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Authoring jobs with custom connectors

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Authoring jobs with custom connectors - AWS Glue
Create jobs that use a connector for the data sourceConfigure source properties for nodes that use connectorsConfigure target properties for nodes that use connectors

You can use connectors and connections for both data source nodes and data target nodes in AWS Glue Studio.

Create jobs that use a connector for the data source

When you create a new job, you can choose a connector for the data source and data targets.

To create a job that uses connectors for the data source or data target

  1. Sign in to the AWS Management Console and open the AWS Glue Studio console at https://console.aws.amazon.com/gluestudio/.

  2. On the Connectors page, in the Your connections resource list, choose the connection you want to use in your job, and then choose Create job.

    Alternatively, on the AWS Glue Studio Jobs page, under Create job, choose Source and target added to the graph. In the Source drop-down list, choose the custom connector that you want to use in your job. You can also choose a connector for Target.

    The image is a screenshot of the Jobs page, with the Source drop-down list selected, showing the various data sources that can be selected for the job, including connectors.

  3. Choose Create to open the visual job editor.

  4. Configure the data source node, as described in Configure source properties for nodes that use connectors.

  5. Continue creating your ETL job by adding transforms, additional data stores, and data targets, as described in Starting visual ETL jobs in AWS Glue Studio.

  6. Customize the job run environment by configuring job properties as described in Modify the job properties.

  7. Save and run the job.

Configure source properties for nodes that use connectors

After you create a job that uses a connector for the data source, the visual job editor displays a job graph with a data source node configured for the connector. You must configure the data source properties for that node.

To configure the properties for a data source node that uses a connector

  1. Choose the connector data source node in the job graph or add a new node and choose the connector for the Node type. Then, on the right-side, in the node details panel, choose the Data source properties tab, if it's not already selected.

    The image is a screenshot of the AWS Glue Studio visual job editor page, with a data source node selected in the graph. The Data source properties tab on the right is selected. The fields displayed for the data source properties are Connection (a drop-down list of available connections, followed by a Refresh button) and an Add schema button. An additional Connection options section is shown in its collapsed state.

  2. In the Data source properties tab, choose the connection that you want to use for this job.

    Enter the additional information required for each connection type:

    JDBC

    • Data source input type: Choose to provide either a table name or a SQL query as the data source. Depending on your choice, you then need to provide the following additional information:

      • Table name: The name of the table in the data source. If the data source does not use the term table, then supply the name of an appropriate data structure, as indicated by the custom connector usage information (which is available in AWS Marketplace).

      • Filter predicate: A condition clause to use when reading the data source, similar to a WHERE clause, which is used to retrieve a subset of the data.

      • Query code: Enter a SQL query to use to retrieve a specific dataset from the data source. An example of a basic SQL query is:

        SELECT column_list FROM 
                          table_name WHERE where_clause

    • Schema: Because AWS Glue Studio is using information stored in the connection to access the data source instead of retrieving metadata information from a Data Catalog table, you must provide the schema metadata for the data source. Choose Add schema to open the schema editor.

      For instructions on how to use the schema editor, see Editing the schema in a custom transform node.

    • Partition column: (Optional) You can choose to partition the data reads by providing values for Partition column, Lower bound, Upper bound, and Number of partitions.

      The lowerBound and upperBound values are used to decide the partition stride, not for filtering the rows in table. All rows in the table are partitioned and returned.

      Note

      Column partitioning adds an extra partitioning condition to the query used to read the data. When using a query instead of a table name, you should validate that the query works with the specified partitioning condition. For example:

      • If your query format is "SELECT col1 FROM table1", then test the query by appending a WHERE clause at the end of the query that uses the partition column.

      • If your query format is "SELECT col1 FROM table1 WHERE col2=val", then test the query by extending the WHERE clause with AND and an expression that uses the partition column.

    • Data type casting: If the data source uses data types that are not available in JDBC, use this section to specify how a data type from the data source should be converted into JDBC data types. You can specify up to 50 different data type conversions. All columns in the data source that use the same data type are converted in the same way.

      For example, if you have three columns in the data source that use the Float data type, and you indicate that the Float data type should be converted to the JDBC String data type, then all three columns that use the Float data type are converted to String data types.

