def nextTuple(self):
race_time = self.myindex # one record per second
# I put sleep to simulate the event. It will be 3x faster than the normal race
# We need at least 80 seconds data to start anomaly detection. I don't want to wait for first 80 seconds
if race_time > 80:
time.sleep(0.35)
row_data = self.data.loc[self.myindex].values
# We can only emit a list. List inside list is not allowed
# Example emit_data: ['word', 9, 74.08, 75.02, 73.76, 77.67, 81.24, 74.63, 76.59, 74.61, 72.88, 71.91]
# the text 'word' does not matter. If there are multiple multiple bolts to take the text, it will be equally splitted to bolts based on the text
# for instance: 'apple' to bolt_instance1, 'banana' to bolt_instance2
# I want them to emitted to a single bolt. Otherwise, I have to consider data order.
emit_data = ["word"]
emit_data.append(race_time)
emit_data = emit_data + row_data.tolist()
self.myindex += 1
# start from the beginning if race ends
if self.myindex == len(self.data):
self.myindex = 0
storm.logInfo("Emiting %s" % str(emit_data))
storm.emit(emit_data)
def process(self, tup):
'''
TODO:
Task: keep track of the top N words
Hint: implement efficient algorithm so that it won't be shutdown before task finished
the algorithm we used when we developed the auto-grader is maintaining a N size min-heap
'''
word = tup.values[0]
count = float(tup.values[1])
new_word_count = WordCount(word, count)
if word in self._top_N_map:
if count > self._top_N_map[word].count:
self._top_N_map[word].count = count
heapq.heapify(self._top_N_heap)
#adding new elements if the element size is less than 10
elif len(self._top_N_heap) < self._N:
self._top_N_map[word] = new_word_count
heapq.heappush(self._top_N_heap, new_word_count)
#find smallest word and replace it with new word
else:
smallest_word_count = self._top_N_heap[0]
if count > smallest_word_count.count:
del (self._top_N_map[smallest_word_count.word])
self._top_N_map[word] = new_word_count
heapq.heapreplace(self._top_N_heap, new_word_count)
storm.logInfo("Add word: %s, count: %d" % (word, count))
storm.emit(["top-N", self.printvalues()])
pass
def process(self, tup):
# Load data from tuple
data = tup.values[0]
# Analyze data
if data['source'] == 'twitter':
weight = .995
sentiment = data['data']['sentiment']
overall_sentiment[
0] = overall_sentiment[0] * weight + sentiment * (1 - weight)
elif data['source'] == 'reddit':
weight = .85
sentiment = data['data']['sentiment']
overall_sentiment[
0] = overall_sentiment[0] * weight + sentiment * (1 - weight)
elif data['source'] == 'news':
weight = .85
sentiment = data['data']['sentiment']
overall_sentiment[
0] = overall_sentiment[0] * weight + sentiment * (1 - weight)
# Get today's date
today = date.today()
# Store analyzed results in DynamoDB
table = dynamodb.Table(config['dynamodb']['analysis'])
table.put_item(
Item={
'date': str(today),
'timestamp': str(time.time()),
'sentiment': Decimal(str(overall_sentiment[0]))
})
# Emit for downstream bolts
storm.emit([data])
def nextTuple(self):
time.sleep(0.2)
# TODO
# Task: randomly generate sentence from sentences string array
sentence = random.chioce(SENTENCE)
storm.logInfo("Emitting %s" % sentence)
storm.emit([sentence])
def process(self, tup):
# TODO:
# Task: keep track of the top N words
word = tup.values[0]
count = int(tup.values[1])
new_word_count = WordCountTuple(word, count)
if word in self._top_N_map:
if count > self._top_N_map[word].count:
self._top_N_map[word].count = count
heapq.heapify(self._top_N_heap)
storm.logInfo("Update word: %s, count: %d" % (word, count))
elif len(self._top_N_heap) < self._N:
self._top_N_map[word] = new_word_count
heapq.heappush(self._top_N_heap, new_word_count)
storm.logInfo("Add word: %s, count: %d" % (word, count))
else:
smallest_word_count = self._top_N_heap[0]
storm.logInfo(
"Current smallest word: %s, count: %d" %
(smallest_word_count.word, smallest_word_count.count))
