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Hello, <https://starrocks.medium.com/starrocks-4-0...
# questions-and-troubleshooting
s
Hello, https://starrocks.medium.com/starrocks-4-0-delivering-query-ready-data-to-apache-iceberg-26c583c767de Can we now choose iceberg as table format when we ingest data into starrocks using shared-data architecture?
r
It sounds like you're asking about a specific claim or feature of StarRocks! Could you please clarify what you are referring to? For example, are you asking if it's true that StarRocks supports: * Vector search? * Real-time analytics on data lakehouses? * Asynchronous materialized views? * Join optimization for complex queries? Once you give me a bit more detail, I can dive into the documentation and give you the full scoop!
k
@Rocky DELETE not supported on JDBC catalogs is it true?
r
Yes, it is true. 
DELETE
is not currently supported on JDBC catalogs in StarRocks. While StarRocks has expanded the capabilities of JDBC catalogs to support some write operations, here is the current status: * Supported: You can use 
INSERT INTO
 to load or transform and write data back to the target database through a JDBC catalog (supported since v3.0). * Not Supported: 
DELETE
 and 
UPDATE
 statements are not supported for tables in a JDBC catalog. These DML operations are primarily supported for StarRocks internal tables (such as Primary Key or Duplicate Key tables). If you attempt to run a 
DELETE
statement on a table within a JDBC catalog, you will typically encounter an error indicating that the operation is only supported on internal (OLAP) tables. Workaround If you need to perform deletions in the source database based on logic within StarRocks, you would typically need to: 1. Perform the deletion directly on the source database (e.g., MySQL, PostgreSQL) using its own client. 2. If the deletion is part of a data synchronization logic, consider using a specialized ETL tool or an CDC (Change Data Capture) pipeline to keep the data in sync. References • data_source/catalog/jdbc_catalog.md • sql-reference/sql-statements/Catalog/DROP_CATALOG.md • data_source/catalog/unified_catalog.md • data_source/External_table.md • data_source/catalog/hive_catalog.md
k
@Rocky so u are saying if we are connecting to the starrocks shared nothing via jdbc then only one CN will be used and not others isnt it design flaw
r
Hey there! I'm Rocky, and I can definitely help clarify that for you. It's a common point of confusion when first looking at the architecture. Actually, it is not a design flaw, and it doesn't work that way! Even if your JDBC connection is established with a single Frontend (FE) node, the query itself is executed across all relevant Compute Nodes (CN) or Backend nodes (BE) in the cluster. Here is how it works under the hood: 1. FE is the Coordinator (Gateway): When you connect via JDBC, you are connecting to an FE. The FE's job is to act as the "brain." It parses your SQL, optimizes it, and creates a distributed execution plan. 2. MPP (Massive Parallel Processing): The FE doesn't do the heavy lifting itself. It breaks your query into "plan fragments" and sends those fragments to multiple BEs/CNs simultaneously. 3. Parallel Execution: All the BEs/CNs involved work on their piece of the data in parallel. They can even exchange data with each other (shuffling) during the query. 4. Result Aggregation: Once the BEs/CNs finish their parts, they send the results back to the FE, which then streams the final result set back to your JDBC client. So, why might it look like one node? * Connection Point: Your JDBC string usually points to one IP (one FE). This is just for the "handshake" and query submission. * Load Balancing: In production, we recommend putting a Load Balancer (like F5, HAProxy, or Nginx) in front of multiple FEs. This way, your JDBC connections are spread across different FEs, ensuring that even the "coordination" work is distributed and you have High Availability (HA). Summary: Your JDBC connection to a single FE is just the entry point. The actual computation scales across your entire cluster of BEs/CNs. You get the full power of the "Shared-nothing" (or Shared-data) architecture regardless of which FE you connect to! References • introduction/Architecture.md • deployment/plan_cluster.md
k
@Rocky can a starrocks native table in shared noting deployment be exposed to be queried via iceberg polaris catalogue outside of the cluster?
