MapR FS Standalone

The MapR FS Standalone origin reads files in MapR. The origin can use multiple threads to enable the parallel processing of files. The files to be processed must all share a file name pattern and be fully written. Use the MapR FS Standalone origin only in pipelines configured for standalone execution mode.

MapR is now HPE Ezmeral Data Fabric. At times, this documentation uses "MapR" to refer to both MapR and HPE Ezmeral Data Fabric. For information about supported versions, see Supported Systems and Versions in the Data Collector documentation.

Tip: Data Collector provides several MapR origins to address different needs. For a quick comparison chart to help you choose the right one, see Comparing MapR Origins.

When you configure the MapR FS Standalone origin, you define the directory to use, read order, file name pattern, 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.

When using the last-modified timestamp read order, you can configure the origin to read from subdirectories. 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 MapR FS Standalone origin can keep, archive, or delete the file.

When the pipeline stops, the MapR FS Standalone 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.

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 origin 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.

The origin can generate events for an event stream. For more information about dataflow triggers and the event framework, see Dataflow Triggers Overview.

File Name Pattern and Mode

Use a file name pattern to define the files that the MapR FS Standalone origin processes. You can use either a glob pattern or a regular expression to define the file name pattern.

The MapR FS Standalone 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 MapR FS Standalone origin reads files in ascending order based on the timestamp or file name:

Last Modified Timestamp

The MapR FS Standalone 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.

Tip: Avoid moving files using commands that preserve the existing timestamp, such as cp -p. Preserving the existing timestamp can be problematic in some cases, such as moving files across time zones.

When ordering based on timestamp, any files with the same timestamp are read in lexicographically ascending order based on the file names.

For example, when reading files with the log*.json file name pattern, the origin reads the following files in the following order:

`File Name`	`Last Modified`
`log-1.json`	`APR 24 2016 14:03:35`
`log-903.json`	`APR 24 2016 14:05:03`
`log-2.json`	`APR 24 2016 14:45:11`
`log-3.json`	`APR 24 2016 14:45:11`

Notice, the log-2.json and log-3.json files have identical timestamps, and so are processed in lexicographically ascending order based on their file names.

Lexicographically Ascending File Names

The MapR FS Standalone origin can read files in lexicographically ascending order based on file names. Lexicographically ascending order reads the numbers 1 through 11 as follows:

1, 10, 11, 2, 3, 4... 9

For example, when reading files with the web*.log file name pattern, the MapR FS Standalone origin reads the following files in the following order:

web-1.log
web-10.log
web-11.log
web-2.log
web-3.log
web-4.log
web-5.log
web-6.log
web-7.log
web-8.log
web-9.log

To read these files in logical and lexicographically ascending order, you might add leading zeros to the file naming convention as follows:

web-0001.log
web-0002.log
web-0003.log
...
web-0009.log
web-0010.log
web-0011.log

Multithreaded Processing

The MapR FS Standalone 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 5 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 MapR FS Standalone 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 MapR FS Standalone 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.

For example, you configure the MapR FS Standalone origin to read from the /logs/ file directory, select the Last Modified Timestamp read order, and enable reading from subdirectories. The MapR FS Standalone origin reads the following files in the following order based on timestamp, even though the files are written to different subdirectories.

`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 MapR FS Standalone 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.

For example, say you configure the origin to archive processed files to a "processed" archive directory. After successfully reading the files in the example above, it writes them to the following directories:

`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 MapR FS Standalone to ignore one or more existing files in the directory.

When you define a first file to process, the MapR FS Standalone 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 MapR FS Standalone 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 MapR FS Standalone 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.

The MapR FS Standalone origin creates the following record 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 MapR FS Standalone 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.

