External table

note

• From v3.0 onwards, we recommend that you use catalogs to query data from Hive, Iceberg, and Hudi. See Hive catalog, Iceberg catalog, and Hudi catalog.

• From v3.1 onwards, we recommend that you use JDBC catalog to query data from MySQL and PostgreSQL and use Elasticsearch catalog to query data from Elasticsearch.

• The External Table feature was designed to help with loading data into StarRocks, NOT to perform efficient queries against external systems as a normal operation. A more performant solution would be to load the data into StarRocks.

StarRocks supports access to other data sources by using external tables. External tables are created based on data tables that are stored in other data sources. StarRocks only stores the metadata of the data tables. You can use external tables to directly query data in other data sources. StarRocks supports the following data sources: MySQL, StarRocks, Elasticsearch, Apache Hive™, Apache Iceberg, and Apache Hudi. Currently, you can only write data from another StarRocks cluster into the current StarRocks cluster. You cannot read data from it. For data sources other than StarRocks, you can only read data from these data sources.

From 2.5 onwards, StarRocks provides the Data Cache feature, which accelerates hot data queriers on external data sources. For more information, see Data Cache.

StarRocks external table

From StarRocks 1.19 onwards, StarRocks allows you to use a StarRocks external table to write data from one StarRocks cluster to another. This achieves read-write separation and provides better resource isolation. You can first create a destination table in the destination StarRocks cluster. Then, in the source StarRocks cluster, you can create a StarRocks external table that has the same schema as the destination table and specify the information of the destination cluster and table in the PROPERTIES field.

Data can be written from a source cluster to a destination cluster by using INSERT INTO statement to write into a StarRocks external table. It can help realize the following goals:

• Data synchronization between StarRocks clusters.
• Read-write separation. Data is written to the source cluster, and data changes from the source cluster are synchronized to the destination cluster, which provides query services.

The following code shows how to create a destination table and an external table.

# Create a destination table in the destination StarRocks cluster.
CREATE TABLE t
(
    k1 DATE,
    k2 INT,
    k3 SMALLINT,
    k4 VARCHAR(2048),
    k5 DATETIME
)
ENGINE=olap
DISTRIBUTED BY HASH(k1);

# Create an external table in the source StarRocks cluster.
CREATE EXTERNAL TABLE external_t
(
    k1 DATE,
    k2 INT,
    k3 SMALLINT,
    k4 VARCHAR(2048),
    k5 DATETIME
)
ENGINE=olap
DISTRIBUTED BY HASH(k1)
PROPERTIES
(
    "host" = "127.0.0.1",
    "port" = "9020",
    "user" = "user",
    "password" = "passwd",
    "database" = "db_test",
    "table" = "t"
);

# Write data from a source cluster to a destination cluster by writing data into the StarRocks external table. The second statement is recommended for the production environment.
insert into external_t values ('2020-10-11', 1, 1, 'hello', '2020-10-11 10:00:00');
insert into external_t select * from other_table;

Parameters:

• EXTERNAL: This keyword indicates that the table to be created is an external table.

• host: This parameter specifies the IP address of the leader FE node of the destination StarRocks cluster.

• port: This parameter specifies the RPC port of the FE node of the destination StarRocks cluster.

  note

  To ensure that the source cluster to which the StarRocks external tables belong can access the destination StarRocks cluster, you must configure your network and firewall to allow access to the following ports:

  • The RPC port of the FE node. See rpc_port in the FE configuration file fe/fe.conf. The default RPC port is 9020.
  • The bRPC port of the BE node. See brpc_port in the BE configuration file be/be.conf. The default bRPC port is 8060.

• user: This parameter specifies the username used to access the destination StarRocks cluster.

• password: This parameter specifies the password used to access the destination StarRocks cluster.

• database: This parameter specifies the database to which the destination table belongs.

• table: This parameter specifies the name of the destination table.

The following limits apply when you use a StarRocks external table:

• You can only run the INSERT INTO and SHOW CREATE TABLE commands on a StarRocks external table. Other data writing methods are not supported. In addition, you cannot query data from a StarRocks external table or perform DDL operations on the external table.
• The syntax of creating an external table is the same as creating a normal table, but the column names and other information in the external table must be the same as the destination table.
• The external table synchronizes table metadata from the destination table every 10 seconds. If a DDL operation is performed on the destination table, there may be a delay for data synchronization between the two tables.

External table for a JDBC-compatible database

From v2.3.0, StarRocks provides external tables to query JDBC-compatible databases. This way, you can analyze the data of such databases in a blazing fast manner without the need to import the data into StarRocks. This section describes how to create an external table in StarRocks and query data in JDBC-compatible databases.

Prerequisites

Before you use a JDBC external table to query data, make sure that the FEs and BEs have access to the download URL of the JDBC driver. The download URL is specified by the driver_url parameter in the statement used for creating the JDBC resource.

Create and manage JDBC resources

Create a JDBC resource

Before you create an external table to query data from a database, you need to create a JDBC resource in StarRocks to manage the connection information of the database. The database must support the JDBC driver and is referred as the "target database". After creating the resource, you can use it to create external tables.

