complete, the table's file path is retained, but the table's data on your HDFS cluster or cloud storage is all dropped along with the table. Exercise caution when you perform this operation to drop a Hive table.

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

DROP TABLE <table_name> FORCE

Manually or automatically update metadata cache​

Manual update​

By default, StarRocks caches the metadata of Hive and automatically updates the metadata in asynchronous mode to deliver better performance. Additionally, after some schema changes or table updates are made on a Hive table, you can also use REFRESH EXTERNAL TABLE to manually update its metadata, thereby ensuring that StarRocks can obtain up-to-date metadata at its earliest opportunity and generate appropriate execution plans:

REFRESH EXTERNAL TABLE <table_name> [PARTITION ('partition_name', ...)]

You need to manually update metadata in the following situations:

  • A data file in an existing partition is changed, for example, by running the INSERT OVERWRITE ... PARTITION ... command.

  • Schema changes are made on a Hive table.

  • An existing Hive table is deleted by using the DROP statement, and a new Hive table with the same name as the deleted Hive table is created.

  • You have specified "enable_cache_list_names" = "true" in PROPERTIES at the creation of your Hive catalog, and you want to query new partitions that you just created on your Hive cluster.

    note

    From v2.5.5 onwards, StarRocks provides the periodic Hive metadata cache refresh feature. For more information, see the below "Periodically refresh metadata cache" section of this topic. After you enable this feature, StarRocks refreshes your Hive metadata cache every 10 minutes by default. Therefore, manual updates are not needed in most cases. You need to perform a manual update only when you want to query new partitions immediately after the new partitions are created on your Hive cluster.

Note that the REFRESH EXTERNAL TABLE refreshes only the tables and partitions cached in your FEs.

Periodically refresh metadata cache​

From v2.5.5 onwards, StarRocks can periodically refresh the cached metadata of the frequently accessed Hive catalogs to perceive data changes. You can configure the Hive metadata cache refresh through the following FE parameters:

Configuration itemDefaultDescription
enable_background_refresh_connector_metadatatrue in v3.0
false in v2.5		Whether to enable the periodic Hive metadata cache refresh. After it is enabled, StarRocks polls the metastore (Hive Metastore or AWS Glue) of your Hive cluster, and refreshes the cached metadata of the frequently accessed Hive catalogs to perceive data changes. true indicates to enable the Hive metadata cache refresh, and false indicates to disable it. This item is an FE dynamic parameter. You can modify it using the ADMIN SET FRONTEND CONFIG command.
background_refresh_metadata_interval_millis600000 (10 minutes)The interval between two consecutive Hive metadata cache refreshes. Unit: millisecond. This item is an FE dynamic parameter. You can modify it using the ADMIN SET FRONTEND CONFIG command.
background_refresh_metadata_time_secs_since_last_access_secs86400 (24 hours)The expiration time of a Hive metadata cache refresh task. For the Hive catalog that has been accessed, if it has not been accessed for more than the specified time, StarRocks stops refreshing its cached metadata. For the Hive catalog that has not been accessed, StarRocks will not refresh its cached metadata. Unit: second. This item is an FE dynamic parameter. You can modify it using the ADMIN SET FRONTEND CONFIG command.

Using the periodic Hive metadata cache refresh feature and the metadata automatic asynchronous update policy together significantly accelerates data access, reduces the read load from external data sources, and improves query performance.

Appendix: Understand metadata automatic asynchronous update​

Automatic asynchronous update is the default policy that StarRocks uses to update the metadata in Hive catalogs.

By default (namely, when the enable_metastore_cache and enable_remote_file_cache parameters are both set to true), if a query hits a partition of a Hive table, StarRocks automatically caches the metadata of the partition and the metadata of the underlying data files of the partition. The cached metadata is updated by using the lazy update policy.

For example, there is a Hive table named table2, which has four partitions: p1, p2, p3, and p4. A query hits p1, and StarRocks caches the metadata of p1 and the metadata of the underlying data files of p1. Assume that the default time intervals to update and discard the cached metadata are as follows:

  • The time interval (specified by the metastore_cache_refresh_interval_sec parameter) to asynchronously update the cached metadata of p1 is 2 hours.
  • The time interval (specified by the remote_file_cache_refresh_interval_sec parameter) to asynchronously update the cached metadata of the underlying data files of p1 is 60 seconds.
  • The time interval (specified by the metastore_cache_ttl_sec parameter) to automatically discard the cached metadata of p1 is 24 hours.
  • The time interval (specified by the remote_file_cache_ttl_sec parameter) to automatically discard the cached metadata of the underlying data files of p1 is 36 hours.

The following figure shows the time intervals on a timeline for easier understanding.

Timeline for updating and discarding cached metadata

Then StarRocks updates or discards the metadata in compliance with the following rules:

  • If another query hits p1 again and the current time from the last update is less than 60 seconds, StarRocks does not update the cached metadata of p1 or the cached metadata of the underlying data files of p1.
  • If another query hits p1 again and the current time from the last update is more than 60 seconds, StarRocks updates the cached metadata of the underlying data files of p1.
  • If another query hits p1 again and the current time from the last update is more than 2 hours, StarRocks updates the cached metadata of p1.
  • If p1 has not been accessed within 24 hours from the last update, StarRocks discards the cached metadata of p1. The metadata will be cached at the next query.
  • If p1 has not been accessed within 36 hours from the last update, StarRocks discards the cached metadata of the underlying data files of p1. The metadata will be cached at the next query.