if count > smallest_word_count.count:
del (self._top_N_map[smallest_word_count.word])
self._top_N_map[word] = new_word_count
heapq.heapreplace(self._top_N_heap, new_word_count)
storm.logInfo("Add word: %s, count: %d" % (word, count))
storm.logInfo("Top N: %s" % self.report())
storm.emit(["top-N", self.report()])
def process(self, tup):
# Load data from tuple
data = tup.values[0]
data = json.loads(data)
# Get today's date
today = date.today()
# Analyze data
sentiment = get_sentiment_score(data['title'], data['description'])
# Store analyzed results in DynamoDB
table = dynamodb.Table(config['dynamodb']['news'])
parsed_data = {
'date': str(today),
'timestamp': str(data['publishedAt']),
'title': data['title'],
'description':
data['description'] if data['description'] != '' else ' ',
'sentiment': Decimal(str(sentiment))
}
table.put_item(Item=parsed_data)
# Emit for downstream bolts
storm.emit([{'source': 'news', 'data': parsed_data}])
def nextTuple(self):
docs = self.get_docs()
uuid = docs[0]['uuid']
tup = [docs[0]['href'][18:]]
self.buffer[uuid] = (tup, 0)
emit(tup, id=uuid)
sleep(1.0)
def process(self, tup):
data = tup.values[0]
tweet = ast.literal_eval(data)
output = real_time_batch_processing(tweet)
table = conn_db.Table("realtime_db")
table.put_item(Item=output)
storm.emit([output])
def process(self,tup):
if tup.values:
words = tup.values[0]
if words:
storm.emit([words])
else:
pass
def process(self, tup):
tweet = tup.values[0]
tweet['tweettext'] = tweet['text']
del tweet['text']
tweet['sentimentscore'] = str(
self.checkForHateWords(tweet['tweettext']))
storm.emit([tweet])
def process(self, tup): text = tup.values[1] #language = langid.classify(text)[0] #l = LangID() #l.train() language = self.l.classify(text) storm.emit([tup.values[0], language])
def process(self, tup):
segments = tup.values[1].rstrip('/').rsplit('/', 1)
path = segments[0] if self.match(segments[-1]) else '/'.join(segments)
event = dict(
timestamp=tup.values[0],
path=path
)
kwargs = dict(
id=tup.values[2]
)
try:
# TODO: Retrieve users from all indicies.
events = self.es.get(self.index, kwargs['id'], 'user',
preference='_primary')
kwargs['version'] = events['_version']
body = {'events': events['_source']['events'] + [event]}
except NotFoundError:
kwargs['op_type'] = 'create'
body = {'events': [event]}
try:
body['rank'] = math.log10(len(body['events'])) / 2
self.es.index(self.index, 'user', body, **kwargs)
paths = list(event['path'] for event in body['events'])
emit([kwargs['id'], paths])
ack(tup)
except TransportError:
fail(tup)
def process(self, tup):
f = open("/root/Japanese.txt")
self.stopwords = f.read().split('\n')
f.close()
self.m = MeCab.Tagger("-Ochasen")
r = redis.Redis(host='localhost', port=6379, db=0)
pid = os.getpid()
base_path = '/var/log/takatoshi/'
logging.basicConfig(filename=base_path + __file__ + '.log',
level=logging.DEBUG)
logging.debug(datetime.now())
logging.debug("abs path of py file: " + os.path.abspath(__file__))
sentence = tup.values[0]
ts = tup.values[1]
res = self.m.parse(sentence).splitlines()[:-1]
output = set()
for _ in res:
fa = _.split('\t')
if u"名詞" in fa[3] and not fa[0] in self.stopwords:
output.add(fa[0])
if len(output):
r.incr('total_' + str(ts))
for _ in output:
logging.debug("getting noun : " + _)
storm.emit([_, ts])
def __do_emit_updated(self, is_updated=True):
#if not self.last_output or is_updated:
# self.last_output = ', '.join(list(self.the_top.keys()))
#storm.emit(['top-N', self.last_output])
#output = ', '.join(list(self.the_top.keys()))
output = ', '.join([cand.word for cand in self.top_cands])
storm.emit(['top-N', output])
def process(self, tup):
sentence = tup.values[0]
sentence = re.sub(r"[,.;!\?]", "", sentence) # get rid of punctuation and num
words = jieba.cut(sentence, cut_all=True)
for word in words:
storm.emit([word])
def process(self, tup):
