AWS Documentation » AWS Glue » Developer Guide » Programming ETL Scripts » Program AWS Glue ETL Scripts in Python » AWS Glue PySpark Extensions Reference » GlueContext Class

GlueContext Class

Wraps the Apache SparkSQL SQLContext object, and thereby provides mechanisms for interacting with the Apache Spark platform.

Creating

• __init__

• getSource

• create_dynamic_frame_from_rdd

• create_dynamic_frame_from_catalog

• create_dynamic_frame_from_options

__init__

__init__(sparkContext)

• sparkContext – The Apache Spark context to use.

getSource

getSource(connection_type, transformation_ctx = "", **options)

Creates a DataSource object that can be used to read DynamicFrames from external sources.

• connection_type – The connection type to use, such as Amazon S3, Amazon Redshift, and JDBC. Valid values include s3, mysql, postgresql, redshift, sqlserver, oracle, and dynamodb.

• transformation_ctx – The transformation context to use (optional).

• options – A collection of optional name-value pairs. For more information, see See Connection Types and Options for ETL in AWS Glue.

The following is an example of using getSource:

>>> data_source = context.getSource("file", paths=["/in/path"])
>>> data_source.setFormat("json")
>>> myFrame = data_source.getFrame()

create_dynamic_frame_from_rdd

create_dynamic_frame_from_rdd(data, name, schema=None, sample_ratio=None, transformation_ctx="")

Returns a DynamicFrame that is created from an Apache Spark Resilient Distributed Dataset (RDD).

• data – The data source to use.

• name – The name of the data to use.

• schema – The schema to use (optional).

• sample_ratio – The sample ratio to use (optional).

• transformation_ctx – The transformation context to use (optional).

create_dynamic_frame_from_catalog

create_dynamic_frame_from_catalog(database, table_name, redshift_tmp_dir, transformation_ctx = "")

Returns a DynamicFrame that is created using a catalog database and table name.

• Database – The database to read from.

• table_name – The name of the table to read from.

• redshift_tmp_dir – An Amazon Redshift temporary directory to use (optional).

• transformation_ctx – The transformation context to use (optional).

create_dynamic_frame_from_options

create_dynamic_frame_from_options(connection_type, connection_options={}, format=None, format_options={}, transformation_ctx = "")

Returns a DynamicFrame created with the specified connection and format.

• connection_type – The connection type, such as Amazon S3, Amazon Redshift, and JDBC. Valid values include s3, mysql, postgresql, redshift, sqlserver, oracle, and dynamodb.

• connection_options – Connection options, such as path and database table (optional). For a connection_type of s3, an Amazon S3 path is defined.

  connection_options = {"paths": ["s3://aws-glue-target/temp"]}

  For JDBC connections, several properties must be defined. Note that the database name must be part of the URL. It can optionally be included in the connection options.

  connection_options = {"url": "jdbc-url/database", "user": "username", "password": "password","dbtable": "table-name", "redshiftTmpDir": "s3-tempdir-path"} 

• format – A format specification (optional). This is used for an Amazon Simple Storage Service (Amazon S3) or an AWS Glue connection that supports multiple formats. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• format_options – Format options for the specified format. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• transformation_ctx – The transformation context to use (optional).

Writing

• getSink

• write_dynamic_frame_from_options

• write_from_options

• write_dynamic_frame_from_catalog

• write_dynamic_frame_from_jdbc_conf

• write_from_jdbc_conf

getSink

getSink(connection_type, format = None, transformation_ctx = "", **options)

Gets a DataSink object that can be used to write DynamicFrames to external sources. Check the SparkSQL format first to be sure to get the expected sink.

• connection_type – The connection type to use, such as Amazon S3, Amazon Redshift, and JDBC. Valid values include s3, mysql, postgresql, redshift, sqlserver, and oracle.

• format – The SparkSQL format to use (optional).

• transformation_ctx – The transformation context to use (optional).

• options – A collection of option name-value pairs.

