@Rocky we want to CDC a materialized view from StarRocks to Postgres. What tools are recommended?

To capture changes (CDC) from a StarRocks Materialized View (MV) and sync them to PostgreSQL, the recommended approach involves using tools that can consume StarRocks' Binlog feature or read from its physical storage. Since Asynchronous Materialized Views in StarRocks are stored as physical tables, you can treat them like standard tables for data egress. Recommended Tools 1. Apache Flink (Recommended Open Source) Flink is the most robust tool for this scenario. You would use the StarRocks Flink Connector as the source and the standard Flink JDBC Sink for PostgreSQL. * StarRocks Source Connector: Since StarRocks v3.0, the Flink connector supports reading data from StarRocks. If your Materialized View is a Primary Key table (or if you have enabled Binlog on the underlying physical table), Flink can capture incremental changes. * Postgres Sink: Flink’s

jdbc

connector handles writing to PostgreSQL with support for Upsert (if primary keys are defined). 2. BladePipe or CloudCanal These are modern data integration platforms that have native support for StarRocks as a source. * BladePipe: Specifically designed for low-latency CDC pipelines. It can simplify the process by handling schema mapping and CDC capture without requiring a complex Kafka/Flink setup. * CloudCanal: Provides a visual interface to set up "StarRocks → PostgreSQL" synchronization tasks, supporting both full and incremental data migration. 3. DataX (Batch/Periodic) If you do not need true real-time CDC but rather a way to sync data after each MV refresh: * Use DataX with the StarRocks Reader and a PostgreSQL Writer. You can schedule this to run after your Materialized View finishes its refresh cycle. ────────── Key Technical Considerations * Binlog Support: For true CDC, you must ensure the Materialized View's underlying table has the Binlog feature enabled. You can enable it via:

sql
    ALTER TABLE your_mv_name SET ("binlog.enable" = "true");

*Note: Currently, Binlog is most mature for Primary Key tables. If your MV is a Duplicate or Aggregate table, verify the specific StarRocks version support (v3.2+ introduced broader support).* * Asynchronous Refresh: Since StarRocks asynchronous MVs refresh in batches (even if triggered frequently), the "CDC" stream will reflect these batch updates. If you need row-level CDC from the source data, it is often better to CDC the base tables directly to Postgres rather than the MV. * Primary Key Requirement: To perform updates/deletes in PostgreSQL (Upsert), your Materialized View in StarRocks should ideally have a unique identifier or Primary Key that maps to the Postgres table. Summary of Workflow (Flink Example) 1. Enable Binlog on the StarRocks MV. 2. Set up a Flink Job using the

flink-connector-starrocks

. 3. Define a Flink SQL Source pointing to your StarRocks MV. 4. Define a Flink SQL Sink pointing to your Postgres instance. 5. Run

INSERT INTO pg_sink SELECT * FROM sr_source;

. References • integrations/loading_tools/SMT.md • faq/Exporting_faq.md • data_source/External_table.md • loading/Kafka-connector-starrocks.md

@Rocky Tell me more about Bladepipe and how it can be setup?

BladePipe is a professional data integration platform that provides a visualized, automated way to handle Change Data Capture (CDC) and data migration. It is the international version of CloudCanal and is specifically optimized for high-performance databases like StarRocks. While most people use it to move data into StarRocks, it also supports StarRocks as a source for downstream systems like PostgreSQL. How BladePipe Works for StarRocks CDC BladePipe uses StarRocks' Binlog feature (available in v3.0+) to track row-level changes. Since an Asynchronous Materialized View in StarRocks is stored as a physical table, BladePipe can treat it as a standard source table to capture updates and sync them to PostgreSQL. Step-by-Step Setup: StarRocks → PostgreSQL 1. Prepare StarRocks (Source) You must enable the Binlog feature on the Materialized View (or the underlying table) for BladePipe to capture changes.

sql
-- Enable binlog for the specific MV
ALTER TABLE your_mv_name SET ("binlog.enable" = "true");

Note: Ensure your StarRocks cluster is v3.0 or higher. 2. Install BladePipe Worker You can deploy a BladePipe Worker via Docker or binary on a machine that has network access to both StarRocks and your PostgreSQL instance. * Docker:

docker run -d --name bladepipe-worker ...

