Pentaho Data Integration Cookbook - Second Edition - Second Edition

Through the chapter you will use a couple of sample databases. Those databases can be created and loaded by running the scripts available at the book's website. The scripts are ready to run under MySQL.

For more information about the structure of the sample databases and the meaning of the tables and fields, please refer to Appendix A, Data Structures. Feel free to adapt the recipes to different databases. You could try some well-known databases; for example, Foodmart (available as part of the Mondrian distribution at http://sourceforge.net/projects/mondrian/) or the MySQL sample databases (available at http://dev.mysql.com/doc/index-other.html).

As part of the sample databases used in this chapter you will use the Pentaho BI platform Demo databases. The Pentaho BI Platform Demo is a preconfigured installation that lets you explore the capabilities of the Pentaho platform. It relies on the following databases:

By default, all those databases are stored in Hypersonic (HSQLDB). The script for creating the databases in HSQLDB can be found at http://sourceforge.net/projects/pentaho/files. Under Business Intelligence Server | 1.7.1-stable look for pentaho_sample_data-1.7.1.zip. While there are newer versions of the actual Business Intelligence Server, they all use the same sample dataset.

These databases can be stored in other DBMSs as well. Scripts for creating and loading these databases in other popular DBMSs for example, MySQL or Oracle can be found in Prashant Raju's blog, at http://www.prashantraju.com/projects/pentaho.

Beside the scripts you will find instructions for creating and loading the databases.

In order to create the connection, you will need to know the connection settings. At least you will need the following:

It's recommended that you also have access to the database at the moment of creating a connection.

Open Spoon and create a new transformation.

A database connection is the definition that allows you to access a database from Kettle. With the data you provide, Kettle can instantiate real database connections and perform the different operations related to databases. Once you define a database connection, you will be able to access that database and execute arbitrary SQL statements: create schema objects like tables, execute SELECT statements, modify rows, and so on.

In this recipe you created the connection from the Database connections tree. You may also create a connection by pressing the New... button in the Configuration window of any database-related step in a transformation or job entry in a job. Alternatively, there is also a wizard accessible from the Tools menu or by pressing the F3 key.

Whichever method you choose, a Settings window, like the one you saw in the recipe, shows up, allowing you to define the connection. This task includes the following:

A database connection can only be created with a transformation or an opened job. Therefore, in the recipe you were asked to create a transformation. The same could have been achieved by creating a job instead.

The recipe showed the simplest way to create a database connection. However, there is more to know about creating database connections.

Avoiding creating the same database connection over and over again

If you intend to use the same database in more than one transformation and/or job, it's recommended that you share the connection. You do this by right-clicking on the database connection under the Database connections tree and clicking on Share. This way the database connection will be available to be used in all transformations and jobs. Shared database connections are recognized because they appear in bold. As an example, take a look at the following sample screenshot:

The databases books and sampledata are shared; the others are not.

The information about shared connections is saved in a file named shared.xml located in the Kettle home directory.

No matter what Kettle storage method is used (repository or files), you can share connections. If you are working with the file method, namely ktr and kjb files, the information about shared connections are not only saved in the shared.xml file, but also saved as part of the transformation or job files even if they don't use the connections.

Note

You can avoid saving all the connection data as part of your transformations and jobs by selecting the option Only save used connections to XML? in the Kettle options window under Tools | Options.

To follow these instructions, you need to have access to any DBMS. Many of the recipes in this chapter will be connecting to a MySQL instance. It is recommended that to fully take advantage of the book's code, (which can be found on the book's website) you have access to a MySQL instance.

The Table Input step you used in the recipe is the main Kettle step to get data from a database. When you run or preview the transformation, Kettle executes the SQL and pushes the rows of data coming from the database into the output stream of the step. Each column of the SQL statement leads to a PDI field and each row generated by the execution of the statement becomes a row in the PDI dataset.

Once you get the data from the database, it will be available for any kind of manipulation inside the transformation.