Execute the following statement to create a JDBC resource named jdbc0:

CREATE EXTERNAL RESOURCE jdbc0
PROPERTIES (
    "type"="jdbc",
    "user"="postgres",
    "password"="changeme",
    "jdbc_uri"="jdbc:postgresql://127.0.0.1:5432/jdbc_test",
    "driver_url"="https://repo1.maven.org/maven2/org/postgresql/postgresql/42.3.3/postgresql-42.3.3.jar",
    "driver_class"="org.postgresql.Driver"
);

The required parameters in PROPERTIES are as follows:

• type: the type of the resource. Set the value to jdbc.

• user: the username that is used to connect to the target database.

• password: the password that is used to connect to the target database.

• jdbc_uri: the URI that the JDBC driver uses to connect to the target database. The URI format must satisfy the database URI syntax. For the URI syntax of some common databases, visit the official websites of Oracle, PostgreSQL, SQL Server.

Note: The URI must include the name of the target database. For example, in the preceding code example, jdbc_test is the name of the target database that you want to connect.

• driver_url: the download URL of the JDBC driver JAR package. An HTTP URL or file URL is supported, for example, https://repo1.maven.org/maven2/org/postgresql/postgresql/42.3.3/postgresql-42.3.3.jar or file:///home/disk1/postgresql-42.3.3.jar.

• driver_class: the class name of the JDBC driver. The JDBC driver class names of common databases are as follows:

  • MySQL: com.mysql.jdbc.Driver (MySQL 5.x and earlier), com.mysql.cj.jdbc.Driver (MySQL 6.x and later)
  • SQL Server: com.microsoft.sqlserver.jdbc.SQLServerDriver
  • Oracle: oracle.jdbc.driver.OracleDriver
  • PostgreSQL: org.postgresql.Driver

When the resource is being created, the FE downloads the JDBC driver JAR package by using the URL that is specified in the driver_url parameter, generates a checksum, and uses the checksum to verify the JDBC driver downloaded by BEs.

Note: If the download of the JDBC driver JAR package fails, the creation of the resource also fails.

When BEs query the JDBC external table for the first time and find that the corresponding JDBC driver JAR package does not exist on their machines, BEs download the JDBC driver JAR package by using the URL that is specified in the driver_url parameter, and all JDBC driver JAR packages are saved in the ${STARROCKS_HOME}/lib/jdbc_drivers directory.

View JDBC resources

Execute the following statement to view all JDBC resources in StarRocks:

SHOW RESOURCES;

Note: The ResourceType column is jdbc.

Delete a JDBC resource

Execute the following statement to delete the JDBC resource named jdbc0:

DROP RESOURCE "jdbc0";

Note: After a JDBC resource is deleted, all JDBC external tables that are created by using that JDBC resource are unavailable. However, the data in the target database is not lost. If you still need to use StarRocks to query data in the target database, you can create the JDBC resource and the JDBC external tables again.

Create a database

Execute the following statement to create and access a database named jdbc_test in StarRocks:

CREATE DATABASE jdbc_test; 
USE jdbc_test; 

Note: The database name that you specify in the preceding statement does not need to be same as the name of the target database.

Create a JDBC external table

Execute the following statement to create a JDBC external table named jdbc_tbl in the database jdbc_test:

create external table jdbc_tbl (
    `id` bigint NULL, 
    `data` varchar(200) NULL 
) ENGINE=jdbc 
properties (
    "resource" = "jdbc0",
    "table" = "dest_tbl"
);

The required parameters in properties are as follows:

• resource: the name of the JDBC resource used to create the external table.

• table: the target table name in the database.

For supported data types and data type mapping between StarRocks and target databases, see [Data type mapping](External_table.md#Data type mapping).

Note:

• Indexes are not supported.
• You cannot use PARTITION BY or DISTRIBUTED BY to specify data distribution rules.

Query a JDBC external table

Before you query JDBC external tables, you must execute the following statement to enable the Pipeline engine:

set enable_pipeline_engine=true;

Note: If the Pipeline engine is already enabled, you can skip this step.

Execute the following statement to query the data in the target database by using JDBC external tables.

select * from JDBC_tbl;

StarRocks supports predicate pushdown by pushing down filter conditions to the target table. Executing filter conditions as close as possible to the data source can improve query performance. Currently, StarRocks can push down operators, including the binary comparison operators (>, >=, =, <, and <=), IN, IS NULL, and BETWEEN ... AND ... . However, StarRocks can not push down functions.

Data type mapping

Currently, StarRocks can only query data of basic types in the target database, such as NUMBER, STRING, TIME, and DATE. If the ranges of data values in the target database are not supported by StarRocks, the query reports an error.

The mapping between the target database and StarRocks varies based on the type of the target database.

MySQL and StarRocks

MySQL	StarRocks
BOOLEAN	BOOLEAN
TINYINT	TINYINT
SMALLINT	SMALLINT
MEDIUMINTINT	INT
BIGINT	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
DECIMAL	DECIMAL
CHAR	CHAR
VARCHAR	VARCHAR
DATE	DATE
DATETIME	DATETIME

Oracle and StarRocks

Oracle	StarRocks
CHAR	CHAR
VARCHARVARCHAR2	VARCHAR
DATE	DATE
SMALLINT	SMALLINT
INT	INT
BINARY_FLOAT	FLOAT
BINARY_DOUBLE	DOUBLE
DATE	DATE
DATETIME	DATETIME
NUMBER	DECIMAL