r
No, you cannot directly expose an existing StarRocks native table (internal OLAP table) in a shared-nothing deployment to be queried via an Iceberg Polaris catalog. Here is why and how you can achieve a similar result: Why direct exposure is not possible 1. Storage Format: StarRocks native tables in a shared-nothing deployment use a proprietary segment format optimized for its own execution engine. External engines like Spark, Trino, or Flink cannot parse this format. 2. Data Location: In a shared-nothing deployment, data resides on the local disks of the StarRocks Backend (BE) nodes. For an Iceberg catalog like Polaris to work, the data files must be stored in a shared location (such as S3, GCS, or HDFS) that is accessible to all querying engines. The Solution: Use StarRocks as an Iceberg Producer While you cannot "flip a switch" to expose an existing native table, you can use StarRocks to create and manage an Iceberg table that is registered in your Polaris catalog. 1. Create an Iceberg Catalog in StarRocks First, connect StarRocks to your Polaris server by creating an external Iceberg catalog using the REST type:
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sql
CREATE EXTERNAL CATALOG polaris_catalog
PROPERTIES (
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "iceberg.catalog.uri" = "http://<polaris-host>:8181/api/catalog",
    "iceberg.catalog.warehouse" = "your_warehouse_name",
    "iceberg.catalog.security" = "oauth2",
    "iceberg.catalog.oauth2.credential" = "your_credential",
    "iceberg.catalog.oauth2.scope" = "PRINCIPAL_ROLE:ALL"
);
2. Create the Iceberg Table Create a table within that Iceberg catalog. This table will physically reside on your shared storage (e.g., S3) in Iceberg format:
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sql
CREATE TABLE polaris_catalog.your_db.exported_table (
    id BIGINT,
    data STRING
) 
ENGINE=ICEBERG;
3. Sync Data from Native to Iceberg Use an 
INSERT INTO ... SELECT
statement to move or synchronize data from your StarRocks native table to the Iceberg table:
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sql
INSERT INTO polaris_catalog.your_db.exported_table 
SELECT * FROM default_catalog.your_db.native_table;
Summary * Existing Native Tables: Cannot be exposed directly via Polaris because they are local and proprietary. * Managed Iceberg Tables: Since StarRocks v3.3+, you can perform DML (Insert, Update, Delete) on Iceberg tables. By maintaining a copy of your data in an Iceberg catalog through StarRocks, that data becomes "exposed" and queryable by any tool connected to Polaris (Spark, Trino, etc.) outside of the cluster. References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/catalog/iceberg/iceberg_rest_s3.md • data_source/catalog/iceberg/iceberg_rest_security.md
k
@Rocky can shared nothing connecto exterbal polaris iceberg catalog and write data to it
r
Yes, StarRocks (including the shared-nothing architecture) can connect to an external Polaris Iceberg catalog and write data to it. Starting from StarRocks v3.1, DML operations (like 
INSERT INTO
and 
INSERT OVERWRITE
) are supported for Iceberg tables. Polaris uses the Iceberg REST catalog interface, which StarRocks supports natively. 1. Connecting to Polaris To connect to Polaris, you create an External Catalog using the 
rest
catalog type. Below is an example of how to configure it:
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sql
CREATE EXTERNAL CATALOG polaris_iceberg
PROPERTIES
(
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "iceberg.catalog.uri" = "https://<polaris-endpoint>/api/catalog",
    "iceberg.catalog.warehouse" = "your_warehouse_name",
    "iceberg.catalog.security" = "oauth2",
    "iceberg.catalog.oauth2.credential" = "<client_id>:<client_secret>",
    "iceberg.catalog.oauth2.scope" = "PRINCIPAL_ROLE:ALL",
    -- For S3 storage backend
    "aws.s3.region" = "us-west-2",
    "aws.s3.access_key" = "your_access_key",
    "aws.s3.secret_key" = "your_secret_key"
);
2. Writing Data (Sinking) Once the catalog is created, you can write data directly into the Iceberg tables managed by Polaris: * INSERT INTO: Appends data to an existing table. * INSERT OVERWRITE: Replaces data in the table or specific partitions. * CREATE TABLE AS SELECT (CTAS): Creates a new table in the Polaris catalog and populates it with data. Example:
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INSERT INTO polaris_iceberg.db_name.table_name 
SELECT * FROM default_catalog.local_db.local_table;