MapR FS Standalone events can be used in any logical way. For example:

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

Event records generated by the MapR FS Standalone origin have the following event-related record header attributes. Record header attributes are stored as String values:


Record Header Attribute	Description
sdc.event.type	Event type. Uses one of the following types: new-file - Generated when the origin starts processing a new file. finished-file - Generated when the origin completes processing a file. no-more-data - Generated after the origin completes processing all available files and the number of seconds configured for Batch Wait Time has elapsed.
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 MapR FS Standalone origin can generate the following types of event records:

new-file

The MapR FS Standalone origin generates a new-file event record when it starts processing a new file.

New-file event records have the sdc.event.type set to new-file and include the following field:


Event Record Field	Description
filepath	Path and name of the file that the origin started or finished processing.

finished-file

The MapR FS Standalone origin generates a finished-file event record when it finishes processing a file.

Finished-file event records have the sdc.event.type set to finished-file and include the following fields:


Event Record Field	Description
filepath	Path and name of the file that the origin started or finished processing.
record-count	Number of records successfully generated from the file.
error-count	Number of error records generated from the file.

no-more-data

The MapR FS Standalone 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.

No-more-data event records generated by the MapR FS Standalone origin have the sdc.event.type set to no-more-data and include the following fields:


Event Record Field	Description
record-count	Number of records successfully generated since the pipeline started or since the last no-more-data event was created.
error-count	Number of error records generated since the pipeline started or since the last no-more-data event was created.
file-count	Number of files the origin attempted to process. Can include files that were unable to be processed or were not fully processed.

Buffer Limit and Error Handling

The MapR FS Standalone 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.

When a record is larger than the specified limit, the MapR FS Standalone origin processes the source file based on the stage error handling:

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.

Note: You can also check the Data Collector log file for error details.

Kerberos Authentication

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

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 MapR FS Standalone origin.

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

HDFS Properties and Configuration Files

You can configure the MapR FS Standalone origin to use individual HDFS properties or HDFS configuration files:

HDFS configuration files

You can use the following HDFS configuration files with the MapR FS Standalone origin:

core-site.xml
hdfs-site.xml

To use HDFS configuration files:

Store the files or a symlink to the files in the Data Collector resources directory.
In the MapR FS Standalone origin, configure the Configuration Files Directory property to 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 MapR FS Standalone origin.

Individual properties

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

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

Impersonation User

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

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 MapR. 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 MapR FS Standalone origin to read from MapR, 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 MapR FS Standalone origin, on the Connection tab, configure the Impersonation User property.

Data Formats

The MapR FS Standalone origin processes data differently based on the data format. The origin processes the following types of data:

Avro

Generates a record for every Avro record. The origin includes the Avro schema in the avroSchema record header attribute. It also includes a precision and scale field attribute for each Decimal field.

The origin expects each file to contain the Avro schema and uses the schema to process the Avro data.

The origin 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.

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.

When an object exceeds the maximum object length defined for the origin, the origin cannot continue processing data in the file. Records already processed from the file are passed to the pipeline. The behavior of the origin is then based on the error handling configured for the stage:

Discard - The origin continues processing with the next file, leaving the partially-processed file in the directory.
To Error - The origin continues processing with the next file. If a post-processing error directory is configured for the stage, the origin moves the partially-processed file to the error directory. Otherwise, it leaves the file in the directory.
Stop Pipeline - The origin stops the pipeline.

Log

Generates a record for every log line.

When a line exceeds the user-defined maximum line length, the origin truncates longer lines.

You can include the processed log line as a field in the record. If the log line is truncated, and you request the log line in the record, the origin includes the truncated line.

You can define the log format or type to be read.

Protobuf

Generates a record for every protobuf message.

Protobuf messages must match the specified message type and be described in the descriptor file.

When the data for a record exceeds 1 MB, the origin cannot continue processing data in the file. The origin handles the file based on file error handling properties and continues reading the next file.

For information about generating the descriptor file, see Protobuf Data Format Prerequisites.

SDC Record

Generates a record for every record. Use to process records generated by a Data Collector pipeline using the SDC Record data format.

For error records, the origin provides the original record as read from the origin in the original pipeline, as well as error information that you can use to correct the record.

When processing error records, the origin expects the error file names and contents as generated by the original pipeline.