PostgreSQL and StarRocks

PostgreSQL	StarRocks
SMALLINTSMALLSERIAL	SMALLINT
INTEGERSERIAL	INT
BIGINTBIGSERIAL	BIGINT
BOOLEAN	BOOLEAN
REAL	FLOAT
DOUBLE PRECISION	DOUBLE
DECIMAL	DECIMAL
TIMESTAMP	DATETIME
DATE	DATE
CHAR	CHAR
VARCHAR	VARCHAR
TEXT	VARCHAR

SQL Server and StarRocks

SQL Server	StarRocks
BOOLEAN	BOOLEAN
TINYINT	TINYINT
SMALLINT	SMALLINT
INT	INT
BIGINT	BIGINT
FLOAT	FLOAT
REAL	DOUBLE
DECIMALNUMERIC	DECIMAL
CHAR	CHAR
VARCHAR	VARCHAR
DATE	DATE
DATETIMEDATETIME2	DATETIME

Limits

• When you create JDBC external tables, you cannot create indexes on the tables or use PARTITION BY and DISTRIBUTED BY to specify data distribution rules for the tables.

• When you query JDBC external tables, StarRocks cannot push down functions to the tables.

(Deprecated) Elasticsearch external table

StarRocks and Elasticsearch are two popular analytics systems. StarRocks is performant in large-scale distributed computing. Elasticsearch is ideal for full-text search. StarRocks combined with Elasticsearch can deliver a more complete OLAP solution.

Example of creating an Elasticsearch external table

Syntax

CREATE EXTERNAL TABLE elastic_search_external_table
(
    k1 DATE,
    k2 INT,
    k3 SMALLINT,
    k4 VARCHAR(2048),
    k5 DATETIME
)
ENGINE=ELASTICSEARCH
PROPERTIES (
    "hosts" = "http://192.168.0.1:9200,http://192.168.0.2:9200",
    "user" = "root",
    "password" = "root",
    "index" = "tindex",
    "type" = "_doc",
    "es.net.ssl" = "true"
);

The following table describes the parameters.

Parameter	Required	Default value	Description
hosts	Yes	None	The connection address of the Elasticsearch cluster. You can specify one or more addresses. StarRocks can parse the Elasticsearch version and index shard allocation from this address. StarRocks communicates with your Elasticsearch cluster based on the address returned by the GET /_nodes/http API operation. Therefore, the value of the host parameter must be the same as the address returned by the GET /_nodes/http API operation. Otherwise, BEs may not be able to communicate with your Elasticsearch cluster.
index	Yes	None	The name of the Elasticsearch index that is created on the table in StarRocks. The name can be an alias. This parameter supports wildcards (*). For example, if you set index to hello*, StarRocks retrieves all indexes whose names start with hello.
user	No	Empty	The username that is used to log in to the Elasticsearch cluster with basic authentication enabled. Make sure you have access to /*cluster/state/*nodes/http and the index.
password	No	Empty	The password that is used to log in to the Elasticsearch cluster.
type	No	_doc	The type of the index. Default value: _doc. If you want to query data in Elasticsearch 8 and later versions, you do not need to configure this parameter because the mapping types have been removed in Elasticsearch 8 and later versions.
es.nodes.wan.only	No	false	Specifies whether StarRocks only uses the addresses specified by hosts to access the Elasticsearch cluster and fetch data. true: StarRocks only uses the addresses specified by hosts to access the Elasticsearch cluster and fetch data and does not sniff data nodes on which the shards of the Elasticsearch index reside. If StarRocks cannot access the addresses of the data nodes inside the Elasticsearch cluster, you need to set this parameter to true. false: StarRocks uses the addresses specified by host to sniff data nodes on which the shards of the Elasticsearch cluster indexes reside. After StarRocks generates a query execution plan, the relevant BEs directly access the data nodes inside the Elasticsearch cluster to fetch data from the shards of indexes. If StarRocks can access the addresses of the data nodes inside the Elasticsearch cluster, we recommend that you retain the default value false.
es.net.ssl	No	false	Specifies whether the HTTPS protocol can be used to access your Elasticsearch cluster. Only StarRocks 2.4 and later versions support configuring this parameter. true: Both the HTTPS and HTTP protocols can be used to access your Elasticsearch cluster. false: Only the HTTP protocol can be used to access your Elasticsearch cluster.
enable_docvalue_scan	No	true	Specifies whether to obtain the values of the target fields from Elasticsearch columnar storage. In most cases, reading data from columnar storage outperforms reading data from row storage.
enable_keyword_sniff	No	true	Specifies whether to sniff TEXT-type fields in Elasticsearch based on KEYWORD-type fields. If this parameter is set to false, StarRocks performs matching after tokenization.

Columnar scan for faster queries

If you set enable_docvalue_scan to true, StarRocks follows these rules when it obtains data from Elasticsearch:

• Try and see: StarRocks automatically checks if columnar storage is enabled for the target fields. If so, StarRocks obtains all values in the target fields from columnar storage.
• Auto-downgrading: If any one of the target fields is unavailable in columnar storage, StarRocks parses and obtains all values in the target fields from row storage (_source).

NOTE

• Columnar storage is unavailable for TEXT-type fields in Elasticsearch. Therefore, if you query fields containing TEXT-type values, StarRocks obtains the values of the fields from _source.
• If you query a large number (greater than or equal to 25) of fields, reading field values from docvalue does not show noticeable benefits compared with reading field values from _source.