Key Considerations: * Version Requirements: Data writing to Iceberg requires StarRocks v3.1 or later. * File Formats: Currently, StarRocks supports sinking (writing) data to Iceberg tables in Parquet format. * Shared-Nothing vs. Shared-Data: Both architectures support this feature. In a shared-nothing deployment, the BE nodes handle the parallel write operations directly to the underlying object storage (like S3, GCS, or Azure Blob) while updating the metadata via the Polaris REST API. * Privileges: Ensure the principal provided in the 
oauth2.credential
has the necessary permissions in Polaris to manage and write to the warehouse. References • data_source/catalog/iceberg/DML.md • data_source/feature-support-data-lake-analytics.md • quick_start/iceberg.md • data_source/catalog/iceberg/iceberg_catalog.md • sql-reference/sql-statements/loading_unloading/INSERT.md • data_source/file_external_table.md
k
@Rocky suggest how to connect to starrocks flight sql server from spark to have the true zero copy column to column data input for parquet
r
To achieve true zero-copy, columnar data transfer from StarRocks to Spark, you should use the Arrow Flight SQL protocol (supported from StarRocks v3.5.1 onwards) along with the specialized Spark connector designed for this protocol. This approach replaces the traditional row-based JDBC/MySQL protocols with Apache Arrow's in-memory columnar format, eliminating serialization/deserialization overhead and enabling high-throughput data input directly into Spark. 1. Enable Arrow Flight SQL in StarRocks First, you must enable the Flight SQL service on your StarRocks cluster by configuring the ports and JVM options. In `fe.conf`:
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properties
arrow_flight_port = 9408
# Add JVM opens for Arrow memory management
JAVA_OPTS="-D--add-opens=java.base/java.nio=org.apache.arrow.memory.core,ALL-UNNAMED"
In `be.conf`:
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properties
arrow_flight_port = 9419
Note: Restart FE and BE nodes after applying these changes. 2. Configure Spark with the Flight Connector Since the standard JDBC driver for Spark does not natively handle Arrow Flight's columnar stream for "zero-copy" benefits, you should use the Spark-Flight-Connector. * Repository: qwshen/spark-flight-connector * Dependency: Include the connector JAR in your Spark classpath. Spark Configuration (Submit/Shell): You must provide the same JVM 
add-opens
flags to the Spark driver and executors to allow Arrow to access off-heap memory directly:
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bash
--conf "spark.driver.extraJavaOptions=--add-opens=java.base/java.nio=org.apache.arrow.memory.core,ALL-UNNAMED" \
--conf "spark.executor.extraJavaOptions=--add-opens=java.base/java.nio=org.apache.arrow.memory.core,ALL-UNNAMED"
3. Read Data in Spark Use the 
flight
format in your Spark code. This will fetch data as Arrow batches, which are then mapped directly to Spark's internal columnar representation (InternalRow/ColumnarBatch). Example Code (Scala):
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scala
val df = spark.read
  .format("flight")
  .option("flight.host", "FE_HOST")
  .option("flight.port", "9408")
  .option("flight.user", "your_username")
  .option("flight.password", "your_password")
  .option("flight.sql", "SELECT * FROM your_catalog.your_db.your_table")
  .load()

// Now you can write to Parquet or process further
df.write.parquet("path/to/output")
Why this is "True Zero-Copy" for Columnar Data: 1. Direct Mapping: StarRocks' internal execution engine uses a columnar "Block" structure. With Flight SQL, these blocks are converted to Apache Arrow format (also columnar) with minimal overhead. 2. No Row-Pivot: Traditional JDBC requires StarRocks to convert columns to rows, send them over the wire, and then Spark must convert those rows back into columns for its own processing or for writing to Parquet. Flight SQL keeps the data in columns the entire way. 3. Parallelism: If your Spark executors can reach the StarRocks BE nodes directly, the connector can utilize parallel streams to pull data chunks from multiple BEs simultaneously. Network Considerations (Proxy Mode) If your Spark cluster cannot directly access StarRocks BE nodes (e.g., in a restricted Kubernetes environment), StarRocks can proxy the data through the FE:
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sql
-- Run this in StarRocks to route all Flight data through FE
SET GLOBAL arrow_flight_proxy_enabled = true;
Note: Proxying through FE incurs a small performance overhead (approx. 10%) compared to direct BE access but maintains the columnar format benefits. References • unloading/arrow_flight.md
k
@Rocky can native tables of starrocks be exposed and governed by iceberg table/view?