In order to save time, or in case you are not sure of the name of the tables or columns in the database, instead of typing the SQL statement, click on the Get SQL select statement... button. This will bring the Database Explorer window. This window allows you to explore the selected database. By expanding the database tree and selecting the table that interests you, you will be able to explore that table through the different options available under the Actions menu.

Double-clicking on the name of the table will generate a SELECT statement to query that table. You will have the chance to include all the field names in the statement, or simply generate a SELECT * statement. After bringing the SQL to the Table Input configuration window, you will be able to modify it according to your needs.

Make sure you have access to the sampledata database.

Perform the following steps to connect to a database with parameters:

When you need to execute a SELECT statement with parameters, the first thing you have to do is to build a stream that provides the parameter values needed by the statement. The stream can be made of just one step; for example, a data grid with fixed values, or a stream made up of several steps. The important thing is that the last step delivers the proper values to the Table Input step.

Then, you have to link the last step in the stream to the Table Input step where you will type the statement. What differentiates this statement from a regular statement is that you have to provide question marks. When you preview or run the transformation, the statement is prepared and the values coming to the Table Input step are bound to the placeholders; that is, the place where you typed the question marks.

Note that in the recipe the output of the stream was a single row with two fields, which is exactly the same number of question marks as in the statement.

Also note that in the stream, the product line was in the first place and the product scale in the second place. If you look at the highlighted lines in the recipe, you will see that the statement expected the parameter values to be exactly in that order.

If you need to parameterize something different from a value expression, you should take another approach, as explained in the next recipe.

There are a couple of situations worth discussing.

Parameters coming in more than one row

In the recipe you received the list of parameter values in a single row with as many columns as expected parameter values. It's also possible to receive the parameter values in several rows. If, instead of a row you had one parameter by row, as shown in the following screenshot, the behavior of the transformation wouldn't have changed:

The statement would have pulled the values for the two parameters from the incoming stream in the same order as the data appeared. It would have bound the first question mark with the value in the first row, and the second question mark with the value coming in the second row.

Note that this approach is less flexible than the previous one. For example, if you have to provide values for parameters with different data types, you will not be able to put them in the same column and different rows.

Executing the SELECT statement several times, each for a different set of parameters

Suppose that you not only want to list the Classic Cars in 1:10 scale, but also the Motorcycles in 1:10 and 1:12 scales. You don't have to run the transformation three times in order to do this. You can have a dataset with three rows, one for each set of parameters, as shown in the following screenshot:

Then, in the Table Input setting window you have to check the Execute for each row? option. This way, the statement will be prepared and the values coming to the Table Input step will be bound to the placeholders, once for each row in the dataset coming to the step. For this example, the result would look like the following:

You will need a book database with the structure as explained in Appendix A, Data Structures.

You can use Kettle variables in any part of the SELECT statement inside a Table Input step. When the transformation is initialized, PDI replaces the variables by their values provided that the Replace variables in script? option is checked.

In the recipe, the first time you ran the transformation, Kettle replaced the variable ORDER_COLUMN with the word title and the statement executed was as follows:

SELECT * FROM books ORDER BY title

The second time, the variable was replaced by genre and the executed statement was as follows:

SELECT * FROM books ORDER BY genre

You may use variables not only for the ORDER BY clause, but in any part of the statement: table names, columns, and so on. You could even hold the full statement in a variable. Note however that you need to be cautious when implementing this.

You can also use the same variable more than once in the same statement. This is an advantage of using variables as an alternative to question marks when you need to execute parameterized SELECT statements.

Named parameters are another option to store parts of statements. They are part of the job or transformation and allow for default values and clear definitions for what the parameter is. To add or edit named parameters, right-click on the transformation or job, go into its settings, and switch to the Parameters tab.