Sniff KEYWORD-type fields

If you set enable_keyword_sniff to true, Elasticsearch allows direct data ingestion without an index because it will automatically create an index after ingestion. For STRING-type fields, Elasticsearch will create a field with both TEXT and KEYWORD types. This is how the Multi-Field feature of Elasticsearch works. The mapping is as follows:

"k4": {
   "type": "text",
   "fields": {
      "keyword": {   
         "type": "keyword",
         "ignore_above": 256
      }
   }
}

For example, to conduct "=" filtering on k4, StarRocks on Elasticsearch will convert the filtering operation into an Elasticsearch TermQuery.

The original SQL filter is as follows:

k4 = "StarRocks On Elasticsearch"

The converted Elasticsearch query DSL is as follows:

"term" : {
    "k4": "StarRocks On Elasticsearch"

}

The first field of k4 is TEXT, and it will be tokenized by the analyzer configured for k4 (or by the standard analyzer if no analyzer has been configured for k4) after data ingestion. As a result, the first field will be tokenized into three terms: StarRocks, On, and Elasticsearch. The details are as follows:

POST /_analyze
{
  "analyzer": "standard",
  "text": "StarRocks On Elasticsearch"
}

The tokenization results are as follows:

{
   "tokens": [
      {
         "token": "starrocks",
         "start_offset": 0,
         "end_offset": 5,
         "type": "<ALPHANUM>",
         "position": 0
      },
      {
         "token": "on",
         "start_offset": 6,
         "end_offset": 8,
         "type": "<ALPHANUM>",
         "position": 1
      },
      {
         "token": "elasticsearch",
         "start_offset": 9,
         "end_offset": 11,
         "type": "<ALPHANUM>",
         "position": 2
      }
   ]
}

Suppose you conduct a query as follows:

"term" : {
    "k4": "StarRocks On Elasticsearch"
}

There is no term in the dictionary that matches the term StarRocks On Elasticsearch, and therefore no result will be returned.

However, if you have set enable_keyword_sniff to true, StarRocks will convert k4 = "StarRocks On Elasticsearch" to k4.keyword = "StarRocks On Elasticsearch" to match the SQL semantics. The converted StarRocks On Elasticsearch query DSL is as follows:

"term" : {
    "k4.keyword": "StarRocks On Elasticsearch"
}

k4.keyword is of the KEYWORD type. Therefore, the data is written into Elasticsearch as a complete term, allowing for successful matching.

Mapping of column data types

When you create an external table, you need to specify the data types of columns in the external table based on the data types of columns in the Elasticsearch table. The following table shows the mapping of column data types.

Elasticsearch	StarRocks
BOOLEAN	BOOLEAN
BYTE	TINYINT/SMALLINT/INT/BIGINT
SHORT	SMALLINT/INT/BIGINT
INTEGER	INT/BIGINT
LONG	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
KEYWORD	CHAR/VARCHAR
TEXT	CHAR/VARCHAR
DATE	DATE/DATETIME
NESTED	CHAR/VARCHAR
OBJECT	CHAR/VARCHAR
ARRAY	ARRAY

Note

• StarRocks reads the data of the NESTED type by using JSON-related functions.
• Elasticsearch automatically flattens multi-dimensional arrays into one-dimensional arrays. StarRocks does the same. The support for querying ARRAY data from Elasticsearch is added from v2.5.

Predicate pushdown

StarRocks supports predicate pushdown. Filters can be pushed down to Elasticsearch for execution, which improves query performance. The following table lists the operators that support predicate pushdown.

SQL syntax	ES syntax
=	term query
in	terms query
>=, <=, >, <	range
and	bool.filter
or	bool.should
not	bool.must_not
not in	bool.must_not + terms
esquery	ES Query DSL

Examples

The esquery function is used to push down queries that cannot be expressed in SQL (such as match and geoshape) to Elasticsearch for filtering. The first parameter in the esquery function is used to associate an index. The second parameter is a JSON expression of basic Query DSL, which is enclosed in brackets . The JSON expression must have but only one root key, such as match, geo_shape, or bool.

• match query

select * from es_table where esquery(k4, '{
    "match": {
       "k4": "StarRocks on elasticsearch"
    }
}');

• geo-related query

select * from es_table where esquery(k4, '{
  "geo_shape": {
     "location": {
        "shape": {
           "type": "envelope",
           "coordinates": [
              [
                 13,
                 53
              ],
              [
                 14,
                 52
              ]
           ]
        },
        "relation": "within"
     }
  }
}');

• bool query

select * from es_table where esquery(k4, ' {
     "bool": {
        "must": [
           {
              "terms": {
                 "k1": [
                    11,
                    12
                 ]
              }
           },
           {
              "terms": {
                 "k2": [
                    100
                 ]
              }
           }
        ]
     }
  }');

Usage notes

• Elasticsearch earlier than 5.x scans data in a different way than that later than 5.x. Currently, only versions later than 5.x are supported.
• Elasticsearch clusters with HTTP basic authentication enabled are supported.
• Querying data from StarRocks may not be as fast as directly querying data from Elasticsearch, such as count-related queries. The reason is that Elasticsearch directly reads the metadata of target documents without the need to filter the real data, which accelerates the count query.

(Deprecated) Hive external table

Before using Hive external tables, make sure JDK 1.8 has been installed on your servers.

Create a Hive resource

A Hive resource corresponds to a Hive cluster. You must configure the Hive cluster used by StarRocks, such as the Hive metastore address. You must specify the Hive resource that is used by the Hive external table.

