Joining datasets - Amazon Glue
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Joining datasets

The Join transform allows you to combine two datasets into one. You specify the key names in the schema of each dataset to compare. The output DynamicFrame contains rows where keys meet the join condition. The rows in each dataset that meet the join condition are combined into a single row in the output DynamicFrame that contains all the columns found in either dataset.

To add a Join transform node to your job diagram
  1. If there is only one data source available, you must add a new data source node to the job diagram.

  2. Choose one of the source nodes for the join. Open the Resource panel and then choose Join to add a new transform to your job diagram.

  3. On the Node properties tab, enter a name for the node in the job diagram.

  4. In the Node properties tab, under the heading Node parents, add a parent node so that there are two datasets providing inputs for the join. The parent can be a data source node or a transform node.

    Note

    A join can have only two parent nodes.

  5. Choose the Transform tab.

    If you see a message indicating that there are conflicting key names, you can either:

    • Choose Resolve it to automatically add an ApplyMapping transform node to your job diagram. The ApplyMapping node adds a prefix to any keys in the dataset that have the same name as a key in the other dataset. For example, if you use the default value of right, then any keys in the right dataset that have the same name as a key in the left dataset will be renamed to (right)key name.

    • Manually add a transform node earlier in the job diagram to remove or rename the conflicting keys.

  6. Choose the type of join in the Join type list.

    • Inner join: Returns a row with columns from both datasets for every match based on the join condition. Rows that don't satisfy the join condition aren't returned.

    • Left join: All rows from the left dataset and only the rows from the right dataset that satisfy the join condition.

    • Right join: All rows from the right dataset and only the rows from the left dataset that satisfy the join condition.

    • Outer join: All rows from both datasets.

    • Left semi join: All rows from the left dataset that have a match in the right dataset based on the join condition.

    • Left anti join: All rows in the left dataset that don't have a match in the right dataset based on join condition.

  7. On the Transform tab, under the heading Join conditions, choose Add condition. Choose a property key from each dataset to compare. Property keys on the left side of the comparison operator are referred to as the left dataset and property keys on the right are referred to as the right dataset.

    For more complex join conditions, you can add additional matching keys by choosing Add condition more than once. If you accidentally add a condition, you can choose the delete icon ( 
                An outline of a trash can
              ) to remove it.

  8. (Optional) After configuring the transform node properties, you can view the modified schema for your data by choosing the Output schema tab in the node details panel. The first time you choose this tab for any node in your job, you are prompted to provide an IAM role to access the data. If you have not specified an IAM role on the Job details tab, you are prompted to enter an IAM role here.

  9. (Optional) After configuring the node properties and transform properties, you can preview the modified dataset by choosing the Data preview tab in the node details panel. The first time you choose this tab for any node in your job, you are prompted to provide an IAM role to access the data. There is a cost associated with using this feature, and billing starts as soon as you provide an IAM role.

For an example of the join output schema, consider a join between two datasets with the following property keys:

Left: {id, dept, hire_date, salary, employment_status} Right: {id, first_name, last_name, hire_date, title}

The join is configured to match on the id and hire_date keys using the = comparison operator.

Because both datasets contain id and hire_date keys, you chose Resolve it to automatically add the prefix right to the keys in the right dataset.

The keys in the output schema would be:

{id, dept, hire_date, salary, employment_status, (right)id, first_name, last_name, (right)hire_date, title}