Hadoop FS (deprecated)

The Hadoop FS origin reads data from the Hadoop Distributed File System (HDFS), Amazon S3, or other file systems using the Hadoop FileSystem interface. For information about supported versions, see Supported Systems and Versions in the Data Collector documentation.

Important: This stage is deprecated along with cluster pipelines, and may be removed in a future release. StreamSets recommends using StreamSets Transformer instead. For more information, see the Transformer documentation.

Use this origin only in pipelines configured for one of the following cluster modes:

Cluster batch mode: Cluster batch mode pipelines use a Hadoop FS origin and run on a Cloudera distribution of Hadoop (CDH) or Hortonworks Data Platform (HDP) cluster to process data from HDFS, Amazon S3, or other file systems using the Hadoop FileSystem interface.
Cluster EMR batch mode: Cluster EMR batch mode pipelines use a Hadoop FS origin and run on an Amazon EMR cluster to process data from Amazon S3.

For more information about cluster pipelines, see Cluster Pipelines (deprecated). To read from HDFS in standalone execution mode, use the Hadoop FS Standalone origin.

When you configure the Hadoop FS origin, you specify the input path and data format for the data to be read. You can configure the origin to read from all subdirectories and to generate a single record for records that include multiple objects.

Note: The origin processes files based on file names and locations. Having files with the same name in the same location can cause the origin to skip reading the duplicate files.

The Hadoop FS origin reads compressed data based on file extension for all Hadoop-supported compression codecs. It also generates record header attributes that enable you to use the origins of a record in pipeline processing.

When necessary, you can enable Kerberos authentication. You can also specify a Hadoop user to impersonate, define a Hadoop configuration file directory, and add Hadoop configuration properties as needed.

When the pipeline stops, the 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 data.

Reading from Amazon S3

The Hadoop FS origin included in a cluster batch or cluster EMR batch pipeline allows you to read from Amazon S3.

To read from Amazon S3, specify the appropriate URI for Amazon S3 when you configure the Hadoop FS origin. Use the s3a scheme in the URI. S3A is the active connector maintained by open source Hadoop and is the only connector that works with Hadoop and Amazon S3.

Configure the URI to point to the Amazon S3 bucket to read from, as follows:

s3a://<bucket>

For example:

s3a://WebServer

Then in the Input Paths property, enter the full path to the data to be read within that Amazon S3 bucket. You can enter multiple paths for the Input Paths property, as follows:

For additional requirements when using the Hadoop FS origin to read from Amazon S3, see Amazon S3 Requirements.

Reading from Other File Systems

The Hadoop FS origin included in a cluster batch pipeline allows you to read from file systems other than HDFS using the Hadoop FileSystem interface.

For example, you can use the Hadoop FS origin to read data from Microsoft Azure Data Lake Storage for a cluster batch pipeline if the origin system has the Hadoop FileSystem interface installed.

To read from a file system other than HDFS, perform the following steps:

Make sure the Hadoop FileSystem interface is installed on the file system.
Install all required file system application JAR files as external libraries for the Hadoop FS stage library that you use. See the file system documentation for details about the files to install. For instructions on installing external libraries, see Install External Libraries in the Data Collector documentation.
When you configure the Hadoop FS origin, specify the appropriate URI for the origin system. For example, instead of hdfs://<authority>, to connect to Azure Data Lake Storage, you might use adls://<authority>.

Kerberos Authentication

You can use Kerberos authentication to connect to HDFS. When you use Kerberos authentication, Data Collector uses the Kerberos principal and keytab to connect to HDFS. By default, Data Collector uses the user account who started it to connect.

Note: Cluster EMR batch mode pipelines that read from Amazon S3 do not support Kerberos authentication at this time.

The Kerberos principal and keytab are defined in the Data Collector configuration file, $SDC_CONF/sdc.properties. To use Kerberos authentication, configure all Kerberos properties in the Data Collector configuration file, and then enable Kerberos in the Hadoop FS origin.

For more information about enabling Kerberos authentication for Data Collector, see Kerberos Authentication in the Data Collector documentation.

Using a Hadoop User

Data Collector can either use the currently logged in Data Collector user or a user configured in the Hadoop FS origin to read from HDFS.

A Data Collector configuration property can be set that requires using the currently logged in Data Collector user. When this property is not set, you can specify a user in the origin. For more information about Hadoop impersonation and the Data Collector property, see Hadoop Impersonation Mode in the Data Collector documentation.

