MapR Multitopic Streams Consumer
The MapR Multitopic Streams Consumer origin reads data from multiple topics in a MapR Streams cluster. The origin can use multiple threads to enable parallel processing of data.
When you configure a MapR Multitopic Streams Consumer, you configure the consumer group name, the topics to process, and the number of threads to use.
You can configure the origin to produce a single record when a message includes multiple objects. And you can add additional MapR Streams and supported Kafka configuration properties as needed.
When processing Avro data, you can configure the MapR Multitopic Streams Consumer to work with the Confluent Schema Registry. The Confluent Schema Registry is a distributed storage layer for Avro schemas which uses MapR Streams as its underlying storage mechanism.
MapR Multitopic Streams Consumer includes record header attributes that enable you to use information about the record in pipeline processing.
Before you use any MapR stage in a pipeline, you must perform additional steps to enable Data Collector to process MapR data. For more information, see MapR Prerequisites in the Data Collector documentation.
Initial and Subsequent Offsets
When you start a pipeline for the first time, the MapR Multitopic Streams Consumer becomes a new consumer group for each specified topic.
By default, the origin reads only incoming data, processing data from all partitions and ignoring any existing data in the topic. After the origin passes data to destinations, it saves the offset with MapR Streams. When you stop and restart the pipeline, processing continues based on the offset.
Processing All Unread Data
You can configure the MapR Multitopic Streams Consumer origin to read all unread data in a topic. By default, the MapR Multitopic Streams Consumer origin reads only incoming data.
auto.offset.reset MapR
Streams configuration property to the origin:- On the Connection tab, click the Add
icon to add a new MapR Streams configuration property.
You can use simple or bulk edit mode to add configuration properties.
- For the property name, enter auto.offset.reset.
- Set the value for the
auto.offset.resetproperty to earliest.
For more information about auto.offset.reset, see the MapR Streams
documentation.
Multithreaded Processing
The MapR Multitopic Streams Consumer origin performs parallel processing and enables the creation of a multithreaded pipeline. The MapR Multitopic Streams Consumer origin uses multiple concurrent threads based on the Number of Threads property. MapR Streams distributes partitions equally among all the consumers in a group.
When performing multithreaded processing, the MapR Multitopic Streams Consumer origin checks the list of topics to process and creates the specified number of threads. Each thread connects to MapR Streams and creates a batch of data from a partition assigned by MapR Streams. Then, it 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 set the Number of Threads property to 5. When you start the pipeline, the origin creates five threads, and Data Collector creates a matching number of pipeline runners. The threads are assigned to different partitions as defined by MapR Streams. Upon receiving data, the origin passes a batch to each of the pipeline runners for processing.
At any given moment, the five pipeline runners can each process a batch, so this multithreaded pipeline processes up to five batches at a time. When incoming data slows, the pipeline runners sit idle, available for use as soon as the data flow increases.
For more information about multithreaded pipelines, see Multithreaded Pipeline Overview. For more information about the MapR Streams, see the MapR Streams documentation.
Additional Properties
You can add custom configuration properties to the MapR Multitopic Streams Consumer. You can use any MapR or Kafka property supported by MapR Streams. For more information, see the MapR Streams documentation.
When you add a configuration property, enter the exact property name and the value. The MapR Multitopic Streams Consumer does not validate the property names or values.
If custom configurations conflict with other stage properties, the stage generates an error unless you select the Override Stage Configurations check box. With the check box selected, the custom configurations override other stage properties. For information about the necessary properties, see the MapR documentation.
- auto.commit.interval.ms
- enable.auto.commit
- group.id
Record Header Attributes
The MapR Multitopic Streams Consumer 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.
- offset - The offset where the record originated.
- partition - The partition where the record originated.
- topic - The topic where the record originated.
Data Formats
The MapR Multitopic Streams Consumer origin processes data differently based on the data format. MapR Multitopic Streams Consumer can process the following types of data:
- Avro
- Generates a record for every message. Includes a
precisionandscalefield attribute for each Decimal field. - Binary
- Generates a record with a single byte array field at the root of the record.
- Datagram
- Generates a record for every message. The origin can process collectd messages, NetFlow 5 and NetFlow 9 messages, and the following types of syslog messages:
- Delimited
- Generates a record for each delimited line.
- 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. By default, the origin assumes messages contain multiple protobuf messages.
- 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.
- 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 a MapR Multitopic Streams Consumer Origin
-
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.
-
On the Connection tab, configure the following
properties:
MapR Multitopic Streams Property Description Consumer Group Consumer group that the Data Collector belongs to. Topic List The topics to read. Use the following format: <path to stream>:<topic>Click Add to add additional topics.
Tip: You can use Raw Preview to generate a list of topics associated with a broker. The list displays in a format that you can use to list the topics in bulk edit mode.Produce Single Record For each partition, generates a single record for records that include multiple objects. When not selected, the origin generates multiple records when a record includes multiple objects.
Number of Threads The number of threads the origin generates and uses for multithreaded processing. MapR Streams distributes partitions equally among all the consumers in a group. For more information, see Multithreaded Processing. 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.
Max Batch Wait Time (ms) Number of milliseconds to wait before sending a partial or empty batch. Configuration Properties Additional configuration properties to use. Using simple or bulk edit mode, click the Add icon to add properties. Use the property names and values as expected by MapR Streams.
You can use MapR Streams properties and the set of Kafka properties supported by MapR Streams.
Override Stage Configurations When configurations conflict, the properties configured in the Configuration Properties property override other properties configured in the stage. -
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
- Binary
- Delimited
- JSON
- Log
- Text
- Protobuf
- SDC Record
- XML
-
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 message.
- 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:loadResourcefunction 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.infosetting 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.
- Embedded Schema ID - Look up the Avro schema ID embedded in each message.
Schema Subject Avro schema subject to look up in Confluent Schema Registry. If the specified subject has multiple schema versions, the stage 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 binary data, on the Data Format tab and configure the
following property:
Binary Property Description Max Data Size (bytes) Maximum number of bytes in the message. Larger messages cannot be processed or written to error. -
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. 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. 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 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
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)
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 information about generating the descriptor file, see Protobuf Data Format Prerequisites. For more information about environment variables, see Data Collector Environment Configuration in the Data Collector documentation.
Message Type The fully-qualified name for the message type to use when reading data. Use the following format:
Use a message type defined in the descriptor file.<package name>.<message type>.Delimited Messages Indicates if a message might include more than one protobuf message. -
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. 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 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.
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.
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. - An XML element directly under the root element.