• Create a Hive resource named hive0.

CREATE EXTERNAL RESOURCE "hive0"
PROPERTIES (
  "type" = "hive",
  "hive.metastore.uris" = "thrift://xx.xx.xx.xx:9083"
);

• View the resources created in StarRocks.

SHOW RESOURCES;

• Delete the resource named hive0.

DROP RESOURCE "hive0";

You can modify hive.metastore.uris of a Hive resource in StarRocks 2.3 and later versions. For more information, see ALTER RESOURCE.

Create a database

CREATE DATABASE hive_test;
USE hive_test;

Create a Hive external table

Syntax

CREATE EXTERNAL TABLE table_name (
  col_name col_type [NULL | NOT NULL] [COMMENT "comment"]
) ENGINE=HIVE
PROPERTIES (
  "key" = "value"
);

Example: Create the external table profile_parquet_p7 under the rawdata database in the Hive cluster corresponding to the hive0 resource.

CREATE EXTERNAL TABLE `profile_wos_p7` (
  `id` bigint NULL,
  `first_id` varchar(200) NULL,
  `second_id` varchar(200) NULL,
  `p__device_id_list` varchar(200) NULL,
  `p__is_deleted` bigint NULL,
  `p_channel` varchar(200) NULL,
  `p_platform` varchar(200) NULL,
  `p_source` varchar(200) NULL,
  `p__city` varchar(200) NULL,
  `p__province` varchar(200) NULL,
  `p__update_time` bigint NULL,
  `p__first_visit_time` bigint NULL,
  `p__last_seen_time` bigint NULL
) ENGINE=HIVE
PROPERTIES (
  "resource" = "hive0",
  "database" = "rawdata",
  "table" = "profile_parquet_p7"
);

Description:

• Columns in the external table

  • The column names must be the same as column names in the Hive table.
  • The column order does not need to be the same as column order in the Hive table.
  • You can select only some of the columns in the Hive table, but you must select all the partition key columns.
  • Partition key columns of an external table do not need to be specified by using partition by. They must be defined in the same description list as other columns. You do not need to specify partition information. StarRocks will automatically synchronize this information from the Hive table.
  • Set ENGINE to HIVE.

• PROPERTIES:

  • hive.resource: the Hive resource that is used.
  • database: the Hive database.
  • table: the table in Hive. view is not supported.

• The following table describes the column data type mapping between Hive and StarRocks.

  Column type of Hive	Column type of StarRocks	Description
  INT/INTEGER	INT
  BIGINT	BIGINT
  TIMESTAMP	DATETIME	Precision and time zone information will be lost when you convert TIMESTAMP data into DATETIME data. You need to convert TIMESTAMP data into DATETIME data that does not have the time zone offset based on the time zone in sessionVariable.
  STRING	VARCHAR
  VARCHAR	VARCHAR
  CHAR	CHAR
  DOUBLE	DOUBLE
  FLOAT	FLOAT
  DECIMAL	DECIMAL
  ARRAY	ARRAY

Note:

• Currently, the supported Hive storage formats are Parquet, ORC, and CSV. If the storage format is CSV, quotation marks cannot be used as escape characters.
• The SNAPPY and LZ4 compression formats are supported.
• The maximum length of a Hive string column that can be queried is 1 MB. If a string column exceeds 1 MB, it will be processed as a null column.

Use a Hive external table

Query the total number of rows of profile_wos_p7.

select count(*) from profile_wos_p7;

Update cached Hive table metadata

• Hive partition information and the related file information are cached in StarRocks. The cache is refreshed at intervals specified by hive_meta_cache_refresh_interval_s. The default value is 7200. hive_meta_cache_ttl_s specifies the timeout duration of the cache and the default value is 86400.
  • The cached data can also be refreshed manually.
    1. If a partition is added or deleted from a table in Hive, you must run the REFRESH EXTERNAL TABLE hive_t command to refresh the table metadata cached in StarRocks. hive_t is the name of the Hive external table in StarRocks.
    2. If data in some Hive partitions is updated, you must refresh the cached data in StarRocks by running the REFRESH EXTERNAL TABLE hive_t PARTITION ('k1=01/k2=02', 'k1=03/k2=04') command. hive_t is the name of the Hive external table in StarRocks. 'k1=01/k2=02' and 'k1=03/k2=04' are the names of Hive partitions whose data is updated.
    3. When you run REFRESH EXTERNAL TABLE hive_t, StarRocks first checks if the column information of the Hive external table is the same as the column information of the Hive table returned by the Hive Metastore. If the schema of the Hive table changes, such as adding columns or removing columns, StarRocks synchronizes the changes to the Hive external table. After synchronization, the column order of the Hive external table remains the same as the column order of the Hive table, with the partition column being the last column.
• When Hive data is stored in the Parquet, ORC, and CSV format, you can synchronize schema changes (such as ADD COLUMN and REPLACE COLUMN) of a Hive table to a Hive external table in StarRocks 2.3 and later versions.

Access object storage

• The path of the FE configuration file is fe/conf, to which the configuration file can be added if you need to customize the Hadoop cluster. For example: If the HDFS cluster uses a highly available nameservice, you need to put hdfs-site.xml under fe/conf. If HDFS is configured with ViewFs, you need to put the core-site.xml under fe/conf.

