![]() ![]() For example, you can convert both CHAR and VARCHAR Amazon Redshift scalar types to SUPER. THEN c_orders.o_orderstatus::VARCHAR <= 'P'ĭynamic typing doesn't exclude from comparisons of types that are minimally comparable. WHERE CASE WHEN JSON_TYPEOF(c_orders.o_orderstatus) = 'string' The following example writes the previous query with static typing: Other functions return null when mistyped arguments The dynamic type of c_orders.o_orderstatus is any other data type except being a ![]() Path expression is a string, the comparison with the string ‘P’ is meaningful. In this example, when the dynamic type of the When using an SQL operator or function with dot and bracket expressions that haveĭynamic types, Amazon Redshift produces results similar to using standard SQL operator orįunction with the respective static types. These are the dynamic types of the expression. Of customer_orders_lineitem may result into an integer. ForĮxample, evaluating c_orders.o_orderstatus on the first record When the queryĮvaluates the data, c_orders.o_orderstatus turns out to be a specific type. The static type of c_orders.o_orderstatus is a SUPER data type.Ĭonventionally, a type is implicitly a static type in SQL.Īmazon Redshift uses dynamic typing to the processing of schemaless data. You can only determine thatĬ_orders.o_orderstatus is a SUPER data type, which can be an Amazon Redshift scalar, an array, Without using dynamic typing, you can't determine whether c_orders.o_orderstatus In all other cases, the equality sign evaluates to false, including the cases where the arguments of the equality are different types. The equality sign in this query evaluates to true when c_orders.o_orderstatus is the string ‘P’. For information about type compatibility and conversion, see Type compatibility and conversion. The following example uses a SELECT statement that requires no explicitĬasting of the dot and bracket expressions to the usual Amazon Redshift types. Dynamic typing is most useful in joins and GROUPīY clauses. Typing uses the results of navigating into SUPER data columns without having toĮxplicitly cast them into Amazon Redshift types. Without the need to declare the data types before you use them in your query. ![]() Amazon Redshift uses dynamic typing to process schemaless SUPER data Dynamic typingĭynamic typing doesn't require explicit casting of data that is extracted from theĭot and bracket paths. For examples that show how to query structured data, with PIVOT and UNPIVOT, Unpivoting in different query levels and the inner unpivoting references the outer one.Īmazon Redshift doesn't support this type of multiple unpivoting.įor more information about the FROM clause, see FROM clause. Specifically, suppose that you have a case where there are multiple examples of When you use object unpivoting, Amazon Redshift doesn't support correlated unpivoting. SELECT attr as attribute_name, val as object_valueįROM customer_orders_lineitem c, c.c_orders AS o, UNPIVOT o AS val AT attr Replace the IAM role with your own credentials. To ingest data in the customer_orders_lineitem table, run the following command. The following example assumes that the c_orders SUPER data column is anĪrray with a structure and an attribute is named o_orderkey. Structures using the dot notation and arrays using the bracket notation. That combine the use of the SUPER data type with path and array navigation, unnesting,Īmazon Redshift uses PartiQL to enable navigation into arrays and structures using the īracket and dot notation respectively. Following, you can find descriptions of the different query patterns It also enables the FROM clause items to iterate over arrays and use for PartiQL syntax uses dotted notation and array subscript for path navigation when accessing This approach enables intuitive filtering, joining,Īnd aggregation on the combination of structured, semistructured, and nested datasets. Amazon Redshift uses the PartiQL language to offer SQL-compatible access to relational, ![]()
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