For example:

>>> data_sink = context.getSink("s3")
>>> data_sink.setFormat("json"),
>>> data_sink.writeFrame(myFrame)

write_dynamic_frame_from_options

write_dynamic_frame_from_options(frame, connection_type, connection_options={}, format=None, format_options={}, transformation_ctx = "")

Writes and returns a DynamicFrame using the specified connection and format.

• frame – The DynamicFrame to write.

• connection_type – The connection type, such as Amazon S3, Amazon Redshift, and JDBC. Valid values include s3, mysql, postgresql, redshift, sqlserver, and oracle.

• connection_options – Connection options, such as path and database table (optional). For a connection_type of s3, an Amazon S3 path is defined.

  connection_options = {"path": "s3://aws-glue-target/temp"}

  For JDBC connections, several properties must be defined. Note that the database name must be part of the URL. It can optionally be included in the connection options.

  connection_options = {"url": "jdbc-url/database", "user": "username", "password": "password","dbtable": "table-name", "redshiftTmpDir": "s3-tempdir-path"} 

• format – A format specification (optional). This is used for an Amazon Simple Storage Service (Amazon S3) or an AWS Glue connection that supports multiple formats. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• format_options – Format options for the specified format. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• transformation_ctx – A transformation context to use (optional).

write_from_options

write_from_options(frame_or_dfc, connection_type, connection_options={}, format={}, format_options={}, transformation_ctx = "")

Writes and returns a DynamicFrame or DynamicFrameCollection that is created with the specified connection and format information.

• frame_or_dfc – The DynamicFrame or DynamicFrameCollection to write.

• connection_type – The connection type, such as Amazon S3, Amazon Redshift, and JDBC. Valid values include s3, mysql, postgresql, redshift, sqlserver, and oracle.

• connection_options – Connection options, such as path and database table (optional). For a connection_type of s3, an Amazon S3 path is defined.

  connection_options = {"path": "s3://aws-glue-target/temp"}

  For JDBC connections, several properties must be defined. Note that the database name must be part of the URL. It can optionally be included in the connection options.

  connection_options = {"url": "jdbc-url/database", "user": "username", "password": "password","dbtable": "table-name", "redshiftTmpDir": "s3-tempdir-path"} 

• format – A format specification (optional). This is used for an Amazon Simple Storage Service (Amazon S3) or an AWS Glue connection that supports multiple formats. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• format_options – Format options for the specified format. See Format Options for ETL Output in AWS Glue for the formats that are supported.

• transformation_ctx – A transformation context to use (optional).

write_dynamic_frame_from_catalog

write_dynamic_frame_from_catalog(frame, database, table_name, redshift_tmp_dir, transformation_ctx = "")

Writes and returns a DynamicFrame using a catalog database and a table name.

• frame – The DynamicFrame to write.

• Database – The database to read from.

• table_name – The name of the table to read from.

• redshift_tmp_dir – An Amazon Redshift temporary directory to use (optional).

• transformation_ctx – The transformation context to use (optional).

write_dynamic_frame_from_jdbc_conf

write_dynamic_frame_from_jdbc_conf(frame, catalog_connection, connection_options={}, redshift_tmp_dir = "", transformation_ctx = "")

Writes and returns a DynamicFrame using the specified JDBC connection information.

• frame – The DynamicFrame to write.

• catalog_connection – A catalog connection to use.

• connection_options – Connection options, such as path and database table (optional).

• redshift_tmp_dir – An Amazon Redshift temporary directory to use (optional).

• transformation_ctx – A transformation context to use (optional).

write_from_jdbc_conf

write_from_jdbc_conf(frame_or_dfc, catalog_connection, connection_options={}, redshift_tmp_dir = "", transformation_ctx = "")

Writes and returns a DynamicFrame or DynamicFrameCollection using the specified JDBC connection information.

• frame_or_dfc – The DynamicFrame or DynamicFrameCollection to write.

• catalog_connection – A catalog connection to use.

• connection_options – Connection options, such as path and database table (optional).

• redshift_tmp_dir – An Amazon Redshift temporary directory to use (optional).

• transformation_ctx – A transformation context to use (optional).