Download the material for the recipe from the book's site. Take a look at the file you will use:

EMPLOYEE_NUMBER, LASTNAME, FIRSTNAME, EXTENSION, OFFICE, REPORTS, TITLE
1188, Firrelli, Julianne,x2174,2,1143, Sales Manager
1619, King, Tom,x103,6,1088,Sales Rep
1810, Lundberg, Anna,x910,2,1143,Sales Rep
1811, Schulz, Chris,x951,2,1143,Sales Rep

Explore the Steel Wheels database, in particular the employees table, so you know what you have before running the transformation. Execute the following MySQL statement:

SELECT
      EMPLOYEENUMBER ENUM
    , CONCAT(FIRSTNAME,' ',LASTNAME) NAME
    , EXTENSION EXT
    , OFFICECODE OFF
    , REPORTSTO REPTO
    , JOBTITLE
    FROM EMPLOYEES
    WHERE EMPLOYEENUMBER IN (1188, 1619, 1810, 1811);
+------+----------------+-------+-----+-------+-----------+
| ENUM | NAME           | EXT   | OFF | REPTO | JOBTITLE  |
+------+----------------+-------+-----+-------+-----------+
| 1188 | Julie Firrelli | x2173 | 2   |  1143 | Sales Rep |
| 1619 | Tom King       | x103  | 6   |  1088 | Sales Rep |
+------+----------------+-------+-----+-------+-----------+
2 rows in set (0.00 sec)

Perform the following steps to insert or update rows in a table:

The Insert/Update step, as its name implies, serves for both inserting or updating rows. For each row in your stream, Kettle looks for a row in the table that matches the condition you put in the upper grid—the grid labeled The key(s) to look up the value(s):. Take for example the last row in your input file:

1811, Schulz, Chris,x951,2,1143,Sales Rep

When this row comes to the Insert/Update step, Kettle looks for a row where EMPLOYEENUMBER equals 1811. When it doesn't find one, it inserts a row following the directions you put in the lower grid. For this sample row, the equivalent INSERT statement would be as follows:

INSERT INTO EMPLOYEES (EMPLOYEENUMBER, LASTNAME, FIRSTNAME,
            EXTENSION, OFFICECODE, REPORTSTO, JOBTITLE)
       VALUES (1811, 'Schulz', 'Chris',
              'x951', 2, 1143, 'Sales Rep')

Now look at the first row:

1188, Firrelli, Julianne,x2174,2,1143, Sales Manager

When Kettle looks for a row with EMPLOYEENUMBER equal to 1188, it finds it. Then, it updates that row according to what you put in the lower grid. It only updates the columns where you put Y under the Update column. For this sample row, the equivalent UPDATE statement would be as follows:

UPDATE EMPLOYEES SET EXTENSION = 'x2174'
                   , OFFICECODE = 2
                   , REPORTSTO = 1143
                   , JOBTITLE = 'Sales Manager'
WHERE EMPLOYEENUMBER = 1188

Note that the name of this employee in the file (Julianne) is different from the name in the table (Julie), but, as you put N under the column Update for the field FIRSTNAME, this column was not updated.

Here there are two alternative solutions to this use case.

Alternative way for inserting and updating

The following is an alternative way for inserting and updating rows in a table.

Note

This alternative only works if the columns in the Key field's grid of the Insert/Update step are a unique key in the database.

You may replace the Insert/Update step by a Table Output step and, as the error handling stream coming out of the Table Output step, put an Update step.

Tip

In order to handle the error when creating the hop from the Table Output step towards the Update step, select the Error handling of step option.

Alternatively, right-click on the Table Output step, select Define error handling..., and configure the Step error handling settings window that shows up. Your transformation would look like the following:

In the Table Output step, select the table EMPLOYEES, check the Specify database fields option, and fill the Database fields tab just as you filled the lower grid in the Insert/Update step, except that here there is no Update column.

In the Update step, select the same table and fill the upper grid—let's call it the Key fields grid—just as you filled the Key fields grid in the Insert/Update step. Finally, fill the lower grid with those fields that you want to update, that is, those rows that had Y under the Update column.