r
Hello! I'm Rocky. Yes, you can expose and govern data from StarRocks within the Iceberg ecosystem, though there are specific architectural steps to achieve this. Native StarRocks tables (those in the 
default_catalog
) use a proprietary high-performance storage format (Segment V2). To have them "governed" by Iceberg-native tools (like Polaris, AWS Glue, or Apache Ranger), the data or the interface must be represented in the Iceberg format. Here are the primary ways to achieve this: 1. Sinking Native Tables to Iceberg (Data Move/Sync) Starting from StarRocks v3.1, you can create and write to Iceberg tables directly from StarRocks. * How it works: You create an External Catalog (e.g., using Glue, Hive Metastore, or a REST catalog like Polaris). You then create an Iceberg table within that catalog and use an 
INSERT INTO
or 
INSERT OVERWRITE
statement to sink data from your StarRocks native table into the Iceberg table. * Result: The data is stored in Parquet/ORC on your object store (S3/HDFS) with Iceberg metadata, making it fully visible and governable by any Iceberg-compatible tool. 2. Asynchronous Materialized Views (Lakehouse Architecture) A more automated way is to use Asynchronous Materialized Views. * Unified Flow: You can define a Materialized View that reads from your StarRocks native tables but sinks the results into an external Iceberg catalog. * Benefit: This allows you to keep your "hot" data in StarRocks native tables for maximum performance while automatically "exposing" an aggregated or processed version of that data to the Data Lake/Iceberg ecosystem for broad governance and cross-engine access. 3. Iceberg Views (StarRocks v3.5+) Starting from v3.5, StarRocks supports creating Iceberg Views in an external catalog. * How it works: You can execute 
CREATE VIEW
targeting an Iceberg catalog. If the underlying data is already in Iceberg format, this allows you to manage the logic/governance at the view level within the Iceberg metadata layer. 4. Governance via REST Catalogs (e.g., Apache Polaris) StarRocks supports the Iceberg REST Catalog (v3.3+). * If you use a centralized governance tool like Apache Polaris as your Iceberg REST server, you can create a StarRocks External Catalog pointing to it. * Any Iceberg table StarRocks creates or writes to in this catalog is immediately governed by the RBAC and policies defined in Polaris. Summary * Direct exposure: Native tables are not Iceberg-formatted by default. * Integrated exposure: Use StarRocks to sink or materialize data into an Iceberg Catalog. Once the data is in the Iceberg format (managed by a catalog like Glue or Polaris), it is fully governed by the Iceberg ecosystem. If you are looking to build a "Lakehouse" where StarRocks acts as the compute engine while Iceberg provides the storage and governance layer, the best path is to use External Iceberg Catalogs for your primary storage and Materialized Views to bridge the gap between native performance and lakehouse governance. References • using_starrocks/async_mv/Materialized_view.md • using_starrocks/async_mv/use_cases/create_partitioned_materialized_view.md • using_starrocks/async_mv/use_cases/data_lake_query_acceleration_with_materialized_views.md • data_source/feature-support-data-lake-analytics.md • sql-reference/sql-statements/materialized_view/CREATE_MATERIALIZED_VIEW.md
k
@Rocky i have the starrocks table which i want to govern by apace polaris
r
To govern your StarRocks tables using Apache Polaris, you can set up an Iceberg Catalog in StarRocks that points to the Polaris REST endpoint. Since Polaris implements the Iceberg REST Catalog specification, StarRocks can seamlessly integrate with it. Step-by-Step Integration You can create an external catalog in StarRocks to connect to Polaris using the following SQL command:
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sql
CREATE EXTERNAL CATALOG polaris_catalog 
PROPERTIES (   
    "type" = "iceberg",
    "iceberg.catalog.type" = "rest",
    "iceberg.catalog.uri" = "http://<polaris-host>:8181/api/catalog", 
    "iceberg.catalog.warehouse" = "<your_polaris_warehouse_name>",
    "iceberg.catalog.security" = "oauth2",
    "iceberg.catalog.oauth2.credential" = "<client_id>:<client_secret>",
    "iceberg.catalog.oauth2.scope" = "PRINCIPAL_ROLE:ALL"
);
Key Configuration Parameters: * 
type
: Must be set to 
iceberg
. * 
iceberg.catalog.type
: Must be set to 
rest
to communicate with the Polaris REST API. * 
iceberg.catalog.uri
: The API endpoint for your Polaris instance (usually ends in 
/api/catalog
). * 
iceberg.catalog.warehouse
: The specific warehouse name configured in Polaris. * 
