SQLExecutor

Executes SQL queries against a database once for each initiating feature that enters the transformer. Schema, the initiating features, and the results of the query may be output.

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Typical Uses

Extract database records as FME features according to a SQL statement
Execute a database join
Perform SQL database table operations (create, drop, modify, truncate)
Create or drop indexes or constraints before or after data loading

How does it work?

The SQLExecutor receives features via the Initiator port, and for every feature it receives, it executes a SQL query against an external database. The query results, as well as the initiating features, are output.

SQL statements are constructed within the transformer, using the appropriate syntax for the database in use. Spatial queries are supported if the database supports spatial predicates.

Schemas may also be produced if supported by the database. One schema feature will be output for every initiator feature.

Examples

Example: Using the SQLExecutor to do a SQL Join

In this example, we have a dataset of Fire Halls, which includes point geometry and some attributes. They have a street address, but not a complete postal address.

We will use a SQLExecutor to join a database table (using the Address field as a key), and retrieve the complete postal address.

Additionally, we will perform a second join from database table to database table, and retrieve the Civic Number for each hall.

The FireHalls datasets is connected to the Initiator port on a SQLExecutor. Each FireHalls feature will cause the SQL query to be executed, for that feature.

The first step is to connect to the external database. Select the type, in this case, PostGIS. Then configure (or select, if pre-saved) the database Connection.

Next we create a SQL statement to join both table. Note:

@value(Address) is providing a value from the Initiator feature - the address for a specific feature, which restricts the results to only matching records for that feature. Since the query is run every time a feature passes through, this repeats for every FireHalls feature.
The PostalAddress table is being joined to the feature using PostalAddress field.
The AddressPoints table (which contains the CivicNo attribute) is joined to the PostalAddress table (not the Initiator feature), using a different key - AddressId.

As the attributes will be hidden by default, we choose a set of attributes to expose on our output features.

The features with query results are output via the Results port, and have new attributes added by the SQL statement.

Usage Notes

If the database supports spatial predicates, spatial queries may be used.
The SchemaScanner may also be useful as an alternative for producing schemas.
For simple joins of one database table to features, the DatabaseJoiner may be more efficient (if the key field(s) are indexed). The DatabaseJoiner does not require knowledge of SQL.
The SQLExecutor can only be used on SQL-enabled data sources (in addition to the Initiator features). To use SQL with non-SQL data, consider the InlineQuerier.
If all the data to be queried already exists in a SQL-capable data source, it is generally most efficient to use the SQLCreator or SQLExecutor, as the queries and filtering of the data is executed directly by the database before it enters the FME environment.
Features that enter this transformer are not output until the SQL query completes. This behavior ensures that additional SQLExecutor transformers, if present, do not execute their queries until the previous query completes.

Choosing a Feature Joining Method

Many transformers can perform data joining based on matching attributes, expressions and/or geometry. When choosing one for a specific joining task, considerations include the complexity of the join, data format, indexing, conflict handling, and desired results. Some transformers use SQL syntax, and some access external databases directly. They may or may not support list attribute reading and creation.

Generally, choosing the one that is most specific to the task you need to accomplish will provide the optimal performance results. If there is more than one way to do it (which is frequently the case), time spent on performance testing alternate methods may be worthwhile. Performance may vary greatly depending on the existence of key indexes when reading external tables (as opposed to features already in the workspace).

Joining Transformers Comparison

Transformer	Match By	Uses SQL Statements	Can Create List	Input Type	Notable	Description
FeatureJoiner	Attributes	No	No	Features	Best performance for simple joins on attribute values Cannot create lists - represents multiple joins as multiple output features Cannot use constructed keys or expressions	Joins features by combining the attributes and/or geometry of features based on common key attribute values. Performs Left, Inner, and Full joins.
FeatureMerger	Attributes	No	Yes	Features	Can use expressions as keys (constructed keys) Can create lists – represents multiple joins as a list on a single feature Cannot match on geometry	Merges the attributes and/or geometry of one set of features onto another set of features, based on matching key attribute values and expressions.
ListBasedFeatureMerger	List Attribute to Single Attribute	No	Yes	Features	Only joining transformer to use all list attribute contents as keys (others may use one specific value in a list, but not the entire list)	Merges the attributes and/or geometry of one set of features onto another set of features, based on matching list attribute values with key attribute values and expressions.
InlineQuerier	SQL query	Yes	No	Features	Use SQL on non-SQL data Allows multiple join/SQL statements, and so can replace multiple transformers with one	Creates SQLite database tables from incoming features, executes SQL queries against them, and outputs the results as features.
DatabaseQuerier	SQL or Cypher query	Yes	No	External DB	Combines capabilities of SQLCreator and SQLExecutor Read an external DB table mid-translation	Executes queries against a database, producing query results and/or schema.
SQLCreator	SQL query	Yes	No	External DB	Best if all features already in SQL-capable source Read an external DB table mid-translation	Generates FME features and/or schemas from the results of a SQL query executed against a database. One FME feature is created for each row of the results of the SQL query.
SQLExecutor	SQL query	Yes	No	External DB	Best for complex joins between features and SQL-capable source Read an external DB table mid-translation	Executes SQL queries against a database once for each initiating feature that enters the transformer. Schema, the initiating features, and the results of the query may be output.
DatabaseJoiner	Attributes	No	Yes	External DB and Features	Best for simple joins between features and a database table Very efficient if keys indexed, and caches data locally Read an external DB table mid-translation Non-blocking	Joins attributes from an external table to features already in a workspace, based on a common key or keys.
Matcher	Geometry and/or Attributes	No	Yes	Features	The only one of these methods that can use geometry as key for matching Can look for attribute differences Uses geometry, not spatial relationships	Detects features that are matches of each other. Features are declared to match when they have matching geometry, matching attribute values, or both. A list of attributes which must differ between the features may also be specified.