In this case, Kettle tries to insert all records coming to the Table Output step. The rows for which the insert fails go to the Update step, and get updated.

If the columns in the Key fields grid of the Insert/Update step are not a unique key in the database, this alternative approach doesn't work. The Table Output would insert all the rows. Those that already existed would be duplicated instead of getting updated.

This strategy for performing inserts and updates has been proven to be much faster than the use of the Insert/Update step whenever the ratio of updates to inserts is low. In general, for best practice reasons, this is not an advisable solution.

For this recipe, you will use the Pentaho sample database. If you don't have that database, you'll have to follow the instructions in the introduction of this chapter.

As you will insert records into the office table, it would be good if you explore that table before doing any insert operations. The following is a sample query:

SELECT
     OFFICECODE
   , ADDRESSLINE1
   , CITY
   , COUNTRY
FROM   OFFICES
ORDER BY OFFICECODE;
+------------+--------------------------+---------------+-----------+
| OFFICECODE | ADDRESSLINE1             | CITY          | COUNTRY   |
+------------+--------------------------+---------------+-----------+
| 1          | 100 Market Street        | San Francisco | USA       |
| 2          | 1550 Court Place         | Boston        | USA       |
| 3          | 523 East 53rd Street     | NYC           | USA       |
| 4          | 43 Rue Jouffroy D'abbans | Paris         | France    |
| 5          | 4-1 Kioicho              | Tokyo         | Japan     |
| 6          | 5-11 Wentworth Avenue    | Sydney        | Australia |
| 7          | 25 Old Broad Street      | London        | UK        |
+------------+--------------------------+---------------+-----------+
7 rows in set (0.00 sec)

In many situations, before inserting data into a table you have to generate the primary key. If the primary key is a simple sequence or the maximum primary key plus one, you can generate it by using a Combination lookup/update step.

In the recipe, for each row in your file, with the Combination lookup/update step, you look for a record in the offices table with the same values for address, city, and country.

Because the offices are new, (there aren't offices in the table with the same combination of address, city, and country values) the lookup fails. As a consequence, the step generates a key value as the maximum OFFICECODE in the table, plus 1. Then, it inserts a row with the generated primary key and the fields you typed in the grid.

Finally, the step adds the generated primary key value to the stream.

As a last task, we used that key to update the other fields coming into the file: POSTALCODE and PHONE.

The Combination lookup/update step is within the Data Warehouse category, because it is mainly used for loading junk dimension tables. But, as you could see, it can also be used in the particular situation where you have to generate a primary key.

In this recipe you generated the primary key as the maximum plus 1, but, as you can see in the settings window, a database sequence can also be used instead.

Run the script that creates and loads data into the books database. You'll find it at http://packtpub.com/support. If the book's earlier example from this chapter has been run, the database and author data should already have been created.

Before proceeding, verify the current values for the primary keys in the table where you will insert data:

SELECT MAX(id_author)
FROM authors;
+----------------+
| MAX(id_author) |
+----------------+
| A00009         |
+----------------+
1 row in set (0.00 sec)

Perform the following steps to generate keys for inserted database records:

When you have to generate a primary key based on the existing primary keys, there is no direct way to do it in Kettle, unless the new primary key is simple to generate by adding one to the maximum. 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. 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 A00010, A00011, and so on. You did this by generating a sequence (1, 2, 3, and so on), adding this sequence to the max_id (that led to values 10, 11, 12, and so on), and finally formatting the key with the use of the calculator.

Note that in the Calculator step, the first A+B performs an arithmetic calculation. It adds the max_id with the delta_value sequence. Then it converts the result to a String giving it the format with the mask 0000. This led to the values 00010, 00011, and so on.

The second A+B is a string concatenation. 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. After the join, the mechanism for building the final key would depend on your particular case.

