class WordCount(Topology):
word_spout = KafkaSpout.spec()
process_bolt = ProcessBolt.spec(inputs=[word_spout], par=2)
senti_bolt = SentimentBolt.spec(
inputs={process_bolt: Grouping.fields('tweet')}, par=2)
entity_bolt = EntityBolt.spec(
inputs={senti_bolt: Grouping.fields('tweet')}, par=2)
class WordCount(Topology):
"""组装topo结构"""
word_spout = SentenceSpout.spec()
split_bolt = SentenceSplitterBolt.spec(
inputs={word_spout: Grouping.fields("sentence")}, par=2)
count_bolt = SentenceCountBolt.spec(
inputs={split_bolt: Grouping.fields('word', 'num')}, par=2)
class WordCount(Topology):
product_spout = ProductSpout.spec()
user_profile_spout = UserProfileSpout.spec()
product_bolt = ProductBolt.spec(
inputs={product_spout: Grouping.fields('product')}, par=2)
profile_bolt = ProductBolt.spec(
inputs={user_profile_spout: Grouping.fields('profile')}, par=2)
class TopWordFinderTopologyPartA(Topology):
# TODO:
# Task: wire up the topology
# Make sure you use the following names for each component
# RandomSentenceSpout -> "spout"
spout = RandomSentenceSpout.spec()
# SplitSentenceBolt -> "split"
split = SplitSentenceBolt.spec(inputs={spout: Grouping.fields('word')})
# WordCountBolt -> "count"
count = WordCountBolt.spec(inputs={split: Grouping.fields('word')})
class NearestVectors(Topology):
vector_spout = IndexSpout.spec()
vector_bolt = VectorMapBolt.spec(inputs={vector_spout: Grouping.SHUFFLE},
par=8)
index_bolt = PairProcessBolt.spec(inputs={vector_bolt: Grouping.SHUFFLE},
par=8)
sum_bolt = VectorSumBolt.spec(
inputs={index_bolt: Grouping.fields('vector_id')}, par=8)
final_bolt = NearestBolt.spec(inputs={sum_bolt: Grouping.fields('final')},
par=8)
class WordCount(Topology):
redditStream_spout = streamRedditSpout.spec()
titleSplit_bolt = titleSplitterBolt.spec(inputs={
redditStream_spout:
Grouping.fields('redditTitle', 'redditLink')
},
par=2)
matchKeywords_bolt = matchKeywordsBolt.spec(inputs={
titleSplit_bolt:
Grouping.fields('splitTitle', 'redditTitle', 'redditLink')
},
par=2)
class TopWordFinderTopologyPartA(Topology):
config = {'coursera.datafile': 'resources/data.txt'}
# TODO:
# Task: wire up the topology
# Make sure you use the following names for each component
# FileReaderSpout -> "spout"
spout = FileReaderSpout.spec(inputs=config)
# SplitSentenceBolt -> "split"
split = SplitSentenceBolt.spec(inputs={spout: Grouping.fields('word')})
# WordCountBolt -> "count"
count = WordCountBolt.spec(inputs={split: Grouping.fields('word')})
class WordCount(Topology):
#spout1
word_spout = WordSpout.spec()
#bolt1
count_bolt = WordCountBolt.spec(
inputs={word_spout: Grouping.fields('word')}, par=2) # process
#bolt2
count_bolt2 = WordCountBolt2.spec(
inputs={count_bolt: Grouping.fields('word')}, par=2)
#bolt3
count_bolt3 = WordCountBolt3.spec(
inputs={count_bolt: Grouping.fields('word')}, par=2)
class TopWordFinderTopologyPartC(Topology):
config = {'coursera.datafile': 'resources/data.txt'}
# TODO:
# Task: wire up the topology
# Make sure you use the following names for each component
# FileReaderSpout -> "spout"
# SplitSentenceBolt -> "split"
# WordCountBolt -> "count"
# NormalizerBolt -> "normalize"
# TopNFinderBolt -> "top-n"
# NOTE: will have to manually kill Topology after submission
sentence_spout = FileReaderSpout.spec(name='spout')
split_bolt = SplitSentenceBolt.spec(name='split', inputs=[sentence_spout])
normalize_bolt = NormalizerBolt.spec(name='normalize', inputs=[split_bolt])
count_bolt = WordCountBolt.spec(
name='count', inputs={normalize_bolt: Grouping.fields('word')})
topnfinder_bolt = TopNFinderBolt.spec(
name='top-n', inputs={count_bolt: Grouping.fields('word')})
class PixelCount(Topology):
pixel_spout = JavaSpout.spec(
name="pixel-spout",
full_class_name="pixelcount.spouts.PixelSpout",
args_list=[],
outputs=["pixel"])
pixel_deserializer = PixelDeserializerBolt.spec(
name='pixel-deserializer-bolt', inputs=[pixel_spout])
pixel_counter = PixelCounterBolt.spec(
name='pixel-count-bolt',
inputs={pixel_deserializer: Grouping.fields('url')},
config={"topology.tick.tuple.freq.secs": 1})
class TopWordFinderTopologyPartA(Topology):
# TODO:
# Task: wire up the topology
# Make sure you use the following names for each component
# RandomSentenceSpout -> "spout"
# SplitSentenceBolt -> "split"
# WordCountBolt -> "count"
# NOTE: will have to manually kill Topology after submission
sentence_spout = RandomSentenceSpout.spec(name='spout')
split_bolt = SplitSentenceBolt.spec(name='split', inputs=[sentence_spout])
count_bolt = WordCountBolt.spec(
name='count', inputs={split_bolt: Grouping.fields('word')})
class TweetProcess(Topology):
tweet_spout = TweetSpout.spec()
amazon_bolt = ProcessAmazon.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
apple_bolt = ProcessApple.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
