Azure Data Lake Storage Gen2 (Legacy)
The Azure Data Lake Storage Gen2 (Legacy) origin uses the Hadoop FileSystem interface to read data from Microsoft Azure Data Lake Storage Gen2. The origin can create multiple threads to enable parallel processing in a multithreaded pipeline. For information about supported versions, see Supported Systems and Versions.
The files to be processed must all share a file name pattern and be fully written. Use the origin only in pipelines configured for standalone execution mode.
When you configure the Azure Data Lake Storage Gen2 (Legacy) origin, you define the directory to use, the read order, the file name pattern, the file name pattern mode, and the first file to process. You can use glob patterns or regular expressions to define the file name pattern that you want to use.
You can configure the origin to read from subdirectories when the origin reads files by last modified timestamp. To use multiple threads for processing, specify the number of threads to use.
You can also enable reading compressed files. After processing a file, the Azure Data Lake Storage Gen2 (Legacy) origin can keep, archive, or delete the file.
When a pipeline stops, the Azure Data Lake Storage Gen2 (Legacy) origin notes where it stops reading. When the pipeline starts again, the origin continues processing from where it stopped by default. You can reset the origin to process all requested files.
The origin generates record header attributes that enable you to use the origins of a record in pipeline processing.
You can also use a connection to configure the origin.
The origin can generate events for an event stream. For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.
Prerequisites
- If necessary, create a new Azure Active Directory
application for Data Collector.
For information about creating a new application, see the Azure documentation.
- Ensure that the Azure Active Directory Data Collector application
has the appropriate access control to perform the necessary
tasks.
The Data Collector application requires Read and Execute permissions to read data in Azure. If also writing to Azure, the application requires Write permission as well.
For information about configuring Gen2 access control, see the Azure documentation.
- Retrieve information from Azure to configure the origin.
After you complete all of the prerequisite tasks, you can configure the Azure Data Lake Storage Gen2 (Legacy) origin.
Retrieve Authentication Information
The Azure Data Lake Storage Gen2 (Legacy) origin can use different methods to authenticate connections with Azure.
- OAuth with Service Principal
- Connections made with OAuth with Service Principal authentication require
the following information:
- Application ID - Application ID for the Azure Active Directory Data Collector
application. Also known as the client ID.
For information on accessing the application ID from the Azure portal, see the Azure documentation.
- Tenant ID - Tenant ID for the Azure Active Directory
Data Collector application. Also known as the directory ID.
For information on accessing the tenant ID from the Azure portal, see the Azure documentation.
- Application Key - Authentication key or client secret
for the Azure Active Directory application. Also known as the
client secret.
For information on accessing the application key from the Azure portal, see the Azure documentation.
- Application ID - Application ID for the Azure Active Directory Data Collector
application. Also known as the client ID.
- Azure Managed Identity
- Connections made with Azure Managed Identity authentication
require the following information:
- Application ID - Application ID for the Azure Active Directory Data Collector
application. Also known as the client ID.
For information on accessing the application ID from the Azure portal, see the Azure documentation.
- Application ID - Application ID for the Azure Active Directory Data Collector
application. Also known as the client ID.
- Shared Key
- Connections made with Shared Key authentication require the following
information:
- Account Shared Key - Shared access key that Azure
generated for the storage account.
For more information on accessing the shared access key from the Azure portal, see the Azure documentation.
- Account Shared Key - Shared access key that Azure
generated for the storage account.
File Directory
To define the directory that the Azure Data Lake Storage Gen2 (Legacy) origin reads files from, enter an absolute directory. Use a glob pattern to include wildcards and define multiple directories to read files from.
/hr/employees/CA/SFO
/hr/employees/CA/SJC
/hr/employees/CA/LAX
/hr/employees/WA/SEA
Folders to Read | File Directory Defined |
---|---|
/hr/employees/CA/SFO /hr/employees/CA/SJC /hr/employees/CA/LAX |
/hr/employees/CA/* |
/hr/employees/CA/SFO /hr/employees/CA/SJC |
/hr/employees/CA/S* |
/hr/employees/CA/SFO /hr/employees/CA/SJC /hr/employees/WA/SEA |
/hr/employees/*/S* |
For more information about glob patterns, see the Oracle Java documentation.
File Name Pattern and Mode
Use a file name pattern to define the files that the Azure Data Lake Storage Gen2 (Legacy) origin processes. You can use either a glob pattern or a regular expression to define the file name pattern.
The
Azure Data Lake Storage Gen2 (Legacy) origin processes files based on the file name pattern mode, file
name pattern, and specified directory. For example, if you specify a
/logs/weblog/
directory, glob mode, and *.json
as the file name pattern, the origin processes all files with the
json
extension in the /logs/weblog/
directory.
The origin processes files in order based on the specified read order.
For more information about glob syntax, see the Oracle Java documentation. For more information about regular expressions, see Regular Expressions Overview.
