89 lines
3.0 KiB
Plaintext
89 lines
3.0 KiB
Plaintext
[DEFAULTS]
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[repositories]
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offsets = monasca_transform.mysql_offset_specs:MySQLOffsetSpecs
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data_driven_specs = monasca_transform.data_driven_specs.mysql_data_driven_specs_repo:MySQLDataDrivenSpecsRepo
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offsets_max_revisions = 10
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[database]
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server_type = mysql:thin
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host = localhost
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database_name = monasca_transform
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username = m-transform
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password = password
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[messaging]
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adapter = monasca_transform.messaging.adapter:KafkaMessageAdapter
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topic = metrics
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brokers=192.168.15.6:9092
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publish_region = useast
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publish_kafka_project_id=d2cb21079930415a9f2a33588b9f2bb6
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adapter_pre_hourly = monasca_transform.messaging.adapter:KafkaMessageAdapterPreHourly
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topic_pre_hourly = metrics_pre_hourly
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[stage_processors]
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pre_hourly_processor_enabled = True
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[pre_hourly_processor]
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late_metric_slack_time = 600
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enable_instance_usage_df_cache = True
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instance_usage_df_cache_storage_level = MEMORY_ONLY_SER_2
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enable_batch_time_filtering = True
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data_provider=monasca_transform.processor.pre_hourly_processor:PreHourlyProcessorDataProvider
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effective_batch_revision=2
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#
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# Configurable values for the monasca-transform service
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#
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[service]
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# The address of the mechanism being used for election coordination
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coordinator_address = kazoo://localhost:2181
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# The name of the coordination/election group
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coordinator_group = monasca-transform
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# How long the candidate should sleep between election result
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# queries (in seconds)
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election_polling_frequency = 15
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# Whether debug-level log entries should be included in the application
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# log. If this setting is false, info-level will be used for logging.
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enable_debug_log_entries = true
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# The path for the monasca-transform Spark driver
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spark_driver = /opt/monasca/transform/lib/driver.py
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# the location for the transform-service log
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service_log_path=/var/log/monasca/transform/
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# the filename for the transform-service log
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service_log_filename=monasca-transform.log
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# Whether Spark event logging should be enabled (true/false)
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spark_event_logging_enabled = true
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# A list of jars which Spark should use
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spark_jars_list = /opt/spark/current/assembly/target/scala-2.10/jars/spark-streaming-kafka-0-8_2.10-2.2.0.jar,/opt/spark/current/assembly/target/scala-2.10/jars/scala-library-2.10.6.jar,/opt/spark/current/assembly/target/scala-2.10/jars/kafka_2.10-0.8.1.1.jar,/opt/spark/current/assembly/target/scala-2.10/jars/metrics-core-2.2.0.jar,/opt/spark/current/assembly/target/scala-2.10/jars/drizzle-jdbc-1.3.jar
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# A list of where the Spark master(s) should run
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spark_master_list = spark://localhost:7077
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# spark_home for the environment
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spark_home = /opt/spark/current
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# Python files for Spark to use
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spark_python_files = /opt/monasca/transform/lib/monasca-transform.zip
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# How often the stream should be read (in seconds)
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stream_interval = 600
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# The working directory for monasca-transform
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work_dir = /var/run/monasca/transform
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# enable caching of record store df
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enable_record_store_df_cache = True
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# set spark storage level for record store df cache
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record_store_df_cache_storage_level = MEMORY_ONLY_SER_2
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