Note that the origin uses a different user account to connect to HDFS. By default, Data Collector uses the user account who started it to connect to external systems. When using Kerberos, Data Collector uses the Kerberos principal.

To configure a user in the Hadoop FS origin to read from HDFS, perform the following tasks:

On Hadoop, configure the user as a proxy user and authorize the user to impersonate a Hadoop user.
For more information, see the Hadoop documentation.
In the Hadoop FS origin, on the Hadoop FS tab, configure the Hadoop FS User property.

Hadoop Properties and Configuration Files

You can configure the Hadoop FS origin to use individual Hadoop properties or Hadoop configuration files:

Hadoop configuration files

You can use the following Hadoop configuration files with the Hadoop FS origin:

core-site.xml
hdfs-site.xml
yarn-site.xml
mapred-site.xml

To use Hadoop configuration files:

Store the files or a symlink to the files in the Data Collector resources directory.
In the Hadoop FS origin, specify the location of the files.

Note: For a Cloudera Manager installation, Data Collector automatically creates a symlink to the files named hadoop-conf. Enter hadoop-conf for the location of the files in the Hadoop FS origin.

Individual properties

You can configure individual Hadoop properties in the origin. To add a Hadoop property, you specify the exact property name and the value. The Hadoop FS origin does not validate the property names or values.

Note: Individual properties override properties defined in the Hadoop configuration files.

Record Header Attributes

The Hadoop FS origin creates record header attributes that include information about the originating file for the record.

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.

The Hadoop FS origin creates the following record header attributes:

file - Provides the file path and file name where the record originated.
offset - Provides the file offset in bytes. The file offset is the location in the file where the record originated.

Data Formats

The Hadoop FS origin processes data differently based on the data format that you select. The origin processes the following types of data:

Avro

Generates a record for every Avro record. Includes a precision and scale field attribute for each Decimal field.

The stage includes the Avro schema in an avroSchema record header attribute. You can use one of the following methods to specify the location of the Avro schema definition:

Message/Data Includes Schema - Use the schema in the file.
In Pipeline Configuration - Use the schema that you provide in the stage configuration.
Confluent Schema Registry - Retrieve the schema from Confluent Schema Registry. Confluent Schema Registry is a distributed storage layer for Avro schemas. You can configure the stage to look up the schema in Confluent Schema Registry by the schema ID or subject specified in the stage configuration.

Using a schema in the stage configuration or retrieving a schema from Confluent Schema Registry overrides any schema that might be included in the file and can improve performance.

The stage reads files compressed by Avro-supported compression codecs without requiring additional configuration.

Delimited

Generates a record for each delimited line.

The CSV parser that you choose determines the delimiter properties that you configure and how the stage handles parsing errors. You can specify if the data includes a header line and whether to use it. You can define the number of lines to skip before reading, the character set of the data, and the root field type to use for the generated record.

You can also configure the stage to replace a string constant with null values and to ignore control characters.

For more information about reading delimited data, see Reading Delimited Data.

Text

Generates a record for each line of text or for each section of text based on a custom delimiter.

When a line or section exceeds the maximum line length defined for the origin, the origin truncates it. The origin adds a boolean field named Truncated to indicate if the line was truncated.

For more information about processing text with a custom delimiter, see Text Data Format with Custom Delimiters.

Configuring a Hadoop FS Origin

Configure a Hadoop FS origin in a cluster pipeline to read data from HDFS, Amazon S3, or other file system using the Hadoop FileSystem interface.

In the Properties panel, on the General tab, configure the following properties:


General Property	Description
Name	Stage name.
Description	Optional description.
Stage Library	Library version that you want to use.
On Record Error	Error record handling for the stage: Discard - Discards the record. Send to Error - Sends the record to the pipeline for error handling. Stop Pipeline - Stops the pipeline. Not valid for cluster pipelines.