facebook_bolt = ProcessFacebook.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
google_bolt = ProcessGoogle.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
lyft_bolt = ProcessLyft.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
microsoft_bolt = ProcessMicrosoft.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
twitter_bolt = ProcessTwitter.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
uber_bolt = ProcessUber.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=1)
class WordCount(Topology):
word_spout = WordSpout.spec()
count_bolt = WordCountBolt.spec(
inputs={word_spout: Grouping.fields('word')}, par=2)
class WordCount(Topology):
tweet_spout = TweetSpout.spec()
count_bolt = AMSBolt.spec(inputs={tweet_spout: Grouping.fields("tweet")}, par=1)
class WordCount(Topology):
tweet_spout = Tweets.spec()
count_bolt = WordCounter.spec(
inputs={tweet_spout: Grouping.fields('tweet')}, par=55555)
class OutputDebugTopology(Topology):
files_spout = FilesMailSpout.spec(
name="files-mails")
tokenizer = Tokenizer.spec(
name="tokenizer",
inputs=[files_spout],
par=1)
attachments = Attachments.spec(
name="attachments",
inputs={tokenizer['attachments']: Grouping.fields('sha256_random')},
par=2)
urls_body = UrlsHandlerBody.spec(
name="urls-handler-body",
inputs={tokenizer['body']: Grouping.fields('sha256_random')})
urls_attachments = UrlsHandlerAttachments.spec(
name="urls-handler-attachments",
inputs={attachments: Grouping.fields('sha256_random')})
phishing = Phishing.spec(
name="phishing",
inputs={
tokenizer['mail']: Grouping.fields('sha256_random'),
attachments: Grouping.fields('sha256_random'),
urls_body: Grouping.fields('sha256_random'),
urls_attachments: Grouping.fields('sha256_random')})
forms = Forms.spec(
name="forms",
inputs={tokenizer['body']: Grouping.fields('sha256_random')})
json = JsonMaker.spec(
name="json",
inputs={
tokenizer['mail']: Grouping.fields('sha256_random'),
phishing: Grouping.fields('sha256_random'),
attachments: Grouping.fields('sha256_random'),
forms: Grouping.fields('sha256_random'),
urls_body: Grouping.fields('sha256_random'),
urls_attachments: Grouping.fields('sha256_random')})
output_debug = OutputDebug.spec(
name="output-debug",
inputs=[json])
class SurpriseNumberTwitter(Topology):
tweet_spout = TweetSpout.spec()
count_bolt = AMSBolt.spec(inputs={tweet_spout: Grouping.fields("tweet")},
par=1)
class Tagger(Topology):
tweet_spout = TweetSpout.spec()
count_bolt = TagCountBolt.spec(inputs=[tweet_spout],par=2)
cassandra_bolt = SaveCountBolt.spec(inputs={count_bolt: Grouping.fields(['cls','tag'])},par=2) #SaveCountBolt.spec(inputs=[count_bolt],par=2)
cassandra_trend_bolt = TrendCountBolt.spec(inputs={count_bolt: Grouping.fields(['cls','tag'])},par=2)
class WordCount(Topology):
word_spout = WordSpout.spec(
) # spec sets the specification of a spout or bolt. can take a name, par, and config argument.
count_bolt = WordCountBolt.spec(
inputs={word_spout: Grouping.fields('word')}, par=2
) # par refers to the number of parallel processes for that operation.
class Twitter(Topology):
tweet_spout = TweetSpout.spec()
tweet_bolt = TweetBolt.spec(inputs={tweet_spout: Grouping.fields("lang")},
par=2)
class Processing(Topology):
consumer_spout = ConsumerSpout.spec(par=5)
processing_bolt = ProcessingBolt.spec(
inputs={consumer_spout: Grouping.fields('sentences')}, par=5)
class OutputElasticsearchTopology(Topology):
files_spout = FilesMailSpout.spec(
name="files-mails")
tokenizer = Tokenizer.spec(
name="tokenizer",
inputs=[files_spout],
par=1)
attachments = Attachments.spec(
name="attachments",
inputs={tokenizer['attachments']: Grouping.fields('sha256_random')},
par=1)
urls = Urls.spec(
name="urls",
inputs={
attachments: Grouping.fields('sha256_random'),
tokenizer['body']: Grouping.fields('sha256_random')})
phishing = Phishing.spec(
name="phishing",
inputs={
attachments: Grouping.fields('sha256_random'),
tokenizer['mail']: Grouping.fields('sha256_random'),
urls: Grouping.fields('sha256_random')})
network = Network.spec(
name="network",
inputs={tokenizer['network']: Grouping.fields('sha256_random')},
par=1)
raw_mail = RawMail.spec(
name="raw_mail",
inputs={tokenizer['raw_mail']: Grouping.fields('sha256_random')},
par=1)
json_maker = JsonMaker.spec(
name="json_maker",
inputs={
attachments: Grouping.fields('sha256_random'),
network: Grouping.fields('sha256_random'),
phishing: Grouping.fields('sha256_random'),
raw_mail: Grouping.fields('sha256_random'),
tokenizer['mail']: Grouping.fields('sha256_random'),
urls: Grouping.fields('sha256_random')})
output_elasticsearch = OutputElasticsearch.spec(
name="output-elasticsearch",
inputs=[json_maker])
class WordCount(Topology):
event_spout = EventSpout.spec()
count_bolt = DirectionBolt.spec(
inputs={event_spout: Grouping.fields('array_event_id')}, par=2)