ADLS Gen1

The ADLS Gen1 destination writes files to Microsoft Azure Data Lake Storage Gen1. To write to Azure Data Lake Storage Gen2, use the ADLS Gen2 destination.

Note: When this stage is included in a pipeline that runs on an Azure HDInsight cluster, use an Azure HDInsight cluster version 4.0 or later.

The destination connects to Azure Data Lake Storage Gen1 using Azure Active Directory service principal authentication, also known as service-to-service authentication.

The destination writes data based on the specified data format and creates a separate file for every partition. Before you use the ADLS Gen1 destination, you must perform some prerequisite tasks.

When you configure the ADLS Gen1 destination, you specify the service name and Azure authentication information such as the application ID and key. Or, you can have the destination use Azure authentication information configured in the cluster where the pipeline runs.

You specify the output directory and write mode to use. You can configure the destination to group partition files by field values. If you configure the destination to overwrite related partitions, you must configure Spark to overwrite partitions dynamically. You can also configure the destination to drop unrelated master records when using the destination as part of a slowly changing dimension pipeline.

You select the data format to write and configure related properties.

Prerequisites

Complete the following prerequisites, as needed, before you configure the ADLS Gen1 destination:
  1. If necessary, create a new Azure Active Directory application for StreamSets Transformer.

    For information about creating a new application, see the Azure documentation.

  2. Ensure that the Azure Active Directory Transformer application has the appropriate access control to perform the necessary tasks.

    To write to Azure, the Transformer application requires Write and Execute permissions. If also reading from Azure, the application requires Read permission as well.

    For information about configuring Gen1 access control, see the Azure documentation.

  3. Install the ADLS Gen1 driver on the cluster where the pipeline runs.

    Most recent cluster versions include the ADLS Gen1 driver, azure-datalake-store.jar. However, older versions might require installing it. For more information about Hadoop support for ADLS Gen1, see the Hadoop documentation.

  4. Retrieve Azure Data Lake Storage Gen1 authentication information from the Azure portal for configuring the destination.

    You can skip this step if you want to use Azure authentication information configured in the cluster where the pipeline runs.

  5. Before using the stage in a local pipeline, ensure that Hadoop-related tasks are complete.

Retrieve Authentication Information

The ADLS Gen1 destination connects to Azure using Azure Active Directory service principal authentication, also known as service-to-service authentication.

The destination requires several Azure authentication details to connect to Azure. If the cluster where the pipeline runs has the necessary Azure authentication information configured, then the destination uses that information by default. However, data preview is not available when using Azure authentication information configured in the cluster.

You can also specify Azure authentication information in stage properties. Any authentication information specified in stage properties takes precedence over the authentication information configured in the cluster.

The destination requires the following Azure authentication information:

  • Application ID - Application ID for the Azure Active Directory Transformer application. Also known as the client ID.

    For information on accessing the application ID from the Azure portal, see the Azure documentation.

  • Application Key - Authentication key for the Azure Active Directory Transformer application. Also known as the client key.

    For information on accessing the application key from the Azure portal, see the Azure documentation.

  • OAuth Token Endpoint - OAuth 2.0 token endpoint for the Azure Active Directory v1.0 application for Transformer. For example: https://login.microsoftonline.com/<uuid>/oauth2/token.

Write Mode

The write mode determines how the ADLS Gen1 destination writes files to the destination system. When writing files, the resulting file names are based on the data format of the files.

The ADLS Gen1 destination includes the following write modes:
Overwrite files
Before writing data from a batch, the destination removes all files from the output directory and any subdirectories.
Overwrite related partitions
Before writing data from a batch, the destination removes the files from subdirectories for which the batch has data. The destination leaves subdirectories intact if the batch has no data for that subdirectory.

For example, say you have a directory with ten subdirectories. If the processed data belongs in two subdirectories, the destination overwrites the two subdirectories with the new data. The other eight subdirectories remain unchanged.

Use this mode to overwrite only affected files, like when writing to a slowly changing grouped file dimension.

The Partition by Fields property specifies the fields used to create subdirectories. With no specified fields, the destination creates no subdirectories and this mode works the same as the Overwrite Files mode.

Note: This mode requires you to configure Spark to overwrite partitions dynamically.
Write new files to new directory
When the pipeline starts, the destination creates the output directory and writes new files to that directory. Before writing data for each subsequent batch during the pipeline run, the destination removes all files from the directory and any subdirectories. The destination generates an error if the output directory exists when the pipeline starts.
Write new or append to existing files
The destination creates new files if the files do not exist, or appends data to existing files if the files already exist in the output directory or subdirectory.