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.

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.

The origin generates two fields: one for a file reference and one for file information. For more information, see Whole File Data Format.

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.

Generated records include XML attributes and namespace declarations as fields in the record by default. You can configure the stage to include them in the record as field attributes.

You can include XPath information for each parsed XML element and XML attribute in field attributes. This also places each namespace in an xmlns record header attribute.

Note: Field attributes and record header attributes are written to destination systems automatically only when you use the SDC RPC data format in destinations. For more information about working with field attributes and record header attributes, and how to include them in records, see Field Attributes and Record Header Attributes.

When a record exceeds the user-defined maximum record length, the origin cannot continue processing data in the file. Records already processed from the file are passed to the pipeline. The behavior of the origin is then based on the error handling configured for the stage:

Discard - The origin continues processing with the next file, leaving the partially-processed file in the directory.
To Error - The origin continues processing with the next file. If a post-processing error directory is configured for the stage, the origin moves the partially-processed file to the error directory. Otherwise, it leaves the file in the directory.
Stop Pipeline - The origin stops the pipeline.

Use the XML data format to process valid XML documents. For more information about XML processing, see Reading and Processing XML Data.

Tip: If you want to process invalid XML documents, you can try using the text data format with custom delimiters. For more information, see Processing XML Data with Custom Delimiters.

Configuring a MapR FS Standalone Origin

Configure a MapR FS Standalone origin to read files in MapR.

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


General Property	Description
Name	Stage name.
Description	Optional description.
Produce Events	Generates event records when events occur. Use for event handling.
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.

On the Connection tab, configure the following properties:


Hadoop FS Property	Description
File System URI	Optional Hadoop URI. To connect to a specific cluster, enter `maprfs:///mapr/<cluster name>`. For example: `maprfs:///mapr/my.cluster.com/` Leave empty to use the default value of `maprfs:///`, which uses the first entry defined in the `$MAPR_HOME/conf/mapr-clusters.conf` file.
Impersonation User	The Hadoop user to impersonate to read from MapR. When using this property, make sure MapR is configured appropriately. When not configured, the pipeline uses the currently logged in Data Collector user. Not configurable when Data Collector is configured to use the currently logged in Data Collector user. For more information, see Hadoop Impersonation Mode in the Data Collector documentation.
Kerberos Authentication	Uses Kerberos credentials to connect to MapR. When selected, uses the Kerberos principal and keytab defined in the Data Collector configuration file, `$SDC_CONF/sdc.properties`.
Configuration Files Directory	Location of the HDFS 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 configuration files: core-site.xml hdfs-site.xml Note: Properties in the configuration files are overridden by individual properties defined in the stage.
Additional 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 MapR.

On the Files tab, configure the following properties:


File Property	Description
File Directory	The directory where source files are stored. Enter an absolute path.
Number of Threads	Number of threads the origin generates and uses for multithreaded processing. Default is 1.
File Name Pattern Mode	Syntax of the file name pattern: Glob Regular Expression
File Name Pattern	Pattern of the file names to process. Use glob patterns or regular expressions based on the specified file name pattern mode.
Read Order	The order to use when reading files: Last-Modified Timestamp - Reads files in ascending order based on the last-modified timestamp. When files have matching timestamps, reads files in lexicographically ascending order based on file names. Lexicographically Ascending File Names - Reads files in lexicographically ascending order based on file name.
Process Subdirectories	Reads files in any subdirectory of the specified file directory. Reads files in ascending order based on the last-modified timestamp, regardless of the location within the file directory. Uses the subdirectory for any configured post-processing directories. Available only when using the Last Modified Timestamp read order.
Batch Size (recs)	Number of records to pass through the pipeline at one time. Honors values up to the Data Collector maximum batch size. Default is 1000. The Data Collector default is 1000.
Batch Wait Time (secs)	Number of seconds to wait before sending a partial or empty batch.
First File to Process	Name of the first file to process. When you do not enter a first file name, the origin reads all files in the directory with the specified file name pattern.
Max Files Soft Limit	Maximum number of files that the origin can add to the processing queue at one time. This value is a soft limit - meaning that the origin can temporarily exceed it. If the origin exceeds this soft limit, the origin starts the spooling period timer. If the number of files in the processing queue goes below the soft limit, the origin adds more files from the directory to the queue. If the number of files in the processing queue remains above the soft limit after the configured spooling period expires, no more files are added to the queue until the queue goes below the soft limit. Configure the soft limit to the expected maximum number of files in the directory. Default is 1000.
Spooling Period (secs)	Number of seconds to continue adding files to the processing queue after the maximum files soft limit has been exceeded. When the spooling period expires, no additional files are added to the processing queue until the queue goes below the soft limit. Default is 5 seconds.
Buffer Limit (KB)	Maximum buffer size. The buffer size determines the size of the record that can be processed. Decrease when memory on the Data Collector machine is limited. Increase to process larger records when memory is available.

