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Mysql create view with primary key9/12/2023 ![]() The WATERMARK clause defines the event time attributes of a table and takes the form WATERMARK FOR rowtime_column_name AS watermark_strategy_expression. MyTable(`user_id` BIGINT, `price` DOUBLE, `quantity` DOUBLE) MyTable(`user_id` BIGINT, `price` DOUBLE, `quantity` DOUBLE, `cost` DOUBLE) Thus, source-to-query schema (for SELECT)Īnd query-to-sink (for INSERT INTO) schema differ: Therefore, a computedĬolumn cannot be the target of an INSERT INTO statement. Similar to virtual metadata columns, computed columns are excluded from persisting. If the original field is not TIMESTAMP(3) type or is nested in a JSON string. For example, the computed column can be used The expression cannotĬomputed columns are commonly used in Flink for defining time attributesĬan be defined easily via proc AS PROCTIME() using the system’s PROCTIME() function.Ĭan be pre-processed before the WATERMARK declaration. ![]() The expression may contain any combination of columns, constants, or functions. The following statement creates a table with only regular columns:ĬREATE TABLE MyTable ( ` user_id ` BIGINT, ` price ` DOUBLE, ` quantity ` DOUBLE, ` cost ` AS price * quantity - evaluate expression and supply the result to queries Other kinds of columns can be declared between physical columns but will not Connectors and formats use these columns (in the defined order) Thus, physical columns represent the payload that is read fromĪnd written to an external system. They define the names, the types, and the Physical columns are regular columns known from databases. If a table with the same name already exists The statement above creates a table with the given name. WATERMARK FOR rowtime_column_name AS watermark_strategy_expression ) NOT ENFORCEDĬolumn_name column_type METADATA Ĭolumn_name AS computed_column_expression ![]() | AS select_query ]Ĭolumn_name column_type Hadoop MapReduce compatibility with FlinkĬREATE TABLE table_name.Conversions between Table and DataStream.Conversions between PyFlink Table and Pandas DataFrame.
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