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, explore the database before following the recipe. In particular, execute the following statement:

SELECT  category
   , COUNT(*) quantity
FROM     products p
JOIN     categories c ON p.id_category=c.id_category
WHERE    price<=50
GROUP BY p.id_category;
+---------------+----------+
| category      | quantity |
+---------------+----------+
| kitchen       |       19 |
| lights        |       14 |
| sleeping bags |        5 |
| tents         |        4 |
| tools         |        8 |
+---------------+----------+
5 rows in set (0.00 sec)

SELECT   category
   , COUNT(*) quantity
FROM     products p
JOIN categories c ON p.id_category=c.id_category
WHERE    price>50
GROUP BY p.id_category;
+---------------+----------+
| category      | quantity |
+---------------+----------+
| kitchen       |        5 |
| lights        |        1 |
| sleeping bags |        1 |
| tents         |        8 |
| tools         |        2 |
+---------------+----------+
5 rows in set (0.00 sec)

The highlighted lines belong to the products that you intend to delete.

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:

PDI builds a prepared statement for the DELETE operation. 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.

If you run the transformation with the log level Detailed and look at the log, you will see the statement that is executed:

DELETE FROM products
WHERE price < ?
AND id_category = ?

The WHERE clause is built based on the conditions you entered in the Delete configuration window. For every row, the values of the fields you typed in the grid—max_price and id_category—are bound to the question marks in the prepared statement.

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 that 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. As the ID for the category tents is 4, the execution of the prepared statement with the values in this row has the same effect as the execution of the following SQL statement:

DELETE FROM products
WHERE price < 50
AND id_category = 4

In order to follow the instructions, you will need the countries.xml file available for downloads from the book's site.

Perform the following steps to create or alter a database table:

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.

A Table Output step causes Kettle to execute an INSERT statement against the database. 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.

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. As in this example, the table doesn't exist, and hence the statement generated by clicking on the button is a CREATE TABLE statement.

When the window with the generated statement appeared, you executed it. This caused 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. In all cases, its purpose is the same: to determine the statements to be executed in order to run the transformation successfully. In the recipe, the statement was a CREATE TABLE, but there are other situations. The following are some examples:

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.

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 on the database connection and selecting the Clear DB Cache option, or restart Spoon.

The Table exists entry, as implied by its name, verifies if a table exists in your database. As with any job entry, this entry either succeeds or fails. If it fails, the job creates the table with an SQL entry. If it succeeds, the job does nothing.

The SQL entry is very useful, not only for creating tables as you did in the recipe, but also for executing very simple statements, as for example, setting a flag before or after running a transformation. Its main use, however, is executing DDL statements.

On the other side, in order to decide if it was necessary to create the table or not, you used a Table exists entry. In addition to this entry and before verifying the existence of the table, you could have used the Check Db connections. This entry allows you to see if the database is available.

Now, let's suppose the table exists, but it is an old version that doesn't have all the columns you need. In this case you can use an extra useful entry: Columns exist in a table. If you can detect that a column is not present, you can alter the table by adding that column, also with an SQL job entry.

For this recipe, you will need the database which can be created and filled by running the script books_2.sql. You also will need the file books_news.txt that accompanies the material for this chapter.

As the recipe will modify the data in the database, before proceeding, explore the database to see what is inside. In particular, run the following statements and pay attention to the results:

SELECT count(*)
FROM   books;
+----------+
| count(*) |
+----------+
|       34 |
+----------+
1 row in set (0.00 sec)

SELECT id_title, title, price
FROM   books
WHERE  author_id='A00001';
+----------+----------------------------------------+-------+
| id_title | title                                  | price |
+----------+----------------------------------------+-------+
| 123-400  | The Girl with the Dragon Tattoo        |    37 |
| 123-401  | The Girl who Played with Fire          |  35.9 |
| 123-402  | The Girl who Kicked the Hornett's Nest |    39 |
+----------+----------------------------------------+-------+
3 rows in set (0.00 sec)

SELECT *
FROM   books
WHERE  title="Mockingjay";

Empty set (0.00 sec)

The Synchronize after merge step allows you to insert, update, or delete rows in a table based on the value of a field in the stream. In the recipe, you used the Synchronize after merge step both for inserting the new books (for example, Mockingjay) and for updating the prices for the books you already had (for example, The Girl with the Dragon Tattoo).

