MDM - Relate 360
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setup_realtime.sh --config=configuration_file_name --rule=matching_rules_file_name --resumeFrom=checkpoint_value [--consolidate=consolidation_rules_file_name] [--instanceName=instance_name] [--zookeeper=zookeeper_connection_string] [--skipCreateTopic] [--sparkMaster=deployment_mode] [--sparkMicroBatchDuration=batch_processing_time] [--checkpointDirectory=directory_to_store_checkpoint] [--partitions=number_of_partitions] [--replica=number_of_replicas] [--outputTopic=output_topic_name] [--driverMemory=driver_memory] [--executorMemory=executor_memory] [--sparkNumExecutors=number_of_executors] [--sparkNumCoresPerExecutor=number_of_cores] [--sparkAppJars=list_of_application_jars] [--sparkDriverJar=driver_jar] [--maxInputRatePerPartition=number_of_records_per_partition] [--sparkMicroBatchDuration=batch_duration] [--enableBackPressure]
| Option | Argument | Description |
|---|---|---|
| --config | configuration_file_name | Absolute path and file name of the configuration file.
Ensure that the configuration file is present in the same directory path in all the Spark nodes.
|
| --rule | matching_rules_file_name | Absolute path and file name of the matching rules file.
The values in the matching rules file override the values in the configuration file.
Ensure that the matching rules file is present in the same directory path in all the Spark nodes.
|
| --resumeFrom | checkpoint_value | Required when you deploy
Relate 360 on Spark for the first time. Indicates the checkpoint from which the Spark instance must recover after a failure. The value of the
--resumeFrom parameter overrides the checkpoint information that the ZooKeeper directory stores.
Configure one of the following values for the
--resumeFrom parameter:
Use the
--resumeFrom parameter with caution because the Spark instance might reprocess the input records based on the value that you specify.
|
| --consolidate | consolidation_rules_file_name | Optional.
Absolute path and file name of the consolidation rules file. Use the consolidation rules file only when you want to consolidate the linked data and create preferred records for all the clusters. |
| --instanceName | instance_name | Optional. Name for the Spark instance that processes the input data.
Default is
BDRMRTIngestSpark.
|
| --zookeeper | zookeeper_connection_string | Optional. Connection string to access the ZooKeeper server.
Use the following format for the connection string:
<Host Name>:<Port>[/<chroot>] The connection string uses the following parameters:
The following example connection string uses the default ZooKeeper root directory:
server1.domain.com:2182 The following example connection string uses the user-defined ZooKeeper root directory:
server1.domain.com:2182/kafkaroot If you use an ensemble of ZooKeeper servers, you can specify multiple ZooKeeper servers separated by commas.
|
| --skipCreateTopic | Required if the topic that you specify in the configuration file already exists in Kafka. Indicates to skip creating the topic.
By default, the script creates the topic.
| |
| --partitions | number_of_partitions | Optional. Number of partitions for the topic. Use partitions to split the data in the topic across multiple brokers. Default is 1. Ensure that the number of partitions is equal to the number of node managers in the cluster. |
| --replica | number_of_replicas | Optional. Number of replicas that you want to create for the topic. Use replicas for high availability purposes.
Default is 1.
|
| --sparkMaster | deployment_mode | Indicates whether the Spark runs in the standalone or cluster mode.
Use one of the following values:
Default is
local[*].
|
| --sparkMicroBatchDuration | batch_processing_time | Number of seconds to wait before attempting to poll the next batch of data.
Default is 2 seconds.
|
| --checkpointDirectory | directory_to_store_checkpoint | Absolute path to a HDFS directory or a shared NFS directory to store the checkpoint-related information. Spark uses the checkpoint-related information when a node recovers from a failure.
For example, the following sample directory path stores the checkpoint-related information in HDFS:
hdfs:///user/spark/checkpoint |
| --outputTopic | output_topic_name | Optional. Name of the topic in Kafka to which you want to publish the output messages. By default, the output messages are not published.
The script does not create the output topic, so ensure that you create the output topic to publish the output messages to it.
|
| --driverMemory | driver_memory | Optional. Amount of memory in gigabytes that you want to allocate to the driver process of the Spark instance. Default is 1g. |
| --executorMemory | executor_memory | Optional. Amount of memory in gigabytes that you want to allocate to each executor process of the Spark instance. Default is 1g. |
| --sparkNumExecutors | number_of_executors | Optional. Number of executor processes that you want to use for the Spark instance. By default, the number of executor processes depends on the data size and the number of node managers in the cluster.
Applicable only when you run the Spark instance on YARN.
|
| --sparkNumCoresPerExecutor | number_of_cores | Optional. Number of cores for each executor process to use. Default is 1. |
| --sparkAppJars | list_of_application_jars | Optional. Comma-separated list of library JAR files and their paths that you want to include in the driver and executor class paths.
You can specify the following JAR files:
|
| --sparkDriverJar | driver_jar | Optional. Name and path of the
bdrm-rt-ingest-spark-10.0.HF5.jar file to include in the driver and executor class paths.
You can find the
bdrm-rt-ingest-spark-10.0.HF5.jar file in the following directory:
/usr/local/mdmbdrm-<Version Number>/bin |
| --maxInputRatePerPartition | number_of_records_per_partition | Optional. Maximum number of records that the Spark instance can read from each Kafka partition. By default, the Spark instance reads all the records. |
| --sparkMicroBatchDuration | batch_duration | Optional. Number of seconds for the Spark instance to wait before packaging the input records into a batch. Default is 2.
When you kill a batch in the Spark web UI, the Spark instance skips the unprocessed records in the batch.
|
| --enableBackPressure | Optional. Indicates to enable the internal backpressure mechanism of Spark. The mechanism controls the receiving rate of the streaming data. By default, the internal backpressure mechanism is disabled. |
setup_realtime.sh --config=/usr/local/conf/config_big.xml --rule=/usr/local/conf/matching_rules.xml --resumeFrom=smallest --instanceName=Prospects --zookeeper=10.28.10.345 --partitions=3 --replica=2 --sparkMaster=yarn --sparkMicroBatchDuration=5 --checkpointDirectory=hdfs:///user/spark/checkpoint --outputTopic=InsuranceOutput --driverMemory=2g --executorMemory=2g --sparkNumExecutors=3 --sparkNumCoresPerExecutor=2 -–sparkAppJars=$sparkDriverLibraryPath/ssan3.jar,$sparkDriverLibraryPath/BDRMRTProcessor.jar,$sparkDriverLibraryPath/fastutil-7.0.2.jar, $sparkDriverLibraryPath/htrace-core.jar,$sparkDriverLibraryPath/guava-12.0.1.jar --sparkDriverJar=$sparkDriverLibraryPath/bdrm-rt-ingest-spark-10.0.HF5.jar --maxInputRatePerPartition=40 --sparkMicroBatchDuration=10 –-enableBackPressure
