When would you choose to use SQLExecutor in an FME workflow?

When would you choose to use SQLExecutor in an FME workflow?

• A. If you have a list of IDs and you wanted to run an SQL statement for each one.
• B. If you need a single static SQL against a local file.
• C. If you want to create SQL statements dynamically from a dataset.
• D. If your input is GeoJSON.

Answer: (A)

SQLExecutor is used when you want to run an SQL statement for every input feature, typically parameterizing the query with attributes from that feature. This fits the scenario where you have a list of IDs and need to execute a query for each one, such as looking up related records or pulling per-ID data from a database. The transformer takes each feature, fills in the ID (or other attributes) into the SQL, runs the statement, and returns the results to be joined back or used downstream. A static, one-time SQL against a local file isn’t its normal pattern because SQLExecutor operates against a database connection and is designed for per-feature queries rather than a single fixed query on a local file. Creating SQL statements dynamically from a dataset is possible, but the common, straightforward use is per-feature execution driven by the attributes you provide. GeoJSON input by itself isn’t a database query target unless you’re using a database read/write step with a prepared SQL for each feature.

SQLExecutor is used when you want to run an SQL statement for every input feature, typically parameterizing the query with attributes from that feature. This fits the scenario where you have a list of IDs and need to execute a query for each one, such as looking up related records or pulling per-ID data from a database. The transformer takes each feature, fills in the ID (or other attributes) into the SQL, runs the statement, and returns the results to be joined back or used downstream.

A static, one-time SQL against a local file isn’t its normal pattern because SQLExecutor operates against a database connection and is designed for per-feature queries rather than a single fixed query on a local file. Creating SQL statements dynamically from a dataset is possible, but the common, straightforward use is per-feature execution driven by the attributes you provide. GeoJSON input by itself isn’t a database query target unless you’re using a database read/write step with a prepared SQL for each feature.