In order to tell PDI whether to execute an insert or an update, you used the field comment. Under the Advanced tab, you told PDI that it should insert the records where the comment was equal to NEW, and update those where the comment was In Stock.

Note that, because you didn't intend to delete rows, you left the Delete when value equal option blank. However, you could also have configured this option in the same way you configured the others. An example of that, could be deleting the books that will stop being published. If there are books that match the out of market criteria, you could type out of market in the Delete when value equal option and those books would be deleted.

The inserts and updates were made based on the fields you entered in the grids under the General tab, which work exactly as the grids in an Insert/Update or an Update step.

Let's see a little more about the step you used in this recipe.

Download the material for this recipe. You will find a sample file with database connections to three branches. It looks like the following:

branch,host,database
0001 (headquarters),localhost,sales2010
0002,183.43.2.33,sales
0003,233.22.1.97,sales

If you intend to run the transformation, modify the file so it points to real databases.

Perform the following steps to dynamically change database connections:

If you have to connect to several databases, and you don't know in advance which or how many databases you will have to connect to, you can't rely on a connection with fixed values or variables defined in a single place, for example, in the kettle.properties file (which is located in the Kettle home directory). In those situations, the best you could do is to define a connection with variables and set the values for the variables at runtime.

In the recipe, you created a text file with a summary sales line for each database in a list.

The transformation that wrote the sales line used a connection with variables defined as named parameters. This means that whoever calls the transformation has to provide the proper values.

The main job loops on the list of database connections. For each row in that list, it calls the transformation copying the values from the file to the parameters in the transformation. In other words, each time the transformation runs, the named parameters are instantiated with the values coming from the file.

In the recipe, you changed the host and the name of the database. You could have parameterized any of the values that made up a database connection, for example, the username and password.

In order to run this recipe, either truncate the employees table in Steel Wheels, or create the table employees in a different database.

If you have to load a table with parent-child relationships, loading all at once is not always feasible. Look at the sampledata database. There is no physical foreign key from the REPORTSTO column to the EMPLOYEENUMBER column, but if the foreign key had existed, it would fail because of the foreign key constraint. Not only that; in this case loading all at once would be impossible because in the file you missed the ID of the parent employee loading all records needed for the REPORTSTO column.

So, in this recipe there was one possible solution for loading the table. We loaded all employees, one role at a time, beginning with the president and followed by the roles below in the hierarchy. The transformation that loaded the other roles simply read the file, kept only the employees with the role being loaded, looked for the ID of the parent employee in the hierarchy, and inserted the records. For the roles you could have used fixed values, but you used regular expressions instead. In doing so, you avoided calling the transformation once for each different role. For example, for loading the vice-presidents you called the transformation once with the regular expression VP.* which matched both VP Sales and VP Marketing.

For this recipe you will need the database which can be created and filled by running the script books.sql. This can be found in the code for this chapter.

As the recipe will read metadata from the books database, before proceeding, explore the database's metadata repository to see what is inside. In particular, run these statements and pay attention to the results:

SELECT
    TABLE_NAME
    , TABLE_TYPE
    , ENGINE
    , VERSION
    , ROW_FORMAT
    , TABLE_ROWS
    , AVG_ROW_LENGTH
    , DATA_LENGTH
FROM information_schema.TABLES
WHERE TABLE_SCHEMA = 'books';