• The path of the BE configuration file is be/conf, to which the configuration file can be added if you need to customize the Hadoop cluster. For example, if the HDFS cluster using a highly available nameservice, you need to put hdfs-site.xml under be/conf. If HDFS is configured with ViewFs, you need to put core-site.xml under be/conf.

• On the machine where BE is located, configure JAVA_HOME as a JDK environment rather than a JRE environment in the BE startup script bin/start_be.sh, for example, export JAVA_HOME = <JDK path>. You must add this configuration at the beginning of the script and restart the BE for the configuration to take effect.

• Configure Kerberos support:

  1. To log in with kinit -kt keytab_path principal to all FE/BE machines, you need to have access to Hive and HDFS. The kinit command login is only good for a period of time and needs to be put into crontab to be executed regularly.
  2. Put hive-site.xml/core-site.xml/hdfs-site.xml under fe/conf, and put core-site.xml/hdfs-site.xml under be/conf.
  3. Add -Djava.security.krb5.conf=/etc/krb5.conf to the value of the JAVA_OPTS option in the $FE_HOME/conf/fe.conf file. /etc/krb5.conf is the save path of the krb5.conf file. You can change the path based on your operating system.
  4. Directly add JAVA_OPTS="-Djava.security.krb5.conf=/etc/krb5.conf" to the $BE_HOME/conf/be.conf file. /etc/krb5.conf is the save path of the krb5.conf file. You can change the path based on your operating system.
  5. When you add a Hive resource, you must pass in a domain name to hive.metastore.uris. In addition, you must add the mapping between Hive/HDFS domain names and IP addresses in the /etc/hosts file.

• Configure support for AWS S3: Add the following configuration to fe/conf/core-site.xml and be/conf/core-site.xml.

  <configuration>
     <property>
        <name>fs.s3a.access.key</name>
        <value>******</value>
     </property>
     <property>
        <name>fs.s3a.secret.key</name>
        <value>******</value>
     </property>
     <property>
        <name>fs.s3a.endpoint</name>
        <value>s3.us-west-2.amazonaws.com</value>
     </property>
     <property>
     <name>fs.s3a.connection.maximum</name>
     <value>500</value>
     </property>
  </configuration>
  1. fs.s3a.access.key: the AWS access key ID.
  2. fs.s3a.secret.key: the AWS secret key.
  3. fs.s3a.endpoint: the AWS S3 endpoint to connect to.
  4. fs.s3a.connection.maximum: the maximum number of concurrent connections from StarRocks to S3. If an error Timeout waiting for connection from poll occurs during a query, you can set this parameter to a larger value.

(Deprecated) Iceberg external table

From v2.1.0, StarRocks allows you to query data from Apache Iceberg by using external tables. To query data in Iceberg, you need to create an Iceberg external table in StarRocks. When you create the table, you need to establish mapping between the external table and the Iceberg table you want to query.

Before you begin

Make sure that StarRocks has permissions to access the metadata service (such as Hive metastore), file system (such as HDFS), and object storage system (such as Amazon S3 and Alibaba Cloud Object Storage Service) used by Apache Iceberg.

Precautions

• Iceberg external tables can be used to query only the following types of data:

  • Iceberg v1 tables (Analytic Data Tables). ORC-formatted Iceberg v2 (Row-level Deletes) tables are supported from v3.0 onwards, and Parquet-formatted Iceberg v2 tables are supported from v3.1 onwards. For the differences between Iceberg v1 tables and Iceberg v2 tables, see Iceberg Table Spec.
  • Tables that are compressed in gzip (default format), Zstd, LZ4, or Snappy format.
  • Files that are stored in Parquet or ORC format.

• Iceberg external tables in StarRocks 2.3 and later versions support synchronizing schema changes of an Iceberg table while Iceberg external tables in versions earlier than StarRocks 2.3 do not. If the schema of an Iceberg table changes, you must delete the corresponding external table and create a new one.

Procedure

Step 1: Create an Iceberg resource

Before you create an Iceberg external table, you must create an Iceberg resource in StarRocks. The resource is used to manage the Iceberg access information. Additionally, you also need to specify this resource in the statement that is used to create the external table. You can create a resource based on your business requirements:

• If the metadata of an Iceberg table is obtained from a Hive metastore, you can create a resource and set the catalog type to HIVE.

• If the metadata of an Iceberg table is obtained from other services, you need to create a custom catalog. Then create a resource and set the catalog type to CUSTOM.

Create a resource whose catalog type is HIVE

For example, create a resource named iceberg0 and set the catalog type to HIVE.

CREATE EXTERNAL RESOURCE "iceberg0" 
PROPERTIES (
   "type" = "iceberg",
   "iceberg.catalog.type" = "HIVE",
   "iceberg.catalog.hive.metastore.uris" = "thrift://xx.xx.xx.xx:9083" 
);

The following table describes the related parameters.

Parameter	Description
type	The resource type. Set the value to iceberg.
iceberg.catalog.type	The catalog type of the resource. Both Hive catalog and custom catalog are supported. If you specify a Hive catalog, set the value to HIVE.If you specify a custom catalog, set the value to CUSTOM.
iceberg.catalog.hive.metastore.uris	The URI of the Hive metastore. The parameter value is in the following format: thrift://< IP address of Iceberg metadata >:< port number >. The port number defaults to 9083. Apache Iceberg uses a Hive catalog to access the Hive metastore and then queries the metadata of Iceberg tables.