    • Job bookmark keys: Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data. Specify one more one or more columns as bookmark keys. AWS Glue Studio uses bookmark keys to track data that has already been processed during a previous run of the ETL job. Any columns you use for custom bookmark keys must be strictly monotonically increasing or decreasing, but gaps are permitted.

      If you enter multiple bookmark keys, they're combined to form a single compound key. A compound job bookmark key should not contain duplicate columns. If you don't specify bookmark keys, AWS Glue Studio by default uses the primary key as the bookmark key, provided that the primary key is sequentially increasing or decreasing (with no gaps). If the table doesn't have a primary key, but the job bookmark property is enabled, you must provide custom job bookmark keys. Otherwise, the search for primary keys to use as the default will fail and the job run will fail.

    • Job bookmark keys sorting order: Choose whether the key values are sequentially increasing or decreasing.

    Spark

    • Schema: Because AWS Glue Studio is using information stored in the connection to access the data source instead of retrieving metadata information from a Data Catalog table, you must provide the schema metadata for the data source. Choose Add schema to open the schema editor.

      For instructions on how to use the schema editor, see Editing the schema in a custom transform node.

    • Connection options: Enter additional key-value pairs as needed to provide additional connection information or options. For example, you might enter a database name, table name, a user name, and password.

      For example, for OpenSearch, you enter the following key-value pairs, as described in Tutorial: Using the AWS Glue Connector for Elasticsearch :

      • es.net.http.auth.user : username

      • es.net.http.auth.pass : password

      • es.nodes : https://<Elasticsearch endpoint>

      • es.port : 443

      • path: <Elasticsearch resource>

      • es.nodes.wan.only : true

    For an example of the minimum connection options to use, see the sample test script MinimalSparkConnectorTest.scala on GitHub, which shows the connection options you would normally provide in a connection.

    Athena

    • Table name: The name of the table in the data source. If you're using a connector for reading from Athena-CloudWatch logs, you would enter the table name all_log_streams.

    • Athena schema name: Choose the schema in your Athena data source that corresponds to the database that contains the table. If you're using a connector for reading from Athena-CloudWatch logs, you would enter a schema name similar to /aws/glue/name.

    • Schema: Because AWS Glue Studio is using information stored in the connection to access the data source instead of retrieving metadata information from a Data Catalog table, you must provide the schema metadata for the data source. Choose Add schema to open the schema editor.

      For instructions on how to use the schema editor, see Editing the schema in a custom transform node.

    • Additional connection options: Enter additional key-value pairs as needed to provide additional connection information or options.

    For an example, see the README.md file at https://github.com/aws-samples/aws-glue-samples/tree/master/GlueCustomConnectors/development/Athena. In the steps in this document, the sample code shows the minimal required connection options, which are tableName, schemaName, and className. The code example specifies these options as part of the optionsMap variable, but you can specify them for your connection and then use the connection.

    anchoranchoranchor

    • Data source input type: Choose to provide either a table name or a SQL query as the data source. Depending on your choice, you then need to provide the following additional information:

      • Table name: The name of the table in the data source. If the data source does not use the term table, then supply the name of an appropriate data structure, as indicated by the custom connector usage information (which is available in AWS Marketplace).

      • Filter predicate: A condition clause to use when reading the data source, similar to a WHERE clause, which is used to retrieve a subset of the data.

      • Query code: Enter a SQL query to use to retrieve a specific dataset from the data source. An example of a basic SQL query is:

        SELECT column_list FROM 
                          table_name WHERE where_clause

    • Schema: Because AWS Glue Studio is using information stored in the connection to access the data source instead of retrieving metadata information from a Data Catalog table, you must provide the schema metadata for the data source. Choose Add schema to open the schema editor.

      For instructions on how to use the schema editor, see Editing the schema in a custom transform node.

    • Partition column: (Optional) You can choose to partition the data reads by providing values for Partition column, Lower bound, Upper bound, and Number of partitions.

      The lowerBound and upperBound values are used to decide the partition stride, not for filtering the rows in table. All rows in the table are partitioned and returned.