Read Order
The Azure Data Lake Storage Gen2 (Legacy) origin reads files in ascending order based on the timestamp or file name:
- Last Modified Timestamp
- The Azure Data Lake Storage Gen2 (Legacy) origin can read files in ascending order based on the last modified timestamp associated with the file. When the origin reads from a secondary location - not the directory where the files are created and written - the last-modified timestamp should be when the file is moved to the directory to be processed.
- Lexicographically Ascending File Names
- The Azure Data Lake Storage Gen2 (Legacy) origin can read files in lexicographically ascending order based on file names. Lexicographically ascending order reads the numbers 1 through 11 as follows:
Multithreaded Processing
The Azure Data Lake Storage Gen2 (Legacy) origin uses multiple concurrent threads to process data based on the Number of Threads property.
Each thread reads data from a single file, and each file can have a maximum of one thread read from it at a time. The file read order is based on the configuration for the Read Order property.
As the pipeline runs, each thread connects to the origin system, creates a batch of data, and passes the batch to an available pipeline runner. A pipeline runner is a sourceless pipeline instance - an instance of the pipeline that includes all of the processors, executors, and destinations in the pipeline and handles all pipeline processing after the origin.
Each pipeline runner processes one batch at a time, just like a pipeline that runs on a single thread. When the flow of data slows, the pipeline runners wait idly until they are needed, generating an empty batch at regular intervals. You can configure the Runner Idle Time pipeline property to specify the interval or to opt out of empty batch generation.
Multithreaded pipelines preserve the order of records within each batch, just like a single-threaded pipeline. But since batches are processed by different pipeline runners, the order that batches are written to destinations is not ensured.
For example, say you configure the origin to read files from a directory using five threads and the Last Modified Timestamp read order. When you start the pipeline, the origin creates five threads, and Data Collector creates a matching number of pipeline runners.
The Azure Data Lake Storage Gen2 (Legacy) origin assigns a thread to each of the five oldest files in the directory. Each thread processes its assigned file, passing batches of data to the origin. Upon receiving data, the origin passes a batch to each of the pipeline runners for processing.
After each thread completes processing a file, it continues to the next file based on the last-modified timestamp, until all files are processed.
For more information about multithreaded pipelines, see Multithreaded Pipeline Overview.
Reading from Subdirectories
When using the Last Modified Timestamp read order, the Azure Data Lake Storage Gen2 (Legacy) origin can read files in subdirectories of the specified file directory.
When you configure the origin to read from subdirectories, it reads files from all subdirectories. It reads files in ascending order based on timestamp, regardless of the location of the file within the directory.
File Name
|
Directory
|
Last Modified Timestamp
|
log-1.json
|
/logs/west/
|
APR 24 2016 14:03:35
|
log-0054.json
|
/logs/east/
|
APR 24 2016 14:05:03
|
log-0055.json
|
/logs/west/
|
APR 24 2016 14:45:11
|
log-2.json
|
/logs/
|
APR 24 2016 14:45:11
|
Post-Processing Subdirectories
When the Azure Data Lake Storage Gen2 (Legacy) origin reads from subdirectories, it uses the subdirectory structure when archiving files during post-processing.
You can archive files when the origin completes processing a file or when it cannot fully process a file.
File Name
|
Archive Directory
|
log-1.json
|
/processed/logs/west/
|
log-0054.json
|
/processed/logs/east/
|
log-0055.json
|
/processed/logs/west/
|
log-2.json
|
/processed/logs/
|
First File for Processing
Configure a first file for processing when you want the Azure Data Lake Storage Gen2 (Legacy) origin to ignore one or more existing files in the directory.
When you define a first file to process, the Azure Data Lake Storage Gen2 (Legacy) origin starts processing with the specified file and continues based on the read order and file name pattern. When you do not specify a first file, the origin processes all files in the directory that match the file name pattern.
For example, say the Azure Data Lake Storage Gen2 (Legacy) origin reads files based on last-modified timestamp. To ignore all files older than a particular file, use that file name as the first file to process.
Similarly, say you have the origin reading files based on lexicographically ascending file names, and the file directory includes the following files: web_001.log, web_002.log, web_003.log.
If you configure web_002.log as the first file, the origin reads web_002.log and continues to web_003.log. It skips web_001.log.
Record Header Attributes
The Azure Data Lake Storage Gen2 (Legacy) origin creates record header attributes that include information about the originating file for the record.
When
the origin processes Avro data, it includes the Avro schema in
an avroSchema
record header attribute.
You can use the record:attribute
or
record:attributeOrDefault
functions to access the information
in the attributes. For more information about working with record header attributes,
see Working with Header Attributes.
- avroSchema - When processing Avro data, provides the Avro schema.
- baseDir - Base directory containing the file where the record originated.