Create a resource whose catalog type is CUSTOM

A custom catalog needs to inherit the abstract class BaseMetastoreCatalog, and you need to implement the IcebergCatalog interface. Additionally, the class name of a custom catalog cannot be duplicated with the name of the class that already exists in StarRock. After the catalog is created, package the catalog and its related files, and place them under the fe/lib path of each frontend (FE). Then restart each FE. After you complete the preceding operations, you can create a resource whose catalog is a custom catalog.

For example, create a resource named iceberg1 and set the catalog type to CUSTOM.

CREATE EXTERNAL RESOURCE "iceberg1" 
PROPERTIES (
   "type" = "iceberg",
   "iceberg.catalog.type" = "CUSTOM",
   "iceberg.catalog-impl" = "com.starrocks.IcebergCustomCatalog" 
);

The following table describes the related parameters.

Parameter	Description
type	The resource type. Set the value to iceberg.
iceberg.catalog.type	The catalog type of the resource. Both Hive catalog and custom catalog are supported. If you specify a Hive catalog, set the value to HIVE. If you specify a custom catalog, set the value to CUSTOM.
iceberg.catalog-impl	The fully qualified class name of the custom catalog. FEs search for the catalog based on this name. If the catalog contains custom configuration items, you must add them to the PROPERTIES parameter as key-value pairs when you create an Iceberg external table.

You can modify hive.metastore.uris and iceberg.catalog-implof a Iceberg resource in StarRocks 2.3 and later versions. For more information, see ALTER RESOURCE.

View Iceberg resources

SHOW RESOURCES;

Drop an Iceberg resource

For example, drop a resource named iceberg0.

DROP RESOURCE "iceberg0";

Dropping an Iceberg resource makes all external tables that reference this resource unavailable. However, the corresponding data in Apache Iceberg is not deleted. If you still need to query the data in Apache Iceberg, create a new resource and a new external table.

Step 2: (Optional) Create a database

For example, create a database named iceberg_test in StarRocks.

CREATE DATABASE iceberg_test; 
USE iceberg_test; 

Note: The name of the database in StarRocks can be different from the name of the database in Apache Iceberg.

Step 3: Create an Iceberg external table

For example, create an Iceberg external table named iceberg_tbl in the database iceberg_test.

CREATE EXTERNAL TABLE `iceberg_tbl` ( 
    `id` bigint NULL, 
    `data` varchar(200) NULL 
) ENGINE=ICEBERG 
PROPERTIES ( 
    "resource" = "iceberg0", 
    "database" = "iceberg", 
    "table" = "iceberg_table" 
); 

The following table describes the related parameters.

Parameter	Description
ENGINE	The engine name. Set the value to ICEBERG.
resource	The name of the Iceberg resource that the external table references.
database	The name of the database to which the Iceberg table belongs.
table	The name of the Iceberg table.

Note:

• The name of the external table can be different from the name of the Iceberg table.

• The column names of the external table must be the same as those in the Iceberg table. The column order of the two tables can be different.

If you define configuration items in the custom catalog and want configuration items to take effect when you query data, you can add the configuration items to the PROPERTIES parameter as key-value pairs when you create an external table. For example, if you define a configuration item custom-catalog.properties in the custom catalog, you can run the following command to create an external table.

CREATE EXTERNAL TABLE `iceberg_tbl` ( 
    `id` bigint NULL, 
    `data` varchar(200) NULL 
) ENGINE=ICEBERG 
PROPERTIES ( 
    "resource" = "iceberg0", 
    "database" = "iceberg", 
    "table" = "iceberg_table",
    "custom-catalog.properties" = "my_property"
); 

When you create an external table, you need to specify the data types of columns in the external table based on the data types of columns in the Iceberg table. The following table shows the mapping of column data types.

Iceberg table	Iceberg external table
BOOLEAN	BOOLEAN
INT	TINYINT / SMALLINT / INT
LONG	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
DECIMAL(P, S)	DECIMAL
DATE	DATE / DATETIME
TIME	BIGINT
TIMESTAMP	DATETIME
STRING	STRING / VARCHAR
UUID	STRING / VARCHAR
FIXED(L)	CHAR
BINARY	VARCHAR
LIST	ARRAY

StarRocks does not support querying Iceberg data whose data type is TIMESTAMPTZ, STRUCT, and MAP.

Step 4: Query the data in Apache Iceberg

After an external table is created, you can query the data in Apache Iceberg by using the external table.

select count(*) from iceberg_tbl;

(Deprecated) Hudi external table

From v2.2.0, StarRocks allows you to query data from Hudi data lakes by using Hudi external tables, thus facilitating blazing-fast data lake analytics. This topic describes how to create a Hudi external table in your StarRocks cluster and use the Hudi external table to query data from a Hudi data lake.

Before you begin

Make sure that your StarRocks cluster is granted access to the Hive metastore, HDFS cluster, or bucket with which you can register Hudi tables.

Precautions

• Hudi external tables for Hudi are read-only and can be used only for queries.
• StarRocks supports querying Copy on Write and Merge On Read tables (MOR tables are supported from v2.5). For the differences between these two types of tables, see Table & Query Types.
• StarRocks supports the following two query types of Hudi: Snapshot Queries and Read Optimized Queries (Hudi only supports performing Read Optimized Queries on Merge On Read tables). Incremental Queries are not supported. For more information about the query types of Hudi, see Table & Query Types.
• StarRocks supports the following compression formats for Hudi files: gzip, zstd, LZ4, and Snappy. The default compression format for Hudi files is gzip.
• StarRocks cannot synchronize schema changes from Hudi managed tables. For more information, see Schema Evolution. If the schema of a Hudi managed table is changed, you must delete the associated Hudi external table from your StarRocks cluster and then re-create that external table.