# IDEA: do language classification here already?
# IDEA: keep classifiers for several languages in a map
# IDEA: batch N classifications (of the same language) for speed?
url, meta, text, content, outlinks = tup.values
metadata = Metadata(meta)
out = [url, metadata, text, content, outlinks]
try:
lang = metadata['n52.language'][0]
except KeyError:
lang = 'unknown'
if lang != 'en':
msg = 'ignoring tuple, as document language {} is not supported'.format(
lang)
logging.debug(msg)
storm.logDebug(msg)
else:
confidence, clazz = self.classify([text])[0]
metadata["n52.classify.class"] = clazz
metadata["n52.classify.confidence"] = confidence
logging.debug([confidence, clazz, text[100:200], url])
storm.emit(out, anchors=[tup])
def process(self, tup):
words = tup.values[0].split('.')
cluster, user, job, task, pid, cpu, mem = tuple(words)
storm.emit(words)
logger.info(
'cluster:%s,username:%s,jobname:%s,taskname:%s,pid:%s,cpu:%s,mem:%s',
cluster, user, job, task, pid, cpu, mem)
def nextTuple(self):
try:
html = urllib2.urlopen(self.url_animals_tl).read()
soup = BeautifulSoup(html)
scripts = soup.find_all('script')
code = scripts[len(scripts) - 1]
content = code.contents[0].strip()
prefix = 'P.start.start('
i = content.find(prefix)
if i != -1:
json_str = content[i + len(prefix) : len(content) - 2]
json_obj = json.loads(json_str)
pins = json_obj['tree']['children'][3]['children'][0]['children'][0]['children']
for pin in pins:
pin_id = pin['options']['pin_id']
if 'module' in pin['children'][1]['options']:
module = pin['children'][1]['options']['module']
orig_link = pin['data']['link']
orig_host = urlparse(pin['data']['link']).hostname
images = pin['data']['images']
if 'orig' in images:
pass
storm.emit([pin_id, orig_link, orig_host, json.dumps(pin, indent=4, sort_keys=True), self.category])
time.sleep(2)
except StopIteration:
pass
except urllib2.HTTPError, err:
if err.code == 404:
pass
def process(self, tup):
# Split the inbound sentence at spaces
words = tup.values[0].split(" ")
# Loop over words and emit
for word in words:
storm.logInfo("Emitting %s" % word)
storm.emit([word])
def process(self,tup):
# storm.log("Dispel4Py ------> %s: Received block." % (self.script.id, ))
try:
inputname = self.inputmapping[tup.component][tup.stream]
storm.log("Dispel4Py ------> %s: Received block at input '%s'" % (self.script.id, inputname, ))
# inputs = tup.values
inputs = decode_types(tup.values)
outputs = self.script.process( { inputname : inputs })
# storm.log("Dispel4Py ------> %s: Processing complete." % self.scriptname)
if outputs is None:
return
for streamname, output in outputs.iteritems():
result = output if isinstance(output, list) else [output]
try:
storm.emit(result, stream=streamname)
storm.log("Dispel4Py ------> %s: Emitted to stream %s: %s" % (self.script.id, streamname, str(result)[:200]))
except TypeError:
# encode manually
encoded = encode_types(result)
storm.emit(encoded, stream=streamname)
storm.log("Dispel4Py ------> %s: Emitted to stream %s" % (self.script.id, streamname))
# except:
# storm.log("%s: %s" % (self.script.id, traceback.format_exc()))
except:
storm.log("Dispel4Py ------> %s: %s" % (self.script.id, traceback.format_exc(), ))
def process(self, tup):
word = tup.values[0];
if (random() < 0.75):
storm.emit([word + 'lalala'], anchors=[tup])
storm.ack(tup)
else:
storm.log(word + ' randomly skipped!')
