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76 lines
3.4 KiB
Python
76 lines
3.4 KiB
Python
#! /usr/bin/python
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import sys
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import time
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from pyhive import hive
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from TCLIService.ttypes import TOperationState
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HIVESTORE='localhost'
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AVROLOADPATH = '../../metadata-events/mxe-schemas/src/renamed/avro/com/linkedin/mxe/MetadataChangeEvent.avsc'
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KAFKATOPIC = 'MetadataChangeEvent'
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BOOTSTRAP = 'localhost:9092'
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SCHEMAREGISTRY = 'http://localhost:8081'
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def hive_query(query):
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"""
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Execute the query to the HiveStore.
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"""
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cursor = hive.connect(HIVESTORE).cursor()
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cursor.execute(query, async=True)
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status = cursor.poll().operationState
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while status in (TOperationState.INITIALIZED_STATE, TOperationState.RUNNING_STATE):
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logs = cursor.fetch_logs()
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for message in logs:
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sys.stdout.write(message)
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status = cursor.poll().operationState
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results = cursor.fetchall()
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return results
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def build_hive_dataset_mce(dataset_name, schema, metadata):
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"""
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Create the MetadataChangeEvent via dataset_name and schema.
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"""
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actor, type, created_time, upstreams_dataset, sys_time = "urn:li:corpuser:" + metadata[2][7:], str(metadata[-1][11:-1]), long(metadata[3][12:]), metadata[-28][10:], long(time.time())
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owners = {"owners":[{"owner":actor,"type":"DATAOWNER"}],"lastModified":{"time":sys_time,"actor":actor}}
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upstreams = {"upstreams":[{"auditStamp":{"time":sys_time,"actor":actor},"dataset":"urn:li:dataset:(urn:li:dataPlatform:hive," + upstreams_dataset + ",PROD)","type":type}]}
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elements = {"elements":[{"url":HIVESTORE,"description":"sample doc to describe upstreams","createStamp":{"time":sys_time,"actor":actor}}]}
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schema_name = {"schemaName":dataset_name,"platform":"urn:li:dataPlatform:hive","version":0,"created":{"time":created_time,"actor":actor},
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"lastModified":{"time":sys_time,"actor":actor},"hash":"","platformSchema":{"OtherSchema": schema},
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"fields":[{"fieldPath":"","description":"","nativeDataType":"string","type":{"type":{"com.linkedin.pegasus2avro.schema.StringType":{}}}}]}
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mce = {"auditHeader": None,
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"proposedSnapshot":("com.linkedin.pegasus2avro.metadata.snapshot.DatasetSnapshot",
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{"urn": "urn:li:dataset:(urn:li:dataPlatform:hive,"+ dataset_name +",PROD)","aspects": [owners, upstreams, elements, schema_name]}),
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"proposedDelta": None}
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produce_hive_dataset_mce(mce)
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def produce_hive_dataset_mce(mce):
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"""
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Produce MetadataChangeEvent records.
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"""
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from confluent_kafka import avro
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from confluent_kafka.avro import AvroProducer
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conf = {'bootstrap.servers': BOOTSTRAP,
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'schema.registry.url': SCHEMAREGISTRY}
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record_schema = avro.load(AVROLOADPATH)
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producer = AvroProducer(conf, default_value_schema=record_schema)
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try:
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producer.produce(topic=KAFKATOPIC, value=mce)
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producer.poll(0)
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sys.stdout.write('\n%s has been successfully produced!\n' % mce)
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except ValueError as e:
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sys.stdout.write('Message serialization failed %s' % e)
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producer.flush()
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databases = hive_query('show databases')
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for database in databases:
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tables = hive_query('show tables in ' + database[0])
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for table in tables:
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dataset_name = database[0] + '.' + table[0]
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description = hive_query('describe extended ' + dataset_name)
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build_hive_dataset_mce(dataset_name, str(description[:-1][:-1]), description[-1][1].split(','))
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sys.exit(0) |