(Refer to BladePipe official docs for the specific image). 3. Add DataSources in BladePipe Console Log in to the BladePipe Cloud/Console and navigate to DataSource > Add DataSource. * StarRocks (Source): * Type: StarRocks * Client Address: The FE IP and query port (typically 9030). * Account/Password: A user with

SELECT

permissions on the MV. * PostgreSQL (Target): * Type: PostgreSQL * Host/Port: Your Postgres connection details. * Database: The target database name. 4. Create the DataJob 1. Go to DataJob > Create DataJob. 2. Select Source & Target: Choose the StarRocks source and PostgreSQL target created in the previous step. 3. Select Job Type: Choose Incremental (this is the CDC part). You should also select Full Data if you want to migrate existing data before starting the real-time sync. 4. Select Tables: Find your Materialized View in the list. BladePipe will automatically map the schema to a PostgreSQL table. 5. Configure Columns: Map the columns as needed. 6. Confirm & Run: Once confirmed, BladePipe will start the "Full Migration" task followed by the "Incremental" CDC task. ────────── Why use BladePipe over other tools? * No Code: You don't need to write Flink SQL or manage Kafka topics. * Schema Evolution: If you add a column to your StarRocks MV, BladePipe can often synchronize that change to Postgres automatically. * Visual Monitoring: You get a dashboard showing QPS, latency, and data consistency checks. Key Considerations * Primary Keys: For CDC to work effectively (handling Updates and Deletes), the StarRocks Materialized View should ideally have a Primary Key. * Refresh Frequency: Since the MV refreshes based on its own schedule (e.g., every 5 minutes), the "CDC" stream will be "bursty"—it will stay quiet until the MV refreshes, at which point all changes will flow to Postgres at once. * Alternative: If you prefer an open-source approach, Apache Flink with the

flink-connector-starrocks

is the standard alternative but requires more manual configuration. References • integrations/loading_tools/CloudCanal.md • loading/Kafka-connector-starrocks.md • loading/Flink_cdc_load.md

@Rocky can I provide bladepipe config through coding instead of UI. We have multiple environments and want to set it up once and then deploy without manual interventions.

Yes, you can absolutely automate BladePipe configurations without using the UI. BladePipe is designed for enterprise environments and provides an Open API specifically for programmatic management and CI/CD integration. To set up a "StarRocks → Postgres" CDC pipeline across multiple environments (Dev, Staging, Prod) without manual intervention, you should follow this automation workflow: 1. Infrastructure Deployment (The Worker) First, automate the deployment of the BladePipe Worker (the engine that moves the data). * Docker/K8s: Use the official BladePipe images. You can define your worker deployment in a

docker-compose.yml

or Kubernetes Manifest/Helm chart. * Configuration: The worker needs a configuration file (typically

system.properties

) or environment variables to connect back to the BladePipe Console. 2. Programmatic Configuration (The Open API) Once the worker is running, use the BladePipe Open API to configure the logic. The API allows you to perform all actions available in the UI. Key API Endpoints: * `/openapi/datasource/adddatasource`: To register your StarRocks and PostgreSQL instances. * `/openapi/datajob/createdatajob`: To define the CDC task, including table mapping and sync settings. * `/openapi/datajob/startdatajob`: To kick off the synchronization. 3. Authentication & Security The API uses an AccessKey/SecretKey mechanism. * You generate these in the BladePipe Console. * Every request must include a

Signature

(HmacSHA1) derived from the request parameters and your SecretKey to ensure security. 4. Recommended Automation Workflow For a multi-environment setup, you can treat your data pipelines as code: 1. Version Control: Store your pipeline definitions (JSON payloads for the API) in a Git repository. 2. CI/CD Pipeline: Use a tool like GitHub Actions, GitLab CI, or Jenkins: * Step 1: Deploy the Worker container to the target environment's VPC. * Step 2: Run a script (Python or Shell) that calls the BladePipe API to: * Check if the DataSource exists; if not, create it using environment-specific variables (DB hosts, credentials). * Create or update the DataJob using the versioned JSON definition. * Verify the job status is

RUNNING

. Example Payload Structure While you would send this via a POST request, the conceptual configuration looks like this:

json
{
  "jobName": "sr_to_pg_cdc_prod",
  "source": { "dataSourceId": "sr_prod_id", "dbName": "analytics" },
  "target": { "dataSourceId": "pg_prod_id", "dbName": "public" },
  "taskType": "INCREMENTAL",
  "fullData": true,
  "tableMappings": [
    { "sourceTable": "mv_orders_summary", "targetTable": "orders_summary" }
  ]
}

Summary of Tools * For Infrastructure: Terraform or Helm (to deploy the BladePipe containers). * For Configuration: Python/Curl + BladePipe Open API. * For Secret Management: Vault or AWS Secrets Manager (to handle the DB passwords and API keys). By using the API, you can ensure that your "StarRocks → Postgres" sync is identical across all environments, with only the connection strings changing between them.