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DocumentaçãoTencent Cloud WeDataOperation GuideData DevelopmentTask DevelopmentTask Node TypeDLCDLC SQL

DLC SQL

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Última atualização: 2026-02-27 17:11:16
Note:
Need to bind the DLC engine. Currently supports Spark SQL, Spark job, and Presto engines. For engine kernel details, see DLC Engine Kernel Version.
1. Currently, users need corresponding DLC computational resources and table privileges.
2. The corresponding database tables are already in DLC.

Feature Description

Submit a DLC SQL task execution to the WeData workflow scheduling platform. When selecting the DLC data source type, it provides "advanced setting" and supports configuration of Presto and SparkSQL parameters.

When using the Spark job engine, you can configure job resource specifications and parameters. Resource configuration must not exceed the limits of the computational resources themselves.

Configuration Description:
Configuration Item
Description
Resource Configuration Method
Divided into two methods: cluster default configuration and custom configuration
1. Use cluster default configuration
Use the current task computational resource cluster configuration
2. Customize
User customizes Executor and Driver configurations.
Executor resource
Enter the required resource count. 1 cu is roughly equivalent to 1 core CPU and 4 GB memory.
1. Small: A single calculation unit (1 cu)
2. Medium: Two calculation units (2 cu)
3. Large: Four calculation units (4 cu)
4. Xlarge: Eight calculation units (8 cu)
Number of Executors
An Executor is a compute node or compute instance responsible for executing tasks and handling computing work. The resources used by each Executor are the configured number of resources.
Driver resources
Enter the required number of Driver resources. 1 cu is roughly equivalent to 1 core CPU and 4 GB memory.
1. Small: A single calculation unit (1 cu)
2. Medium: Two calculation units (2 cu)
3. Large: Four calculation units (4 cu).
4. Xlarge: Eight calculation units (8 cu).

Sample Code

-- Create a user information table
create table if not exists wedata_demo_db.user_info (
user_id string COMMENT 'User ID'
user_name string COMMENT 'username'
user_age int COMMENT 'age'
city string COMMENT 'City'
) COMMENT 'User information table';

-- Insert data into the user information table
insert into wedata_demo_db.user_info values ('001', 'Zhang San', 28, 'beijing');
insert into wedata_demo_db.user_info values ('002', 'Li Si', 35, 'shanghai');
insert into wedata_demo_db.user_info values ('003', 'Wang Wu', 22, 'shenzhen');
insert into wedata_demo_db.user_info values ('004', 'Zhao Liu', 45, 'guangzhou');
insert into wedata_demo_db.user_info values ('005', 'Xiao Ming', 20, 'beijing');
insert into wedata_demo_db.user_info values ('006', 'Xiao Hong', 30, 'shanghai');
insert into wedata_demo_db.user_info values ('007', 'Xiao Gang', 25, 'shenzhen');
insert into wedata_demo_db.user_info values ('008', 'Xiao Li', 40, 'guangzhou');
insert into wedata_demo_db.user_info values ('009', 'Xiao Zhang', 23, 'beijing');
insert into wedata_demo_db.user_info values ('010', 'Xiao Wang', 50, 'shanghai');

select  * from  wedata_demo_db.user_info;
Note:
When using an Iceberg external table, SQL syntax differs from that of a native Iceberg table. For details, see DLC Iceberg External Table and Native Table Statement Differences.

Presto Engine Example Code

Applicable table types: native Iceberg tables, external Iceberg tables.
  CREATE TABLE `cpt_demo`.`dempts` (
    id bigint COMMENT 'id number',
    num int,
    eno float,
    dno double,
    cno decimal(9,3),
    flag boolean,
    data string,
    ts_year timestamp,
    date_month date,
    bno binary,
    point struct<x: double, y: double>,
    points array<struct<x: double, y: double>>,
    pointmaps map<struct<x: int>, struct<a: int>>
)
COMMENT 'table documentation'
PARTITIONED BY (bucket(16, id), years(ts_year), months(date_month), identity(bno), bucket(3, num), truncate(10, data));

SparkSQL Engine Example Code

Applicable table types: native Iceberg tables, external Iceberg tables.
CREATE TABLE `cpt_demo`.`dempts` (
    id bigint COMMENT 'id number',
    num int,
    eno float,
    dno double,
    cno decimal(9,3),
    flag boolean,
    data string,
    ts_year timestamp,
    date_month date,
    bno binary,
    point struct<x: double, y: double>,
    points array<struct<x: double, y: double>>,
    pointmaps map<struct<x: int>, struct<a: int>>
)
COMMENT 'table documentation'
PARTITIONED BY (bucket(16, id), years(ts_year), months(date_month), identity(bno), bucket(3, num), truncate(10, data));

SparkSQL Job Engine Example Code

Applicable table types: native Iceberg tables, external Iceberg tables.
CREATE TABLE `cpt_demo`.`dempts` (
    id bigint COMMENT 'id number',
    num int,
    eno float,
    dno double,
    cno decimal(9,3),
    flag boolean,
    data string,
    ts_year timestamp,
    date_month date,
    bno binary,
    point struct<x: double, y: double>,
    points array<struct<x: double, y: double>>,
    pointmaps map<struct<x: int>, struct<a: int>>
)
COMMENT 'table documentation'
PARTITIONED BY (id, ts_year, date_month);
Notes:
For more DLC grammar, please refer to Data Lake Compute (DLC) SQL Syntax Overview.

Execution Result and View Details

Operation completed. View runtime results and logs below.

For DLC SQL, DLC Spark, and DLC PySpark tasks, support clicking to jump to DLC or Spark at the top of logs to view more logs and execution results.
Product portal:
Click this run > corresponding SQL result > log > DLC: navigate to the DLC console view for more running logs, detailed information, and insights.
Click this run > corresponding SQL result > log > Spark: navigate to the corresponding execution page for more running logs and details.

Log entry:
Search for Execution Summary in logs to locate the execution summary of this run. It shows the DLC UI and Spark UI of this run. Press and hold the command key on the keyboard and click the UI in logs to navigate to the corresponding page for more detailed log information.

Anterior: DLC Spark StreamingPróximo: DLC PySpark

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