SQL Converter for Apache Doris

SQL Dialect

tip

Starting from version 2.1, Doris can support multiple SQL dialects, such as Presto, Trino, Hive, PostgreSQL, Spark, Oracle, Clickhouse, and more. Through this feature, users can directly use the corresponding SQL dialect to query data in Doris, which facilitates users to smoothly migrate their original business to Doris.

caution

This function is currently an experimental function. If you encounter any problems during use, you are welcome to provide feedback through the mail group, GitHub issue

Deploy service

1. Download latest SQL Converter for Apache Doris

:::info NOTE
The SQL dialect conversion tool is based on the open-source [SQLGlot](https://github.com/tobymao/sqlglot) and has been further developed by SelectDB. For more information about SQLGlot, please refer to the [SQLGlot official website](https://sqlglot.com/sqlglot.html).

The SQL Convertor is not maintained or endorsed by Apache Doris; these efforts are supervised by Committers and the Doris PMC. The use of these resources and services is entirely at your own discretion, and the community does not verify the licensing or validity of these tools. :::

2. On any FE node, start the service through the following command:

   # Configuring the service port
   vim apiserver/conf/config.conf
   # Start the SQL Converter for Apache Doris conversion service
   sh apiserver/bin/start.sh
   # If you need a front-end interface, you can configure the corresponding port in the webserver and start it. If you don't need a front-end, you can ignore the following operations.
   vim webserver/conf/config.conf
   # Start the front-end interface
   sh webserver/bin/start.sh

:::tip
  1. This service is a stateless service and can be started and stopped at any time.

  2. Configure port in `apiserver/conf/config.conf` to specify any available port, and configure workers to specify the number of threads to start. In concurrent scenarios, you can adjust as needed, the default is 1.

  3. It is recommended to start a separate service on each FE node.

4. If you need to start the front-end interface, you can configure the SQL Converter for Apache Doris conversion service address in `webserver/conf/config`.conf. The default is `API_HOST=http://127.0.0.1:5001`
:::

3. Start the Doris cluster (version 2.1 or higher)

4. Set the URL of the SQL Dialect Conversion Service with the following command in Doris:

   MySQL> set global sql_converter_service_url = "http://127.0.0.1:5001/api/v1/convert"

   • 127.0.0.1:5001 is the deployment node IP and port of the SQL dialect conversion service.

Use SQL dialect

Currently supported dialect types include:

• presto
• trino
• clickhouse
• hive
• spark
• postgres
• oracle

example:

• Presto

mysql> CREATE TABLE  test_sqlconvert (
         id int,
         start_time DateTime,
         value String,
         arr_int ARRAY<Int>,
         arr_str ARRAY<String>
     ) ENGINE=OLAP
     DUPLICATE KEY(`id`)
     COMMENT 'OLAP'
     DISTRIBUTED BY HASH(`id`) BUCKETS 1
     PROPERTIES (
     "replication_allocation" = "tag.location.default: 1"
     );
Query OK, 0 rows affected (0.01 sec)

mysql> INSERT INTO test_sqlconvert values(1, '2024-05-20 13:14:52', '2024-01-14',[1, 2, 3, 3], ['Hello', 'World']);
Query OK, 1 row affected (0.08 sec)

mysql> set sql_dialect=presto;
Query OK, 0 rows affected (0.00 sec)

mysql> SELECT cast(start_time as varchar(20)) as col1,
            array_distinct(arr_int) as col2,
            FILTER(arr_str, x -> x LIKE '%World%') as col3,
            to_date(value,'%Y-%m-%d') as col4,
            YEAR(start_time) as col5,
            date_add('month', 1, start_time) as col6,
            REGEXP_EXTRACT_ALL(value, '-.') as col7,
            JSON_EXTRACT('{"id": "33"}', '$.id')as col8,
            element_at(arr_int, 1) as col9,
            date_trunc('day',start_time) as col10
         FROM test_sqlconvert
         where date_trunc('day',start_time)= DATE'2024-05-20'     
     order by id;
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
| col1                | col2      | col3      | col4       | col5 | col6                | col7        | col8 | col9 | col10               |
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
| 2024-05-20 13:14:52 | [1, 2, 3] | ["World"] | 2024-01-14 | 2024 | 2024-06-20 13:14:52 | ['-0','-1'] | "33" |    1 | 2024-05-20 00:00:00 |
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
1 row in set (0.03 sec)

Clickhouse

mysql> set sql_dialect=clickhouse;
Query OK, 0 rows affected (0.00 sec)

mysql> select  toString(start_time) as col1,
             arrayCompact(arr_int) as col2,
             arrayFilter(x -> x like '%World%',arr_str)as col3,
             toDate(value) as col4,
             toYear(start_time)as col5,
             addMonths(start_time, 1)as col6,
             extractAll(value, '-.')as col7,
             JSONExtractString('{"id": "33"}' , 'id')as col8,
             arrayElement(arr_int, 1) as col9,
             date_trunc('day',start_time) as col10
          FROM test_sqlconvert
          where date_trunc('day',start_time)= '2024-05-20 00:00:00'     
     order by id;
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
| col1                | col2      | col3      | col4       | col5 | col6                | col7        | col8 | col9 | col10               |
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
| 2024-05-20 13:14:52 | [1, 2, 3] | ["World"] | 2024-01-14 | 2024 | 2024-06-20 13:14:52 | ['-0','-1'] | "33" |    1 | 2024-05-20 00:00:00 |
+---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
1 row in set (0.02 sec)