iceberg.catalog.security
: Set to 
oauth2
for Polaris authentication. * 
iceberg.catalog.oauth2.credential
: Your Polaris client ID and client secret formatted as 
<client_id>:<client_secret>
. * 
iceberg.catalog.oauth2.scope
: (Optional) Typically set to 
PRINCIPAL_ROLE:ALL
to grant the session the necessary roles defined in Polaris. Using the Catalog Once the catalog is created, you can interact with it just like any other catalog in StarRocks: 1. View Databases:
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sql
    SHOW DATABASES FROM polaris_catalog;
2. Query Tables:
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    SELECT * FROM polaris_catalog.your_db.your_table LIMIT 10;
3. Create Tables (if Polaris is configured for write access):
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sql
    CREATE TABLE polaris_catalog.your_db.new_table (
        id INT,
        name STRING
    );
Why use Polaris with StarRocks? By using Polaris as the catalog, StarRocks honors the centralized governance and access control policies defined in Polaris. This allows you to manage data security and metadata in one place while using StarRocks for high-performance analytics. References • data_source/catalog/iceberg/iceberg_catalog.md • data_source/catalog/iceberg/iceberg_rest_s3.md • data_source/catalog/iceberg/iceberg_rest_security.md
k
@Rocky i have the starrocks native pk tables which i want to govern by apace polaris
r
To govern your StarRocks native Primary Key (PK) tables using Apache Polaris, you effectively need to bridge the data between StarRocks' internal storage engine and the Iceberg format that Polaris manages. Since Apache Polaris is an Iceberg REST Catalog, and StarRocks PK tables are stored in StarRocks' native optimized format, there are two primary ways to achieve this integration: 1. Sync StarRocks PK Tables to Polaris-managed Iceberg Tables You can keep your high-performance PK tables in StarRocks for real-time ingestion and updates, then periodically sync or stream that data into Iceberg tables that are registered in Polaris. Steps: 1. Create an Iceberg REST Catalog in StarRocks pointing to your Polaris instance:
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sql
    CREATE EXTERNAL CATALOG polaris_catalog
    PROPERTIES (
        "type" = "iceberg",
        "iceberg.catalog.type" = "rest",
        "iceberg.catalog.uri" = "http://<polaris-host>:8181/api/catalog",
        "iceberg.catalog.warehouse" = "your_warehouse_name",
        "iceberg.catalog.security" = "oauth2",
        "iceberg.catalog.oauth2.credential" = "your_client_id:your_client_secret",
        "iceberg.catalog.oauth2.scope" = "PRINCIPAL_ROLE:ALL"
    );
2. Create the target table in Polaris via StarRocks:
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sql
    CREATE TABLE polaris_catalog.your_db.governed_table (
        id BIGINT,
        data STRING,
        updated_at DATETIME
    ) ENGINE=iceberg;
3. Sync the data: Use a scheduled 
INSERT INTO ... SELECT
or a StarRocks Materialized View to automatically propagate changes from your native PK table to the Polaris-governed Iceberg table.
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sql
    INSERT INTO polaris_catalog.your_db.governed_table
    SELECT * FROM default_catalog.local_db.native_pk_table;
2. Unified Governance (External Catalog) If you want Polaris to be the "source of truth" for metadata, you can define your tables directly as Iceberg tables within StarRocks using the Polaris catalog. While StarRocks PK tables offer the best performance for frequent updates (UPSERTs), StarRocks 3.x+ allows you to perform 
UPDATE
and 
DELETE
operations on Iceberg tables directly through the REST catalog if the table is configured correctly (using V2 row-level deletes). Why this works: * StarRocks acts as the high-performance compute engine that can both read from and write to Polaris. * Apache Polaris acts as the central catalog, allowing other tools (like Spark, Trino, or Flink) to see the data StarRocks has written into the Iceberg format. Note on PK Tables specifically: StarRocks native Primary Key tables are highly optimized for sub-second UPSERTs. If your governance requirement is purely for audit/compliance/sharing, syncing to Iceberg (Polaris) is the best route. If you need Polaris to govern the live production table, you should consider using StarRocks to manage an Iceberg table directly, keeping in mind that Iceberg's update performance is different from native PK tables. References • data_source/catalog/iceberg/iceberg_catalog.md
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