Spark Requirement to Overwrite Related Partitions

If you set Write Mode to Overwrite Related Partitions, you must configure Spark to overwrite partitions dynamically.

Set the Spark configuration property spark.sql.sources.partitionOverwriteMode to dynamic.

You can configure the property in Spark, or you can configure the property in individual pipelines. For example, you might set the property to dynamic in Spark when you plan to enable the Overwrite Related Partitions mode in most of your pipelines, and then set the property to static in individual pipelines that do not use that mode.

To configure the property in an individual pipeline, add an extra Spark configuration property on the Cluster tab of the pipeline properties.

Partition Files

Pipelines process data in partitions. The ADLS Gen1 destination writes files, which contain the processed data, in the configured output directory. The destination writes one file for each partition.

The destination groups the partition files if you specify one or more fields in the Partition by Fields property. For each unique value of the fields specified in the Partition by Fields property, the destination creates a subdirectory. In each subdirectory, the destination writes one file for each partition that has the corresponding field and value. Because the subdirectory name includes the field name and value, the file omits that data. With grouped partition files, you can more easily find data with certain values, such as all the data for a particular city.

To overwrite subdirectories that have updated data and leave other subdirectories intact, set the Write Mode property to Overwrite Related Partitions. Then, the destination clears affected subdirectories before writing, replacing files in those subdirectories with new files.

If the Partition by Fields property lists no fields, the destination does not group partition files. Instead, the destination writes one file for each partition directly in the output directory.

Because the text data format only contains data from one field, the destination does not group partition files for the text data format. Do not configure the Partition by Fields property if the destination writes in the text data format.

Example: Grouping Partition Files

Suppose your pipeline processes orders. You want to write data in the Sales directory and group the data by cities. Therefore, in the Directory Path property, you enter Sales, and in the Partition by Fields property, you enter City. Your pipeline is a batch pipeline that only processes one batch. You want to overwrite the entire Sales directory each time you run the pipeline. Therefore, in the Write Mode property, you select Overwrite Files.

As the pipeline processes the batch, the origins and processors lead Spark to split the data into three partitions. The batch contains two values in the City field: Oakland and Fremont. Before writing the processed data in the Sales directory, the destination removes any existing files and subdirectories and then creates two subdirectories, City=Oakland and City=Fremont. In each subdirectory, the destination writes one file for each partition that contains data for that city, as shown below:

Note that the written files do not include the City field; instead, you infer the city from the subdirectory name. The first partition does not include any data for Fremont; therefore, the City=Fremont subdirectory does not contain a file from the first partition. Similarly, the third partition does not include any data for Oakland; therefore, the City=Oakland subdirectory does not contain a file from the third partition.

Data Formats

The ADLS Gen1 destination writes records based on the specified data format.

The destination can write using the following data formats:
Avro
The destination writes an Avro file for each partition and includes the Avro schema in each file.
Output files use the following naming convention:
part-<multipart partition number>.avro
When you configure the destination, you must specify the Avro option appropriate for the version of Spark to run the pipeline: Spark 2.3 or Spark 2.4 or later.
Delimited
The destination writes a delimited file for each partition. It creates a header line for each file and uses \n as the newline character. You can specify a custom delimiter, quote, and escape character to use in the data.
Output files use the following naming convention:
part-<multipart partition number>.csv
JSON
The destination writes a file for each partition and writes each record on a separate line. For more information, see the JSON Lines website.
Output files use the following naming convention:
part-<multipart partition number>.json
ORC
The destination writes an ORC file for each partition.
Output files use the following naming convention:
part-<multipart partition number>.snappy.orc
Parquet
The destination writes a Parquet file for each partition and includes the Parquet schema in every file.
Output files use the following naming convention:
part-<multipart partition number>.snappy.parquet
Text
The destination writes a text file for every partition and uses \n as the newline character.
The destination writes data from a single String field to output files. You specify the field used.
Output files use the following naming convention:
part-<multipart partition number>.txt
Do not configure the Partition by Fields property when the destination writes Text data.
XML
The destination writes an XML file for every partition. You specify the root and row tags to use in output files.
Output files use the following naming convention:
part-<5 digit number>

Configuring an ADLS Gen1 Destination

Configure an ADLS Gen1 destination to write files to Azure Data Lake Storage Gen1. Before you use this destination, complete the required prerequisites.

To write to Azure Data Lake Storage Gen2, use the ADLS Gen2 destination.

  1. On the Properties panel, on the General tab, configure the following properties:
    General Property Description
    Name Stage name.
    Description Optional description.
    Stage Library Stage library to use:
    • ADLS cluster-provided libraries - The cluster where the pipeline runs has Apache Hadoop Azure Data Lake libraries installed, and therefore has all of the necessary libraries to run the pipeline.