Procedure

Step 1: Create and manage Hudi resources

You must create Hudi resources in your StarRocks cluster. The Hudi resources are used to manage the Hudi databases and external tables that you create in your StarRocks cluster.

Create a Hudi resource

Execute the following statement to create a Hudi resource named hudi0:

CREATE EXTERNAL RESOURCE "hudi0" 
PROPERTIES ( 
    "type" = "hudi", 
    "hive.metastore.uris" = "thrift://xx.xx.xx.xx:9083"
);

The following table describes the parameters.

Parameter	Description
type	The type of the Hudi resource. Set the vaue to hudi.
hive.metastore.uris	The Thrift URI of the Hive metastore to which the Hudi resource connects. After connecting the Hudi resource to a Hive metastore, you can create and manage Hudi tables by using Hive. The Thrift URI is in the <IP address of the Hive metastore>:<Port number of the Hive metastore> format. The default port number is 9083.

From v2.3 onwards, StarRocks allows changing the hive.metastore.uris value of a Hudi resource. For more information, see ALTER RESOURCE.

View Hudi resources

Execute the following statement to view all Hudi resources that are created in your StarRocks cluster:

SHOW RESOURCES;

Delete a Hudi resource

Execute the following statement to delete the Hudi resource named hudi0:

DROP RESOURCE "hudi0";

Note:

Deleting a Hudi resource causes unavailability of all Hudi external tables that are created by using that Hudi resource. However, the deletion does not affect your data stored in Hudi. If you still want to query your data from Hudi by using StarRocks, you must re-create Hudi resources, Hudi databases, and Hudi external tables in your StarRocks cluster.

Step 2: Create Hudi databases

Execute the following statement to create and open a Hudi database named hudi_test in your StarRocks cluster:

CREATE DATABASE hudi_test; 
USE hudi_test; 

Note:

The name that you specify for the Hudi database in your StarRocks cluster does not need to be the same as the associated database in Hudi.

Step 3: Create Hudi external tables

Execute the following statement to create a Hudi external table named hudi_tbl in the hudi_test Hudi database:

CREATE EXTERNAL TABLE `hudi_tbl` ( 
    `id` bigint NULL, 
    `data` varchar(200) NULL 
) ENGINE=HUDI 
PROPERTIES ( 
    "resource" = "hudi0", 
    "database" = "hudi", 
    "table" = "hudi_table" 
); 

The following table describes the parameters.

Parameter	Description
ENGINE	The query engine of the Hudi external table. Set the value to HUDI.
resource	The name of the Hudi resource in your StarRocks cluster.
database	The name of the Hudi database to which the Hudi external table belongs in your StarRocks cluster.
table	The Hudi managed table with which the Hudi external table is associated.

Note:

• The name that you specify for the Hudi external table does not need to be the same as the associated Hudi managed table.

• The columns in the Hudi external table must have the same names but can be in a different sequence compared to their counterpart columns in the associated Hudi managed table.

• You can select some or all columns from the associated Hudi managed table and create only the selected columns in the Hudi external table. The following table lists the mapping between the data types supported by Hudi and the data types supported by StarRocks.

Data types supported by Hudi	Data types supported by StarRocks
BOOLEAN	BOOLEAN
INT	TINYINT/SMALLINT/INT
DATE	DATE
TimeMillis/TimeMicros	TIME
TimestampMillis/TimestampMicros	DATETIME
LONG	BIGINT
FLOAT	FLOAT
DOUBLE	DOUBLE
STRING	CHAR/VARCHAR
ARRAY	ARRAY
DECIMAL	DECIMAL

Note

StarRocks does not support querying data of the STRUCT or MAP type, nor does it support querying data of the ARRAY type in Merge On Read tables.

Step 4: Query data from a Hudi external table

After you create a Hudi external table associated with a specific Hudi managed table, you do not need to load data into the Hudi external table. To query data from Hudi, execute the following statement:

SELECT COUNT(*) FROM hudi_tbl;

(Deprecated) MySQL external table

In the star schema, data is generally divided into dimension tables and fact tables. Dimension tables have less data but involve UPDATE operations. Currently, StarRocks does not support direct UPDATE operations (update can be implemented by using the Unique Key table). In some scenarios, you can store dimension tables in MySQL for direct data read.

To query MySQL data, you must create an external table in StarRocks and map it to the table in your MySQL database. You need to specify the MySQL connection information when creating the table.

CREATE EXTERNAL TABLE mysql_external_table
(
    k1 DATE,
    k2 INT,
    k3 SMALLINT,
    k4 VARCHAR(2048),
    k5 DATETIME
)
ENGINE=mysql
PROPERTIES
(
    "host" = "127.0.0.1",
    "port" = "3306",
    "user" = "mysql_user",
    "password" = "mysql_passwd",
    "database" = "mysql_db_test",
    "table" = "mysql_table_test"
);

Parameters:

• host: the connection address of the MySQL database
• port: the port number of the MySQL database
• user: the username to log in to MySQL
• password: the password to log in to MySQL
• database: the name of the MySQL database
• table: the name of the table in the MySQL database