On the Hadoop FS tab, configure the following properties:


Hadoop FS Property	Description
Hadoop FS URI	Optional URI to use. To read from HDFS, include the HDFS scheme and authority as follows: `<scheme>://<authority>`. For example: `hdfs://nameservice` To read from Amazon S3 or other file systems using the Hadoop FileSystem interface, enter the appropriate URI for the system. For more information, see Reading from Amazon S3 or Reading from Other File Systems. When not configured, the stage uses the URI defined by the fs.defaultFS property in the core-site.xml file.
Input Paths	Location of the input data to be read. Enter the path as follows: `/<path>`. For example: `/user/hadoop/directory`
Include All Subdirectories	Reads from all directories within the specified input path.
Produce Single Record	Generates a single record when a record includes multiple objects.
Kerberos Authentication	Uses Kerberos credentials to connect to HDFS. When selected, uses the Kerberos principal and keytab defined in the Data Collector configuration file, `$SDC_CONF/sdc.properties`. Note: Cluster EMR batch mode pipelines that read from Amazon S3 do not support Kerberos authentication at this time.
Hadoop FS Configuration Directory	Location of the Hadoop configuration files. For a Cloudera Manager installation, enter `hadoop-conf`. For all other installations, use a directory or symlink within the Data Collector resources directory. You can use the following files with the Hadoop FS origin: core-site.xml hdfs-site.xml yarn-site.xml mapred-site.xml Note: Properties in the configuration files are overridden by individual properties defined in the stage.
Hadoop FS User	The Hadoop user to use to read from HDFS. When using this property, make sure HDFS is configured appropriately. When not configured, the pipeline uses the currently logged in Data Collector user. Not configurable when Data Collector is configured to use the currently logged in Data Collector user. For more information, see Hadoop Impersonation Mode in the Data Collector documentation.
Hadoop FS Configuration	Additional Hadoop configuration properties to use. To add properties, click Add and define the property name and value. Use the property names and values as expected by Hadoop.
Max Batch Size (records)	Maximum number of records processed at one time. Honors values up to the Data Collector maximum batch size. Default is 1000. The Data Collector default is 1000.

On the Data Format tab, configure the following property:


Data Format Property	Description
Data Format	Type of data to be read. Use one of the following options: Avro Delimited Text

For Avro data, on the Data Format tab, configure the following properties:


Avro Property	Description
Avro Schema Location	Location of the Avro schema definition to use when processing data: Message/Data Includes Schema - Use the schema in the file. In Pipeline Configuration - Use the schema provided in the stage configuration. Confluent Schema Registry - Retrieve the schema from Confluent Schema Registry. Using a schema in the stage configuration or in Confluent Schema Registry can improve performance.
Avro Schema	Avro schema definition used to process the data. Overrides any existing schema definitions associated with the data. You can optionally use the `runtime:loadResource` function to load a schema definition stored in a runtime resource file.
Schema Registry URLs	Confluent Schema Registry URLs used to look up the schema. To add a URL, click Add and then enter the URL in the following format: `http://<host name>:<port number>`
Basic Auth User Info	User information needed to connect to Confluent Schema Registry when using basic authentication. Enter the key and secret from the `schema.registry.basic.auth.user.info` setting in Schema Registry using the following format: `<key>:<secret>` Tip: To secure sensitive information such as user names and passwords, you can use runtime resources or credential stores. For more information about credential stores, see Credential Stores in the Data Collector documentation.
Lookup Schema By	Method used to look up the schema in Confluent Schema Registry: Subject - Look up the specified Avro schema subject. Schema ID - Look up the specified Avro schema ID. Overrides any existing schema definitions associated with the data.
Schema Subject	Avro schema subject to look up in Confluent Schema Registry. If the specified subject has multiple schema versions, the origin uses the latest schema version for that subject. To use an older version, find the corresponding schema ID, and then set the Look Up Schema By property to Schema ID.
Schema ID	Avro schema ID to look up in Confluent Schema Registry.

For delimited data, on the Data Format tab, configure the following properties:


