SQL Dialect

SQL Dialect Convertor - 图1tip

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.

SQL Dialect Convertor - 图2caution

  1. 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, etc. .

Deploy service

  1. Download latest Doris SQL Convertor

    Note:

    The SQL convertor tool is based on the open source SQLGlot. For more information about SQLGlot, please refer to SQLGlot official website

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

    sh bin/start.sh

    SQL Dialect Convertor - 图3tip

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

    2. The default startup port is 5001, and the specified port can be configured in conf/config.conf.

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

  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"

    SQL Dialect Convertor - 图4tip

    1. 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
  • hive
  • spark
  • postgres
  • clickhouse
  • oracle

example:

  • Presto
  1. mysql> CREATE TABLE  test_sqlconvert (
  2.          id int,
  3.          start_time DateTime,
  4.          value String,
  5.          arr_int ARRAY<Int>,
  6.          arr_str ARRAY<String>
  7.      ) ENGINE=OLAP
  8.      DUPLICATE KEY(`id`)
  9.      COMMENT 'OLAP'
  10.      DISTRIBUTED BY HASH(`id`) BUCKETS 1
  11.      PROPERTIES (
  12.      "replication_allocation" = "tag.location.default: 1"
  13.      );
  14. Query OK, 0 rows affected (0.01 sec)
  16. mysql> INSERT INTO test_sqlconvert values(1, '2024-05-20 13:14:52', '2024-01-14',[1, 2, 3, 3], ['Hello', 'World']);
  17. Query OK, 1 row affected (0.08 sec)
  19. mysql> set sql_dialect=presto;
  20. Query OK, 0 rows affected (0.00 sec)
  22. mysql> SELECT cast(start_time as varchar(20)) as col1,
  23.             array_distinct(arr_int) as col2,
  24.             FILTER(arr_str, x -> x LIKE '%World%') as col3,
  25.             to_date(value,'%Y-%m-%d') as col4,
  26.             YEAR(start_time) as col5,
  27.             date_add('month', 1, start_time) as col6,
  28.             REGEXP_EXTRACT_ALL(value, '-.') as col7,
  29.             JSON_EXTRACT('{"id": "33"}', '$.id')as col8,
  30.             element_at(arr_int, 1) as col9,
  31.             date_trunc('day',start_time) as col10
  32.          FROM test_sqlconvert
  33.          where date_trunc('day',start_time)= DATE'2024-05-20'     
  34.      order by id;
  35. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  36. | col1                | col2      | col3      | col4       | col5 | col6                | col7        | col8 | col9 | col10               |
  37. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  38. | 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 |
  39. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  40. 1 row in set (0.03 sec)

Clickhouse

  1. mysql> set sql_dialect=clickhouse;
  2. Query OK, 0 rows affected (0.00 sec)
  4. mysql> select  toString(start_time) as col1,
  5.              arrayCompact(arr_int) as col2,
  6.              arrayFilter(x -> x like '%World%',arr_str)as col3,
  7.              toDate(value) as col4,
  8.              toYear(start_time)as col5,
  9.              addMonths(start_time, 1)as col6,
  10.              extractAll(value, '-.')as col7,
  11.              JSONExtractString('{"id": "33"}' , 'id')as col8,
  12.              arrayElement(arr_int, 1) as col9,
  13.              date_trunc('day',start_time) as col10
  14.           FROM test_sqlconvert
  15.           where date_trunc('day',start_time)= '2024-05-20 00:00:00'     
  16.      order by id;
  17. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  18. | col1                | col2      | col3      | col4       | col5 | col6                | col7        | col8 | col9 | col10               |
  19. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  20. | 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 |
  21. +---------------------+-----------+-----------+------------+------+---------------------+-------------+------+------+---------------------+
  22. 1 row in set (0.02 sec)