On the Post Processing tab, configure the following properties:


Post Processing Property	Description
Error Directory	The directory for files that cannot be fully processed due to data handling errors. When you specify an error directory, files that cannot be fully processed are moved to this directory. Use to manage files for error handling and reprocessing.
File Post Processing	The action taken after processing a file: None - Keeps the file in place. Archive - Moves the file to the archive directory. Delete - Deletes the file.
Archive Directory	The directory for files that are fully processed. When you specify an archive directory, files are moved to this directory after being fully processed. Use to archive processed files.
Archive Retention Time (mins)	Number of minutes processed files are saved in the archive directory. Use 0 to keep archived files indefinitely.

On the Data Format tab, configure the following property:


Data Format Property	Description
Data Format	Data format for source files. Use one of the following formats: Avro Delimited Excel JSON Log Protobuf SDC Record Text Whole File XML

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


Delimited Property	Description
Header Line	Indicates whether a file contains a header line, and whether to use the header line.
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.
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.
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.
Field Separator	One or more characters to use as delimiter characters between columns. Available when using the Univocity parser.
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.
Line Separator	Line separator. Available when using the Univocity parser.
Allow Comments	Allows commented data to be ignored for custom delimiter format. Available when using the Univocity parser.
Comment Character	Character that marks a comment when comments are enabled for custom delimiter format. 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 Excel files, on the Data Format tab, configure the following properties:


Excel Property	Description
Excel Header Option	Indicates whether files include a header row and whether to ignore the header row. A header row must be the first row of a file.
Skip Cells With No Header	Skips processing cells when they do not have a corresponding header value. Available when Excel Header Option is set to With Header Line.
Include Cells With Empty Value	Includes empty cells in records.
Read All Sheets	Reads all sheets in the Excel file.
Import Sheets	Name of sheet to read. Using simple or bulk edit mode, click Add Another to add additional sheets. Available when Read All Sheets is not selected.

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


JSON Property	Description
JSON Content	Type of JSON content. Use one of the following options: JSON array of objects Multiple JSON objects
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.
Max Object Length (chars)	Maximum number of characters in a JSON object. Longer objects are diverted to the pipeline for error handling. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
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 log data, on the Data Format tab, configure the following properties:


Log Property	Description
Log Format	Format of the log files. Use one of the following options: Common Log Format Combined Log Format Apache Error Log Format Apache Access Log Custom Format Regular Expression Grok Pattern Log4j Common Event Format (CEF) Log Event Extended Format (LEEF)
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.
Max Line Length	Maximum length of a log line. The origin truncates longer lines. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
Retain Original Line	Determines how to treat the original log line. Select to include the original log line as a field in the resulting record. By default, the original line is discarded.
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.

When you select Apache Access Log Custom Format, use Apache log format strings to define the Custom Log Format.
When you select Regular Expression, enter the regular expression that describes the log format, and then map the fields that you want to include to each regular expression group.
When you select Grok Pattern, you can use the Grok Pattern Definition field to define custom grok patterns. You can define a pattern on each line.
In the Grok Pattern field, enter the pattern to use to parse the log. You can use a predefined grok patterns or create a custom grok pattern using patterns defined in Grok Pattern Definition.
For more information about defining grok patterns and supported grok patterns, see Defining Grok Patterns.