Delimited Property	Description
Delimiter Format Type	Delimiter format type. Use one of the following options: Default CSV - File that includes comma-separated values. Ignores empty lines in the file. RFC4180 CSV - Comma-separated file that strictly follows RFC4180 guidelines. MS Excel CSV - Microsoft Excel comma-separated file. MySQL CSV - MySQL comma-separated file. Tab-Separated Values - File that includes tab-separated values. PostgreSQL CSV - PostgreSQL comma-separated file. PostgreSQL Text - PostgreSQL text file. Custom - File that uses user-defined delimiter, escape, and quote characters. Multi Character Delimited - File that uses multiple user-defined characters to delimit fields and lines, and single user-defined escape and quote characters. Available when using the Apache Commons parser type.
Header Line	Indicates whether a file contains a header line, and whether to use the header line.
Allow Extra Columns	Allows processing records with more columns than exist in the header line. Available when using the Apache Commons parser to process data with a header line.
Extra Column Prefix	Prefix to use for any additional columns. Extra columns are named using the prefix and sequential increasing integers as follows: `<prefix><integer>`. For example, `_extra_1`. Default is `_extra_`. Available when using the Apache Commons parser to process data with a header line while allowing extra columns.
Max Record Length (chars)	Maximum length of a record in characters. Longer records are not read. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size. Available when using the Apache Commons parser.
Delimiter Character	Delimiter character. Select one of the available options or use 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. Default is the pipe character ( \| ). Available when using the Apache Commons parser with a custom delimiter format.
Multi Character Field Delimiter	Characters that delimit fields. Default is two pipe characters (\|\|). Available when using the Apache Commons parser with the multi-character delimiter format.
Multi Character Line Delimiter	Characters that delimit lines or records. Default is the newline character (\n). Available when using the Apache Commons parser with the multi-character delimiter format.
Escape Character	Escape character. Available when using the Apache Commons parser with the custom or multi-character delimiter format. Also available when using the Univocity parser.
Quote Character	Quote character. Available when using the Apache Commons parser with the custom or multi-character delimiter format. Also available when using the Univocity parser.
Enable Comments	Allows commented data to be ignored for custom delimiter format. Available when using the Apache Commons parser.
Comment Marker	Character that marks a comment when comments are enabled for custom delimiter format. Available when using the Apache Commons parser.
Lines to Skip	Number of lines to skip before reading data.
Compression Format	The compression format of the files: None - Processes only uncompressed files. Compressed File - Processes files compressed by the supported compression formats. Archive - Processes files archived by the supported archive formats. Compressed Archive - Processes files archived and compressed by the supported archive and compression formats.
File Name Pattern within Compressed Directory	For archive and compressed archive files, file name pattern that represents the files to process within the compressed directory. You can use UNIX-style wildcards, such as an asterisk or question mark. For example, .json. Default is , which processes all files.
CSV Parser	Parser to use to process delimited data: Apache Commons - Provides robust parsing and a wide range of delimited format types. Univocity - Can provide faster processing for wide delimited files, such as those with over 200 columns. Default is Apache Commons.
Max Columns	Maximum number of columns to process per record. Available when using the Univocity parser.
Max Character per Column	Maximum number of characters to process in each column. Available when using the Univocity parser.
Skip Empty Lines	Allows skipping empty lines. Available when using the Univocity parser.
Allow Extra Columns	Allows processing records with more columns than exist in the header line. Available when using the Apache Commons parser to process data with a header line.
Extra Column Prefix	Prefix to use for any additional columns. Extra columns are named using the prefix and sequential increasing integers as follows: `<prefix><integer>`. For example, `_extra_1`. Default is `_extra_`. Available when using the Apache Commons parser to process data with a header line while allowing extra columns.
Max Record Length (chars)	Maximum length of a record in characters. Longer records are not read. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size. Available when using the Apache Commons parser.
Ignore Empty Lines	Allows empty lines to be ignored. Available when using the Apache Commons parser with the custom delimiter format.
Root Field Type	Root field type to use: List-Map - Generates an indexed list of data. Enables you to use standard functions to process data. Use for new pipelines. List - Generates a record with an indexed list with a map for header and value. Requires the use of delimited data functions to process data. Use only to maintain pipelines created before 1.1.0.
Parse NULLs	Replaces the specified string constant with null values.
NULL Constant	String constant to replace with null values.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

For text data, on the Data Format tab, configure the following properties:


Text Property	Description
Max Line Length	Maximum number of characters allowed for a line. Longer lines are truncated. Adds a boolean field to the record to indicate if it was truncated. The field name is Truncated. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Use Custom Delimiter	Uses custom delimiters to define records instead of line breaks.
Custom Delimiter	One or more characters to use to define records.
Charset	Character encoding of the files to be processed.
Ignore Control Characters	Removes all ASCII control characters except for the tab, line feed, and carriage return characters.

If using the origin in a cluster EMR batch or cluster batch mode pipeline to read data from Amazon S3, configure the following properties on the S3 tab:


S3 Property	Description
Access Key ID	AWS access key ID.
Secret Access Key	AWS secret access key. The origin uses the access key pair to pass credentials to Amazon Web Services to read from Amazon S3. If using the origin in a cluster EMR batch pipeline, enter the same access key pair that you entered on the EMR tab of the pipeline. For more information, see Configuring Cluster EMR Batch Mode for Amazon S3. Tip: To secure sensitive information such as access key pairs, you can use runtime resources or credential stores. For more information about credential stores, see Credential Stores in the Data Collector documentation.