"clicks_shared" WHERE convert_timezone ( 'UTC', 'America/New_York', "datetime" ) BETWEEN ' 00:00:00.000' :: TIMESTAMP_TZ AND ' 23:59:59.999' :: TIMESTAMP_TZ ), "cte_customs" AS ( SELECT "last_search_id" AS "search_id", "origin_country", "visit_id", "custom_datas" : "c_context_website" :: string AS "website" FROM "COVEO". "searches_shared" WHERE convert_timezone ( 'UTC', 'America/New_York', "datetime" ) BETWEEN ' 00:00:00.000' :: TIMESTAMP_TZ AND ' 23:59:59.999' :: TIMESTAMP_TZ ), "cte_clicks" AS ( SELECT "search_id", "origin_country", "visit_id", "custom_datas" : "c_context_website" :: string AS "website" FROM "COVEO". The concrete describe infos we can get from the specific catalog object.WITH "cte_searches" AS ( SELECT "search_id", "origin_country", "visit_id", "custom_datas" : "c_context_website" :: string AS "website" FROM "COVEO". |function_description_item| function_description_value| |database_description_item| database_description_value| | catalog_description_item| catalog_description_value | Add SqlDescribeFunction & DescribeFunctionOperation Add SqlDescribeDatabase & DescribeDatabaseOperation Add SqlDescribeCatalog & DescribeCatalogOperation Some SqlNodes and Operations could be added or changed.
#ILIKE ANY SNOWFLAKE FULL#
In TableEnvironmentImpl we do filter for returned full results. We can use such as TableEnvironment.getCatalog(catalogName ).get().listDat abases(), TableEnvironment.getCatalog(catalogName).get().listFunctions(databaseName) for util to get result. No need to add new apis (there is a discussion about api changes, pls see the discuss thread). If the optional EXTENDED option is specified, the basic metadata information is returned along with the extended information. SHOW VIEWS database_name ] (LIKE | ILIKE) ] SHOW FUNCTIONS database_name ] (LIKE | ILIKE) ] SHOW COLUMNS ( FROM | IN ) database.] (LIKE | ILIKE) ] SHOW COLUMNS ( FROM | IN ) database.] LIKE ] SHOW TABLES database_name ] (LIKE | ILIKE) ] We focus on standard sql, but also consider absorbing the syntax of some mature engines to let users know that this is not standard, just for better use (e.g. Comparison with other popular enginesīecause each engine has its own personalized auxiliary sql statements features, here are some common operations listed as much as possible, and compare what other unrealized abilities of flink. įor example, many popular engines support show operation with filtering except flink, and support describe many objects(flink only supports describe table).Īnd these improved features are very useful for users and developers. These features have been a critical integration for Flink to be able to manage metadata like a classic RDBMS and make developers more easy to create or modify or list needed meta datas.īut these features are not very complete compared with other popular engines such as spark, hive, presto and commercial engines such as snowflake. Motivation:Ĭurrently flink sql auxiliary statements has supported some good features such as catalog/databases/table support. Please keep the discussion on the mailing list rather than commenting on the wiki (wiki discussions get unwieldy fast).
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