- filename - Provides the name of the file where the record originated.
- file - Provides the file path and file name where the record originated.
- mtime - Provides the last-modified time for the file.
- offset - Provides the file offset in bytes. The file offset is the location in the file where the record originated.
- atime - Provides the last accessed time.
- isDirectory - Indicates if the file is a directory.
- isSymbolicLink - Indicates if the file is a symbolic link.
- size - Provides the file size.
- owner - Provides the file owner.
- group - Provides the group associated with the file.
- blocksize - Provides the block size of the file.
- replication - Provides the replication of the file.
- isEncrypted - Indicates if the file is encrypted.
Event Generation
The Azure Data Lake Storage Gen2 (Legacy) origin can generate events that you can use in an event stream. When you enable event generation, the origin generates event records each time the origin starts or completes reading a file. It can also generate events when it completes processing all available data and the configured batch wait time has elapsed.
- With the Pipeline Finisher executor to
stop the pipeline and transition the pipeline to a Finished state when
the origin completes processing available data.
When you restart a pipeline stopped by the Pipeline Finisher executor, the origin continues processing from the last-saved offset unless you reset the origin.
For an example, see Stopping a Pipeline After Processing All Available Data.
- With the Email executor to send a custom email
after receiving an event.
For an example, see Sending Email During Pipeline Processing.
- With a destination to store event information.
For an example, see Preserving an Audit Trail of Events.
For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.
Event Records
Record Header Attribute | Description |
---|---|
sdc.event.type | Event type. Uses one of the following types:
|
sdc.event.version | Integer that indicates the version of the event record type. |
sdc.event.creation_timestamp | Epoch timestamp when the stage created the event. |
The Azure Data Lake Storage Gen2 (Legacy) origin can generate the following types of event records:
- new-file
- The Azure Data Lake Storage Gen2 (Legacy) origin generates a new-file event record when it starts processing a new file.
- finished-file
- The Azure Data Lake Storage Gen2 (Legacy) origin generates a finished-file event record when it finishes processing a file.
- no-more-data
- The Azure Data Lake Storage Gen2 (Legacy) origin generates a no-more-data event record when the origin completes processing all available records and the number of seconds configured for Batch Wait Time elapses without any new files appearing to be processed.
Buffer Limit and Error Handling
The Azure Data Lake Storage Gen2 (Legacy) origin passes each record to a buffer. The size of the buffer determines the maximum size of the record that can be processed. Decrease the buffer limit when memory on the Data Collector machine is limited. Increase the buffer limit to process larger records when memory is available.
- Discard
- The origin discards the record and all remaining records in the file, and then continues processing the next file.
- Send to Error
- With a buffer limit error, the origin cannot send the record to the pipeline
for error handling because it is unable to fully process the record.
Instead, the origin creates a message stating that a buffer overrun error occurred. The message includes the file and offset where the buffer overrun error occurred. The information displays in the pipeline history.
If an error directory is configured for the stage, the origin moves the file to the error directory and continues processing the next file.
- Stop Pipeline
- The origin stops the pipeline and creates a message stating that a buffer overrun error occurred. The message includes the file and offset where the buffer overrun error occurred. The information displays in the pipeline history.
Data Formats
- Avro
- Generates a record for every Avro record. The origin includes the Avro schema in the
avroSchema
record header attribute. It also includes aprecision
andscale
field attribute for each Decimal field. - Delimited
- Generates a record for each delimited line.
- Excel
- Generates a record for every row in the file. Can process
.xls
or.xlsx
files.You can configure the origin to read from all sheets in a workbook or from particular sheets in a workbook. You can specify whether files include a header row and whether to ignore the header row. You can also configure the origin to skip cells that do not have a corresponding header value. A header row must be the first row of a file. Vertical header columns are not recognized.
The origin cannot process Excel files with large numbers of rows. You can save such files as CSV files in Excel, and then use the origin to process with the delimited data format.
- JSON
- Generates a record for each JSON object. You can process JSON files that include multiple JSON objects or a single JSON array.
- Log
- Generates a record for every log line.
- Protobuf
- Generates a record for every protobuf message.
- SDC Record
- Generates a record for every record. Use to process records generated by a Data Collector pipeline using the SDC Record data format.
- Text
- Generates a record for each line of text or for each section of text based on a custom delimiter.
- Whole File
- Streams whole files from the origin system to the destination system. You can specify a transfer rate or use all available resources to perform the transfer.
- XML
- Generates records based on a user-defined delimiter element. Use an XML element directly under the root element or define a simplified XPath expression. If you do not define a delimiter element, the origin treats the XML file as a single record.
Configuring an Azure Data Lake Storage Gen2 (Legacy) Origin
Configure an Azure Data Lake Storage Gen2 (Legacy) origin to read data from Azure Data Lake Storage Gen2. Be sure to complete the necessary prerequisites before you configure the origin.