      Use when you want to use the Apache Hadoop Azure Data Lake library installed on the cluster. When the pipeline runs on an Apache Spark for HDInsight cluster, you must use this stage library.

    • ADLS Transformer-provided libraries for Hadoop 3.2.0 - Transformer passes the necessary libraries with the pipeline to enable running the pipeline.

      Use when running the pipeline locally or when the cluster where the pipeline runs does not include Apache Hadoop Azure Data Lake libraries version 3.2.0.

    Note: When using additional ADLS stages in the pipeline, ensure that they use the same stage library.
  2. On the Azure tab, configure the following properties:
    ADLS Property Description
    Service Name Azure Data Lake Storage Gen1 service name.
    Application ID Application ID for the Azure Active Directory Transformer application. Also known as the client ID.

    When not specified, the stage uses the equivalent Azure authentication information configured in the cluster where the pipeline runs.

    For information on accessing the application ID from the Azure portal, see the Azure documentation.

    Tip: To secure sensitive information, you can use credential stores or runtime resources.
    Application Key Authentication key for the Azure Active Directory Transformer application. Also known as the client key.

    When not specified, the stage uses the equivalent Azure authentication information configured in the cluster where the pipeline runs.

    For information on accessing the application key from the Azure portal, see the Azure documentation.
    Tip: To secure sensitive information, you can use credential stores or runtime resources.
    OAuth Token Endpoint OAuth 2.0 token endpoint for the Azure Active Directory v1.0 application for Transformer. For example: https://login.microsoftonline.com/<uuid>/oauth2/token.

    When not specified, the stage uses the equivalent Azure authentication information configured in the cluster where the pipeline runs.
    Directory Path

    Path to the output directory where the destination writes files. Use the following format:

    /<path to files>/

    The destination creates the directory if it doesn’t exist. The authentication credentials used to connect to Azure must have write access to the root of the specified directory path.
    Write Mode Mode to write files:
    • Overwrite files - Removes all files from the output directory and any subdirectories.
    • Overwrite related partitions - Removes the files from subdirectories for which the batch has data.
    • Write new or append to existing files - Creates new files or appends data to existing files.
    • Write new files to new directory - Creates the output directory and writes new files to that directory.
    Additional Configuration

    Additional HDFS properties to pass to an HDFS-compatible file system. Specified properties override those in Hadoop configuration files.

    To add properties, click the Add icon and define the HDFS property name and value. You can use simple or bulk edit mode to configure the properties. Use the property names and values as expected by your version of Hadoop.

    Partition by Fields Fields used to group partition files. The destination creates a subdirectory for each unique field value and writes a file in the subdirectory for each processed partition that contains the corresponding field and value.

    In a file dimension pipeline that writes to a grouped file dimension, specify the fields that are listed in the Key Fields property of the Slowly Changing Dimension processor.
    Exclude Unrelated SCD Master Records Does not write slowly changing dimension master records that are unrelated to the change data. Writes only the master records related to the change data.

    Selecting this property and setting Write Mode to Overwrite Related Partitions results in the destination overwriting only the files with changes.

    Use only in a slowly changing dimension pipeline that writes to a grouped file dimension. In pipelines that write to an ungrouped file dimension, clear this option to include all master records.

    For guidelines on configuring slowly changing dimension pipelines, see Pipeline Configuration.
  3. On the Data Format tab, configure the following property:
    Data Format Property Description
    Data Format Format of the data. Select one of the following formats:
    • Avro (Spark 2.4 or later) - For Avro data processed by Spark 2.4 or later.
    • Avro (Spark 2.3) - For Avro data processed by Spark 2.3.
    • Delimited
    • JSON
    • ORC
    • Parquet
    • Text
    • XML
  4. For delimited data, on the Data Format tab, configure the following property:
    Delimited Property Description
    Delimiter Character Delimiter character to use in the data. Select one of the available options or select Other to enter a custom character.

    You can enter a Unicode control character using the format \uNNNN, where ​ N is a hexadecimal digit from the numbers 0-9 or the letters A-F. For example, enter \u0000 to use the null character as the delimiter or \u2028 to use a line separator as the delimiter.
    Quote Character Quote character to use in the data.
    Escape Character Escape character to use in the data
  5. For text data, on the Data Format tab, configure the following property:
    Text Property Description
    Text Field String field in the record that contains the data to be written. All data must be incorporated into the specified field.
  6. For XML data, on the Data Format tab, configure the following properties:
    XML Property Description
    Root Tag Tag to use as the root element.

    Default is ROWS, which results in a <ROWS> root element.
    Row Tag Tag to use as a record delineator.

    Default is ROW, which results in a <ROW> record delineator element.