Search results
18 paź 2023 · The "Error: ambiguous column name: Name" in SQL happens when the same column name is found in multiple tables or parts of a query, making it unclear which one to use. This error is common in various situations, such as joining tables, using subqueries, and creating views.
This is especially useful when you use JOIN where id field most probably would be ambiguous in different tables. The downside of this approach is that you have to enumerate all the necessary fields in the query but in some cases this might be particularly useful.
5 paź 2020 · One of the simplest ways to solve an “ambiguous name column” error — without changing column name — is to give the tables you want to join an alias. This sends a clear information to the SQL Machine the columns are different.
The "Ambiguous Name Column" error in SQL occurs when joining tables that contain columns with the same name, leading to confusion for the SQL engine as to which column is being referenced. The article suggests using aliases for the tables as a straightforward solution to this problem.
26 gru 2023 · To fix an ambiguously defined column, you need to specify the table name before the column name. You can do this in the following ways: Use a table alias. Use the table name in the subquery. Use the `AS` keyword to rename the column. Here are some examples of how to fix an ambiguously defined column:
27 sty 2022 · Please alias the Datasets with different names via `Dataset.as` before joining them, and specify the column using qualified name, e.g. `df.as("a").join(df.as("b"), $"a.id" > $"b.id")`. You can also set spark.sql.analyzer.failAmbiguousSelfJoin to false to disable this check.
26 gru 2023 · A column ambiguously defined error occurs when a SQL statement references a column that does not exist in the specified table. This can happen for a variety of reasons, such as: The column name is misspelled. The column is not included in the FROM clause of the SELECT statement.
