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Dataphin:Create an express synchronization pipeline

Last Updated:Nov 03, 2026

Express synchronization pipeline tasks use the Apache Arrow columnar in-memory format for end-to-end data transport. This approach boosts synchronization throughput in large-scale data synchronization scenarios by leveraging vectorized batch reads and writes, zero-copy mapping, and the Arrow Flight SQL protocol. This topic describes how to create an express synchronization pipeline task.

Prerequisites

Configure the required source and destination data sources before you begin. This allows you to select them when configuring the input and output components. Express synchronization pipeline tasks support the following data sources:

Procedure

Step 1: Create an express synchronization pipeline

  1. In the top navigation bar of the Dataphin homepage, choose Development > Data Integration.

  2. In the top navigation bar of the integration page, select a project. In Dev-Prod mode, you must also select an environment.

  3. In the offline integration list, click the New icon, select Express Synchronization, and open the Create Express Synchronization Pipeline dialog box.

  4. In the Create Express Synchronization Pipeline dialog box, configure the pipeline parameters.

    Parameter

    Description

    Pipeline name

    Enter a name for the pipeline. The name can be up to 64 characters long and cannot contain the following special characters: vertical bar (|), forward slash (/), backslash (\), colon (:), question mark (?), angle brackets (<>), asterisk (*), or single quotation mark (').

    Scheduling type

    Select a scheduling type for the pipeline:

    • Periodic node: A task that runs at regular intervals.

    • Manual node: A task that has no dependencies and must be triggered manually.

    Description

    Enter a description for the express synchronization pipeline, up to 1,000 characters.

    Select directory

    Select the directory to store the task.

    If you have not created a directory, you can create one:

    1. Above the compute task list on the left, click the new folder icon to open the New Folder dialog box.

    2. In the New Folder dialog box, enter a folder Name and select its location under Select Directory.

    3. Click OK.

  5. Click OK to create the express synchronization pipeline.

Step 2: Develop the pipeline script

The component library for express synchronization pipeline tasks supports only input and output components. The configuration for these components is the same as for those in offline integration tasks. You can also switch to script mode.

The input and output components for express synchronization pipeline tasks are divided into two categories based on whether the data source natively supports Arrow:

  • Apache Arrow acceleration: The data source provides a native columnar read/write path, allowing data to be transmitted in Arrow format without row-to-column conversion.

  • Apache Arrow adaptation: The data source does not provide a native columnar path, requiring conversion between the row format and the Arrow columnar format during read and write operations.

The following input components are supported:

Input component

Arrow capability

Arrow read/write mode

Description

Configure MySQL input component

Apache Arrow adaptation

JDBC to Arrow columnar

Reads data via JDBC and converts the result set to the Arrow columnar in-memory format in batches.

Configure Oracle input component

Apache Arrow adaptation

JDBC to Arrow columnar

Reads data via JDBC and converts the result set to the Arrow columnar in-memory format in batches.

Configure PostgreSQL input component

Apache Arrow adaptation

JDBC to Arrow columnar

Reads data via JDBC and converts the result set to the Arrow columnar in-memory format in batches.

Configure Microsoft SQL Server input component

Apache Arrow adaptation

JDBC to Arrow columnar

Reads data via JDBC and converts the result set to the Arrow columnar in-memory format in batches.

Configure MaxCompute input component

Apache Arrow acceleration

Storage API (Arrow)

Reads data in native Arrow format at high throughput by using the MaxCompute Storage API, which eliminates the need for conversion.

Configure Hive input component

Apache Arrow acceleration

Zero-copy columnar file read

Directly maps ORC, Parquet, or text files to the Arrow memory layout to enable zero-copy reads without deserialization.

Configure HDFS input component

Apache Arrow acceleration

Zero-copy columnar file read

Maps ORC, Parquet, or text files to the Arrow columnar layout for zero-copy vectorized scanning.

Configure Doris input component

Apache Arrow acceleration

Arrow Flight SQL

Reads data as a columnar stream by using the Arrow Flight SQL high-speed port of the Doris FE (default: 8070).

Configure SelectDB input component

Apache Arrow acceleration

Arrow Flight SQL

Reads data as a columnar stream by using the Arrow Flight SQL high-speed port of the SelectDB FE (default: 8070). You must specify a warehouse (compute cluster).

Configure Hologres input component

Apache Arrow acceleration

COPY protocol (Arrow)

Reads data in native Arrow columnar format through the PostgreSQL COPY protocol, bypassing row-by-row result set parsing.

Configure ClickHouse input component

Apache Arrow acceleration

Native ArrowStream

Reads data as a columnar stream in ArrowStream format by using the ClickHouse native client. The server returns Arrow columnar data directly, which eliminates row-by-row parsing.

The following output components are supported:

Output component

Arrow capability

Arrow read/write mode

Description

Configure MySQL output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure Oracle output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure PostgreSQL output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure Microsoft SQL Server output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure MaxCompute output component

Apache Arrow acceleration

Storage API (Arrow)

Provides high-throughput writes in native Arrow format through the MaxCompute Storage API, eliminating the need for conversion.

Configure Hive output component

Apache Arrow acceleration

Zero-copy columnar file write

Writes ORC, Parquet, or text files directly from the Arrow memory layout, which avoids serialization overhead.

For Paimon tables, Arrow columnar data must be converted to row format before writing. This feature is supported only for Hive data sources on Amazon EMR.

Configure HDFS output component

Apache Arrow acceleration

Zero-copy columnar file write

Writes ORC, Parquet, or text files directly from the Arrow memory layout, which avoids serialization overhead.

Configure Doris output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure SelectDB output component

Apache Arrow adaptation

Arrow columnar to JDBC

Converts Arrow columnar data to a row-based format before writing it via JDBC.

Configure Hologres output component

Apache Arrow acceleration

COPY protocol (Arrow)

Performs batch writes in native Arrow columnar format through the COPY protocol, avoiding row-to-column conversion.

Configure ClickHouse output component

Apache Arrow acceleration

Native ArrowStream

Writes data in batches in ArrowStream format by using the ClickHouse native client, which avoids row-to-column conversion for Arrow columnar data.

Step 3: Configure pipeline scheduling

Click Scheduling Configuration on the toolbar to configure the basic information, scheduling configuration, scheduling dependencies, scheduling parameters, runtime configuration, and resource configuration for the express synchronization pipeline task. For more information, see:

Step 4: Save and submit the task

  1. Click the save icon above the canvas to save the express synchronization pipeline task.

  2. Click the submit icon above the canvas. In the Submission Comments dialog box, enter your comments and click OK and Submit.

Next steps

  • If your development mode is Dev-Prod mode, you must publish a task before it can be scheduled in the production environment.

  • If your development mode is Basic mode, a successfully submitted task becomes eligible for scheduling in the production environment, and you can then go to the O&M Center to view the task.