      Note

      Column partitioning adds an extra partitioning condition to the query used to read the data. When using a query instead of a table name, you should validate that the query works with the specified partitioning condition. For example:

      • If your query format is "SELECT col1 FROM table1", then test the query by appending a WHERE clause at the end of the query that uses the partition column.

      • If your query format is "SELECT col1 FROM table1 WHERE col2=val", then test the query by extending the WHERE clause with AND and an expression that uses the partition column.

    • Data type casting: If the data source uses data types that are not available in JDBC, use this section to specify how a data type from the data source should be converted into JDBC data types. You can specify up to 50 different data type conversions. All columns in the data source that use the same data type are converted in the same way.

      For example, if you have three columns in the data source that use the Float data type, and you indicate that the Float data type should be converted to the JDBC String data type, then all three columns that use the Float data type are converted to String data types.

    • Job bookmark keys: Job bookmarks help AWS Glue maintain state information and prevent the reprocessing of old data. Specify one more one or more columns as bookmark keys. AWS Glue Studio uses bookmark keys to track data that has already been processed during a previous run of the ETL job. Any columns you use for custom bookmark keys must be strictly monotonically increasing or decreasing, but gaps are permitted.

      If you enter multiple bookmark keys, they're combined to form a single compound key. A compound job bookmark key should not contain duplicate columns. If you don't specify bookmark keys, AWS Glue Studio by default uses the primary key as the bookmark key, provided that the primary key is sequentially increasing or decreasing (with no gaps). If the table doesn't have a primary key, but the job bookmark property is enabled, you must provide custom job bookmark keys. Otherwise, the search for primary keys to use as the default will fail and the job run will fail.

    • Job bookmark keys sorting order: Choose whether the key values are sequentially increasing or decreasing.

  3. (Optional) After providing the required information, you can view the resulting data schema for your data source by choosing the Output schema tab in the node details panel. The schema displayed on this tab is used by any child nodes that you add to the job graph.

  4. (Optional) After configuring the node properties and data source properties, you can preview the dataset from your data source 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.

Configure target properties for nodes that use connectors

If you use a connector for the data target type, you must configure the properties of the data target node.

To configure the properties for a data target node that uses a connector

  1. Choose the connector data target node in the job graph. Then, on the right-side, in the node details panel, choose the Data target properties tab, if it's not already selected.

  2. In the Data target properties tab, choose the connection to use for writing to the target.

    Enter the additional information required for each connection type:

    JDBC

    • Connection: Choose the connection to use with your connector. For information about how to create a connection, see Creating connections for connectors.

    • Table name: The name of the table in the data target. If the data target does not use the term table, then supply the name of an appropriate data structure, as indicated by the custom connector usage information (which is available in AWS Marketplace).

    • Batch size (Optional): Enter the number of rows or records to insert in the target table in a single operation. The default value is 1000 rows.

    Spark

    • Connection: Choose the connection to use with your connector. If you did not create a connection previously, choose Create connection to create one. For information about how to create a connection, see Creating connections for connectors.

    • Connection options: Enter additional key-value pairs as needed to provide additional connection information or options. You might enter a database name, table name, a user name, and password.

      For example, for OpenSearch, you enter the following key-value pairs, as described in Tutorial: Using the AWS Glue Connector for Elasticsearch :

      • es.net.http.auth.user : username

      • es.net.http.auth.pass : password

      • es.nodes : https://<Elasticsearch endpoint>

      • es.port : 443

      • path: <Elasticsearch resource>

      • es.nodes.wan.only : true

    For an example of the minimum connection options to use, see the sample test script MinimalSparkConnectorTest.scala on GitHub, which shows the connection options you would normally provide in a connection.

    anchoranchor

    • Connection: Choose the connection to use with your connector. For information about how to create a connection, see Creating connections for connectors.

    • Table name: The name of the table in the data target. If the data target does not use the term table, then supply the name of an appropriate data structure, as indicated by the custom connector usage information (which is available in AWS Marketplace).

    • Batch size (Optional): Enter the number of rows or records to insert in the target table in a single operation. The default value is 1000 rows.

  3. After providing the required information, you can view the resulting data schema for your data source by choosing the Output schema tab in the node details panel.

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