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In order to insert the rows, add a Table output step, double-click it, and select the connection to the book's database. As Target table type authors. Check the option Specify database fields. Select the Database fields tab and fill the grid as follows: Explore the authors table.

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When you have to generate a primary key based on the existing primary keys, unless the new primary key is simple to generate by adding one to the maximum, there is no direct way to do it in Kettle. One possible solution is the one shown in the recipe: Getting the last primary key in the table, combining it with your main stream, and using those two sources for generating the new primary keys. This is how it worked in this example.

First, by using a Table Input step, you found out the last primary key in the table. In fact, you got only the numeric part needed to build the new key.

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In this exercise, the value was 9. With the Join Rows cartesian product step, you added that value as a new column in your main stream. Taking that number as a starting point, you needed to build the new primary keys as A, A, and so on. Then it converts the result to a String giving it the format with the mask This led to the values , , and so on.

It concatenates the literal A with the previously calculated ID. Note that this approach works as long as you have a single user scenario. If you run multiple instances of the transformation they can select the same maximum value, and try to insert rows with the same PK leading to a primary key constraint violation. The key in this exercise is to get the last or maximum primary key in the table, join it to your main stream, and use that data to build the new key.

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After the join, the mechanism for building the final key would depend on your particular case. See also f Inserting new rows when a simple primary key has to be generated. If the primary key to be generated is simply a sequence, it is recommended to examine this recipe. If you face the second of the above situations, you can even use a Truncate table job entry. For more complex situations you should use the Delete step. Let's suppose the following situation: You have a database with outdoor products.

Each product belongs to a category: tools, tents, sleeping bags, and so on. Getting ready In order to follow the recipe, you should download the material for this chapter: a script for creating and loading the database, and an Excel file with the list of categories involved.

After creating the outdoor database and loading data by running the script provided, and before following the recipe you can explore the database. The value to which you will compare the price before deleting will be stored as a named parameter. Drag to the canvas an Excel Input step to read the Excel file with the list of categories. After that, add a Database lookup step. So far, the transformation looks like this: For higher volumes it's better to get the variable just once in a separate stream and join the two streams with a Join Rows cartesian product step.

Select the Database lookup step and do a preview. You should see this: 31 Working with Databases 7.

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Finally, add a Delete step. You will find it under the Output category of steps. Double-click the Delete step, select the outdoor connection, and fill in the key grid as follows: 9.

Explore the database. The Delete step allows you to delete rows in a table in a database based on certain conditions. In this case, you intended to delete rows from the table products where the price was less than or equal to 50, and the category was in a list of categories, so the Delete step is the right choice. This is how it works. Then, for each row in your stream, PDI binds the values of the row to the variables in the prepared statement. Let's see it by example. In the transformation you built a stream where each row had a single category and the value for the price.

Note that the conditions in the Delete step are based on fields in the same table. In this case, as you were provided with category descriptions and the products table does not have the descriptions but the ID for the categories, you had to use an extra step to get that ID: a Database lookup. Suppose that the first row in the Excel file had the value tents. Refer to this recipe if you need to understand how the Database lookup step works.

These are some use cases: f You receive a flat file and have to load the full content in a temporary table f You have to create and load a dimension table with data coming from another database You could write a CREATE TABLE statement from scratch and then create the transformation that loads the table, or you could do all that in an easier way from Spoon. In this case, suppose that you received a file with data about countries and the languages spoken in those countries.

You need to load the full content into a temporary table. The table doesn't exist and you have to create it based on the content of the file. Getting ready In order to follow the instructions, you will need the countries. Create a transformation and create a connection to the database where you will save the data. In order to read the countries. Fill the Fields grid as follows: The symbol preceding the field isofficial is optional.

By selecting Attribute as Element Kettle automatically understands that this is an attribute.

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From the Output category, drag and drop a Table Output step. Create a hop from the Get data from XML step to this new step. Double-click the Table Output step and select the connection you just created.

Click on the SQL button. After clicking on Execute, a window will show up telling that the statement has been executed, that is, the table has been created. All the information coming from the XML file is saved into the table just created. PDI allows you to create or alter tables in your databases depending on the tasks implemented in your transformations or jobs. To understand what this is about, let's explain the previous example. The insert is made based on the data coming to the Table Output and the data you put in the Table Output configuration window, for example the name of the table or the mapping of the fields.

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When you click on the SQL button in the Table Output setting window, this is what happens: Kettle builds the statements needed to execute that insert successfully. When the window with the generated statement appeared, you executed it. This causes the table to be created, so you could safely run the transformation and insert into the new table the data coming from the file to the step. The SQL button is present in several database-related steps.

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  6. In all cases its purpose is the same: Determine the statements to be executed in order to run the transformation successfully. Note that in this case the execution of the statement is not mandatory but recommended. You can execute the SQL as it is generated, you can modify it before executing it as you did in the recipe , or you can just ignore it. Sometimes the SQL generated includes dropping a column just because the column exists in the table but is not used in the transformation.

    In that case you shouldn't execute it. Read the generated statement carefully, before executing it. Finally, you must know that if you run the statement from outside Spoon, in order to see the changes inside the tool you either have to clear the cache by right-clicking the database connection and selecting the Clear DB Cache option, or restart Spoon.

    See also f 36 Creating or altering a database table from PDI runtime. Instead of doing these operations from Spoon during design time, you can do them at runtime. This recipe explains the details. Chapter 1 Creating or altering a database table from PDI runtime When you are developing with PDI, you know or have the means to find out if the tables you need exist or not, and if they have all the columns you will read or update. If they don't exist or don't meet your requirements, you can create or modify them, and then proceed.

    Assume the following scenarios: f You need to load some data into a temporary table. The table exists but you need to add some new columns to it before proceeding.