Configuration

Input Ports

Output Ports

Parameters

Database

Format	Select a database format.
Dataset/Connection	According to chosen format, select a dataset or use/configure a database connection.
Parameters	Format-specific parameters

General

SQL Statement	Specify the SQL query using the text editor. Multiple SQL commands can be delimited by a character specified using the keyword FME_SQL_DELIMITER, embedded at the very beginning of the SQL statement. The single character immediately following this keyword will be used to split the SQL which will then be sent to the database for execution. (Note: Include a space before the character.) An individual statement may be preceded with a single hyphen, indicating that errors should be ignored. Note Some database formats support comments with /* ... / or double hyphens (--). The following example contains two SQL commands where errors from the first command will be ignored: FME_SQL_DELIMITER ; -SELECT FROM TABLEA; SELECT * FROM TABLEB;
Attributes to Expose	Enter the names of attributes to expose on the features created by the query. The attributes will be output in the same sequence as specified in the list. Note By default, the attributes of the resulting features are hidden. Specify which attributes to expose by entering the attribute names. Click the browse button next to the Attributes to Expose parameter. You can also use a SQL statement to populate the list by pressing “Populate from SQL Query…” and entering a SQL query. The columns from the first matching feature will be used to populate the attributes list.
Combine Attributes	Result Attributes Only: The result feature attributes consist solely of the query results. Keep Initiator Attributes if Conflict: The result feature attributes are a combination of both the query results and the initiator feature's attributes. If there is a conflict, attribute values are taken from the initiator feature. Keep Result Attributes if Conflict: The result feature attributes are a combination of both the query results and initiator feature's attributes. If there is a conflict, attribute values are taken from the query results.
Ignore Nulls	Enabled when Combine Attributes is set to either Keep Initiator Attributes if Conflict or Keep Result Attributes if Conflict. If Yes, where there are attributes with the same name on both Initiator and Result feature, if one has a value of <null> then the other attribute value will be used. If No, the Combine Attributes selection will be strictly observed, including keeping null values.
Combine Geometry	Result Geometry Only: The geometries of result features consist only of those produced from the SQL query. Initiator Geometry Only: The geometries of result features consist only of those present in the initiator features. Aggregate Initiator and Result Geometry: Result features are populated with geometries from both the initiator features and the SQL query.

Schema/Data Features

Features to Read

Select the type of features to read and output:

Schema and Data Features	Read schema and query results.
Data Features	Read query results only.
Schema Features	Read schema only.

Editing Transformer Parameters

Transformer parameters can be set by directly entering values, using expressions, or referencing other elements in the workspace such as attribute values or user parameters. Various editors and context menus are available to assist. To see what is available, click beside the applicable parameter.

How to Set Parameter Values

Defining Values

There are several ways to define a value for use in a Transformer. The simplest is to simply type in a value or string, which can include functions of various types such as attribute references, math and string functions, and workspace parameters.

Using the Text Editor

The Text Editor provides a convenient way to construct text strings (including regular expressions) from various data sources, such as attributes, parameters, and constants, where the result is used directly inside a parameter.

Text Editor

Using the Arithmetic Editor

The Arithmetic Editor provides a convenient way to construct math expressions from various data sources, such as attributes, parameters, and feature functions, where the result is used directly inside a parameter.

Arithmetic Editor

Conditional Values

Set values depending on one or more test conditions that either pass or fail.

Parameter Condition Definition Dialog

Content

Expressions and strings can include a number of functions, characters, parameters, and more.

When setting values - whether entered directly in a parameter or constructed using one of the editors - strings and expressions containing String, Math, Date/Time or FME Feature Functions will have those functions evaluated. Therefore, the names of these functions (in the form @<function_name>) should not be used as literal string values.

Content Types

String Functions	These functions manipulate and format strings.
Special Characters	A set of control characters is available in the Text Editor.
Math Functions	Math functions are available in both editors.
Date/Time Functions	Date and time functions are available in the Text Editor.
Math Operators	These operators are available in the Arithmetic Editor.
FME Feature Functions	These return primarily feature-specific values.
FME Parameters	FME and workspace-specific parameters may be used.
Creating and Modifying User Parameters	Create your own editable parameters.

Dialog Options - Tables

Table Tools

Transformers with table-style parameters have additional tools for populating and manipulating values.

Row Reordering

Enabled once you have clicked on a row item. Choices include:

Add a row
Remove a row
Move current row up one
Move current row down one
Move current row to top
Move current row to bottom

Cut, Copy, and Paste

Enabled once you have clicked on a row item. Choices include:

Cut a row - delete and copy to clipboard
Copy a row to the clipboard
Paste a row from the clipboard

Cut, copy, and paste may be used within a transformer, or between transformers.

Filter

Start typing a string, and the matrix will only display rows matching those characters. Searches all columns. This only affects the display of attributes within the transformer - it does not alter which attributes are output.

Import

Import populates the table with a set of new attributes read from a dataset. Specific application varies between transformers.

Reset/Refresh

Generally resets the table to its initial state, and may provide additional options to remove invalid entries. Behavior varies between transformers.

Note: Not all tools are available in all transformers.

For more information, see Transformer Parameter Menu Options.

Reference

Processing Behavior	Not applicable
Feature Holding	Not applicable
Dependencies	Format-dependent - may require third-party drivers for some formats
Aliases
History

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Examples may contain information licensed under the Open Government Licence – Vancouver, Open Government Licence - British Columbia, and/or Open Government Licence – Canada.