Table Input

Description

The Table Input transform is one of the most frequently used transforms in Apache Hop. It reads data from a relational database using a connection and an SQL query, and introduces that data into a pipeline.

This transform supports parameterization using: - Apache Hop variables (e.g., ${customerId}) - Input fields from a previous transform (JDBC prepared statements using ? placeholders)

These features make Table Input ideal for dynamic and reusable data retrieval logic.

Supported Engines

Hop Engine

Supported

Spark

Supported

Flink

Supported

Dataflow

Supported

Getting Started: Generate a Basic SQL Query

You can auto-generate a query using the Get SQL select statement button.

This opens the database explorer, allowing you to select a table or view. Once selected, you can choose to generate:

A full column list: SELECT col_a, col_b, col_c FROM my_table;
A wildcard query: SELECT * FROM my_table;

Use Fields from a Previous Transform

To pass dynamic values into your SQL query at runtime, use the Insert data from transform option. This creates a JDBC Prepared Statement using ? placeholders.

Use ? in your SQL where values from the input transform should be inserted. Values are passed in the order of fields in the incoming stream. Use a Select Values transform to ensure the correct field order.

Prepared statements:

Improve security by preventing SQL injection
Cannot parameterize all parts of a SQL statement (e.g., IN (?) or table names)

You can also combine this with variable substitution.

Examples

Parameterized query using input fields:

Parameterized query using input fields

SELECT *
FROM public.tableinput
WHERE id > ?;

Replace variables in script: unchecked
Insert data from transform: Select the previous transform providing NameId and AddressId

Sample pipeline: tableinput-accept-input.hpl

Using a date range from a system transform:

Using a date range from a system transform

SELECT *
FROM public.tableinput
WHERE changed_date BETWEEN ? AND ?;

Use a Get System Info transform to generate the start and end dates
Insert those dates using Insert data from transform

Use Variables in Your SQL Query

If your query includes Hop variables, enable Replace variables in script. This performs a simple string replacement before the query is sent to the database.

SELECT *
FROM public.tableinput
WHERE id > ${PRM_ID};

${PRM_ID} is defined as a pipeline variable (e.g., via parameters or Set Variables transform)

Parameter definition in pipeline

This gives you full control over the query structure
Combine with ? placeholders if needed

Variable substitution happens before execution and does not protect against SQL injection.

Replace variables in script: checked
Insert data from transform: leave empty

Sample pipeline: tableinput-variables.hpl

Using Both Variables and Prepared Statements

You can combine both techniques in a single query:

Using Both Variables and Prepared Statements

SELECT *
FROM public.tableinput
where id > ${PRM_ID} AND lastdate > ?;

${startDate} is a pipeline variable
? is a parameter provided by the input stream

Best Practices and Pro Tips

Pro Tips

The Table input transform does not pass input data to the output, only fields inside the query are returned to the pipeline so all other variables and data will be lost. You can solve this by adding the variable as a field in the query or put a Get variables transform behind the table input.

If you are getting unexpected query results, try clearing the database cache. Click the broom icon or go to Tools > Clear DB Cache. After clearing, click OK, save your pipeline, and reopen it if needed.

A cartesian join transform will combine a different number of fields from multiple table inputs without requiring key join fields.

Using the "insert data from transform" drop down will block until the transform selected has completed.

For better performance with large datasets you can use indexed columns in WHERE clauses and avoid SELECT * and only retrieve needed fields.

Dynamic SQL with Metadata Injection

Table Input can be used in a metadata-driven pipeline. For example, create a template pipeline with a generic query:

SELECT *
FROM ${tableName}
WHERE ${condition}

Then use a Metadata Injection transform to inject actual values (e.g., from a CSV or database).

Steps:

Create a template pipeline with Table Input
Use ${tableName} and ${condition} as placeholders
In a separate pipeline, use Metadata Injection to inject values into the SQL field
Execute the injected pipeline

This allows you to create reusable and dynamic pipelines without editing SQL manually.

You can inject metadata into the following fields of the Table Input transform:

Connection
SQL
Replace variables in script?
Insert data from transform
Execute for each row?
Limit size

Options

Option Description

Option	Description
Transform name	Name of the transform instance.
Connection	Database connection to execute the query against.
SQL	SQL statement used to retrieve data. Use `Get SQL select statement` to auto-generate.
Replace variables in script?	Enable to substitute variables (e.g., `${param}`) in your SQL before execution.
Insert data from transform	Select a transform to use its fields as input for `?` parameters in a prepared statement.
Execute for each row?	Runs the SQL query once for each incoming row, using that row’s values as parameters. Only applies when “Insert data from transform” is enabled. Useful for row-specific lookups, but may be slower on large datasets.
Limit size	Number of rows to return. `0` means no limit.

Transform name

Name of the transform instance.

Connection

Database connection to execute the query against.

SQL

SQL statement used to retrieve data. Use Get SQL select statement to auto-generate.

Replace variables in script?

Enable to substitute variables (e.g., ${param}) in your SQL before execution.

Insert data from transform

Select a transform to use its fields as input for ? parameters in a prepared statement.

Execute for each row?

Runs the SQL query once for each incoming row, using that row’s values as parameters. Only applies when “Insert data from transform” is enabled. Useful for row-specific lookups, but may be slower on large datasets.

Limit size

Number of rows to return. 0 means no limit.