+--------------+------------+--------+---------+------------+------------+----------------+-------------+
| TABLE_NAME   | TABLE_TYPE | ENGINE | VERSION | ROW_FORMAT | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+--------------+------------+--------+---------+------------+------------+----------------+-------------+
| authors      | BASE TABLE | InnoDB |      10 | Compact    |         13 |           1260 |       16384 |
| books        | BASE TABLE | InnoDB |      10 | Compact    |         34 |            481 |       16384 |
+--------------+------------+--------+---------+------------+------------+----------------+-------------+
2 rows in set (0.00 sec)

SELECT
    TABLE_NAME
    , COLUMN_NAME
    , ORDINAL_POSITION
    , COLUMN_DEFAULT
    , IS_NULLABLE
    , DATA_TYPE
FROM information_schema.COLUMNS
WHERE TABLE_SCHEMA = 'books';

+--------------+-------------+------------------+----------------+-------------+-----------+
| TABLE_NAME   | COLUMN_NAME | ORDINAL_POSITION | COLUMN_DEFAULT | IS_NULLABLE | DATA_TYPE |
+--------------+-------------+------------------+----------------+-------------+-----------+
| authors      | lastname    |                1 | NULL           | NO          | tinytext  |
| authors      | firstname   |                2 | NULL           | NO          | tinytext  |
| authors      | nationality |                3 | unknown        | YES         | varchar   |
| authors      | birthyear   |                4 | 1900           | YES         | int       |
| authors      | id_author   |                5 | NULL           | NO          | char      |
| books        | id_title    |                1 | NULL           | NO          | char      |
| books        | title       |                2 | NULL           | NO          | tinytext  |
| books        | id_author   |                3 | NULL           | NO          | tinytext  |
| books        | price       |                4 | 0              | YES         | double    |
| books        | genre       |                5 | unknown        | YES         | varchar   |
+--------------+-------------+------------------+----------------+-------------+-----------+
10 rows in set (0.00 sec)

Compare how the data in the information_schema database matches the CREATE DDL statements found in the books.sql file. Notice how the same parameters in the statements used to create the tables translate into the TABLES and COLUMNS tables.

Most databases have a metadata repository that details tables and columns. This can be used to build dynamic queries to extract data for further processing. In this instance, once the books database was created, the database application stored the metadata inside the information_schema database. We then queried that database and used a Row denormaliser step to merge the column details into a single field so that our query would execute properly.

For this recipe you will need the database which can be created and filled by running the script books.sql. This can be found in the code for this chapter.

As the recipe will read metadata from the books database, before proceeding, explore the database's metadata repository to see what is inside. In particular, run the following statement to see what tables are available in the books database:

SELECT
    TABLE_NAME
    , TABLE_TYPE
    , ENGINE
    , VERSION
    , ROW_FORMAT
    , TABLE_ROWS
    , AVG_ROW_LENGTH
    , DATA_LENGTH
FROM information_schema.TABLES
WHERE TABLE_SCHEMA = 'books';

+--------------+------------+--------+---------+------------+------------+----------------+-------------+
| TABLE_NAME   | TABLE_TYPE | ENGINE | VERSION | ROW_FORMAT | TABLE_ROWS | AVG_ROW_LENGTH | DATA_LENGTH |
+--------------+------------+--------+---------+------------+------------+----------------+-------------+
| authors      | BASE TABLE | InnoDB |      10 | Compact    |         13 |           1260 |       16384 |
| books        | BASE TABLE | InnoDB |      10 | Compact    |         34 |            481 |       16384 |
+--------------+------------+--------+---------+------------+------------+----------------+-------------+
2 rows in set (0.00 sec)

Using a database's metadata repository is a very powerful and effective way to know the structure of a given source database. With this transformation we took advantage of that by reading out a list of all the tables from the books database and ran a variable-based query that added two columns to each table based on the table name.

Try adding additional filters to specify certain tables from the books database. MySQL's information_schema database also has a table that details the columns of each table (aptly named COLUMNS). For larger databases, you may want to filter just a subset of tables based on given columns or types.