When you select Log4j, define the following properties:


Log4j Property	Description
On Parse Error	Determines how to handle information that cannot be parsed: Skip and Log Error - Skips reading the line and logs a stage error. Skip, No Error - Skips reading the line and does not log an error. Include as Stack Trace - Includes information that cannot be parsed as a stack trace to the previously-read log line. The information is added to the message field for the last valid log line.
Use Custom Log Format	Allows you to define a custom log format.
Custom Log4J Format	Use log4j variables to define a custom log format.

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


Protobuf Property	Description
Protobuf Descriptor File	Descriptor file (.desc) to use. The descriptor file must be in the Data Collector resources directory, `$SDC_RESOURCES`. For more information about environment variables, see Data Collector Environment Configuration in the Data Collector documentation. For information about generating the descriptor file, see Protobuf Data Format Prerequisites.
Message Type	The fully-qualified name for the message type to use when reading data. Use the following format: `<package name>.<message type>`. Use a message type defined in the descriptor file.
Delimited Messages	Indicates if a file might include more than one protobuf message.
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.

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


SDC Record Property	Description
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.

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


Text Property	Description
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.
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.
Include Custom Delimiter	Includes delimiter characters in the record.
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 whole files, on the Data Format tab, configure the following properties:


Whole File Property	Description
Buffer Size (bytes)	Size of the buffer to use to transfer data.
Rate per Second	Transfer rate to use. Enter a number to specify a rate in bytes per second. Use an expression to specify a rate that uses a different unit of measure per second, e.g. ${5 * MB}. Use -1 to opt out of this property. By default, the origin does not use a transfer rate.

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


XML Property	Description
Delimiter Element	Delimiter to use to generate records. Omit a delimiter to treat the entire XML document as one record. Use one of the following: An XML element directly under the root element. Use the XML element name without surrounding angle brackets ( < > ) . For example, msg instead of <msg>. A simplified XPath expression that specifies the data to use. Use a simplified XPath expression to access data deeper in the XML document or data that requires a more complex access method. For more information about valid syntax, see Simplified XPath Syntax.
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.
Preserve Root Element	Includes the root element in the generated records. When omitting a delimiter to generate a single record, the root element is the root element of the XML document. When specifying a delimiter to generate multiple records, the root element is the XML element specified as the delimiter element or is the last XML element in the simplified XPath expression specified as the delimiter element.
Include Field XPaths	Includes the XPath to each parsed XML element and XML attribute in field attributes. Also includes each namespace in an xmlns record header attribute. When not selected, this information is not included in the record. By default, the property is not selected. Note: Field attributes and record header attributes are written to destination systems automatically only when you use the SDC RPC data format in destinations. For more information about working with field attributes and record header attributes, and how to include them in records, see Field Attributes and Record Header Attributes.
Namespaces	Namespace prefix and URI to use when parsing the XML document. Define namespaces when the XML element being used includes a namespace prefix or when the XPath expression includes namespaces. For information about using namespaces with an XML element, see Using XML Elements with Namespaces. For information about using namespaces with XPath expressions, see Using XPath Expressions with Namespaces. Using simple or bulk edit mode, click the Add icon to add additional namespaces.
Output Field Attributes	Includes XML attributes and namespace declarations in the record as field attributes. When not selected, XML attributes and namespace declarations are included in the record as fields. Note: Field attributes are automatically included in records written to destination systems only when you use the SDC RPC data format in the destination. For more information about working with field attributes, see Field Attributes. By default, the property is not selected.
Max Record Length (chars)	The maximum number of characters in a record. Longer records are diverted to the pipeline for error handling. This property can be limited by the Data Collector parser buffer size. For more information, see Maximum Record Size.
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.