def process(self, tup):
race_time = tup.values[1]
row_data = [tup.values[i + 2] for i in range(10)]
storm.logInfo("Inference Bolt data: race_time: %s, data: %s" %
(str(race_time), str(row_data)))
# restart the event
if race_time == 0:
self.speed_data = []
self.next_step_data = row_data
# we need to have 80 seconds data at least to start detection
elif race_time <= self.time_step:
self.speed_data.append(self.next_step_data)
self.next_step_data = row_data
#normal case
else:
self.speed_data.append(self.next_step_data)
self.speed_data.pop()
self.next_step_data = row_data
input_data = np.expand_dims(
self.scaler.transform(np.array(self.speed_data).T), 2)
prediction = self.scaler.inverse_transform(
self.model.predict(input_data))
anomaly_score = np.abs(prediction[:, 0] -
np.array(self.next_step_data)).tolist()
#storm.logInfo("race_time:%s speed:%s anomaly_score %s" % (str(race_time), str(self.next_step_data), str(anomaly_score)))
emit_data = ["word"]
emit_data.append(race_time)
emit_data = emit_data + self.next_step_data + anomaly_score
storm.logInfo("Inference Bolt emiting: %s" % str(emit_data))
storm.emit(emit_data)
def nextTuple(self):
docs = self.get_docs()
uuid = docs[0]['uuid']
tup = [docs[0]['href'][18:]]
self.buffer[uuid] = (tup, 0)
emit(tup, id=uuid)
sleep(1.0)
def process(self, tup):
# Load data from tuple
data = tup.values[0]
data = json.loads(data)
# Initialize if deque is empty
if len(past_prices) == 0:
past_prices.extend([float(data['price'].replace(',', ''))] *
LOOK_BACK)
# Append to rolling deque and compute price
else:
past_prices.append(float(data['price'].replace(',', '')))
# Check if enough elements exist
if len(past_prices) == LOOK_BACK:
prediction = predict(MODEL,
np.array(past_prices).reshape(1, LOOK_BACK))
# Store predicted results in DynamoDB
table = dynamodb.Table(config['dynamodb']['prediction'])
table.put_item(
Item={
'timestamp': shift_future(data['timestamp'],
FUTURE_OFFSET),
'price': Decimal(str(prediction)),
})
# Emit for downstream bolts
storm.emit([data])
def process(self, tup):
dict_data = tup.values[0]
sensor_type = tup.values[1]
ordered_dict = self.order_dict(dict_data)
#转列表
values_list = list(ordered_dict.values())
time_list = list(ordered_dict.keys())
#训练长度
train_len = int(len(values_list) * 0.85)
#预测长度
predict_len = int(len(values_list) * 0.15)
#训练数据
train_values = values_list[:train_len]
#训练时间序列
train_time = time_list[:train_len]
#预测时间序列
predict_time = time_list[-predict_len:]
#列表转ndarray
nd_values = np.array(train_values, dtype=np.float32)
nd_time = np.array(train_time, dtype=np.float32)
#训练
predict = self.train_predict(nd_time, nd_values, predict_len)
#转字典(转json)
predict_dict = dict(
zip(predict_time, map(lambda x: round(x, 4), predict[0])))
storm.emit([dict_data, predict_dict, sensor_type])
def process(self, tup):
word = tup.values[0]
if (random() < 0.75):
storm.emit([word + 'lalala'], anchors=[tup])
storm.ack(tup)
else:
storm.log(word + ' randomly skipped!')
def nextTuple(self):
try:
input_tuple = None
try:
if self.counter >= self.script._num_iterations:
return
except:
try:
input_tuple = self.script._static_input.pop(0)
except AttributeError:
# there is no static input
pass
except IndexError:
# static input is empty - no more processing
return
outputs = self.script.process(input_tuple)
if outputs is None:
return
for streamname, output in outputs.iteritems():
result = output if isinstance(output, list) else [output]
storm.emit(result, stream=streamname, id=self.counter)
storm.log("Dispel4Py ------> %s: emitted tuple %s to stream %s" % (self.script.id, result, streamname))
self.counter += 1
except:
# logging the error but it should be passed to client somehow
storm.log("Dispel4Py ------> %s: %s" % (self.scriptname, traceback.format_exc(), ))
def process(self, tuple):
'''
Must fulfil the following contract expressed in the Java wrapper:
declarer.declare(new Fields(TopologyFields.AUTHOR_SCREEN_NAME, TopologyFields.CREATED_AT,
TopologyFields.FAV_COUNT, TopologyFields.HASHTAGS_TEXTS, TopologyFields.IN_REPLY_TO_SCREEN_NAME,
TopologyFields.LANG, TopologyFields.RETWEET_COUNT, TopologyFields.RETWEETED,
TopologyFields.SOURCE, TopologyFields.PLACE, TopologyFields.POSSIBLY_SENSITIVE,
TopologyFields.TEXT, TopologyFields.TOPIC_NAME));
'''
place, topic_name, query = tuple.values
try:
tweets = list(get_tweets.get_tweets_for_trends(self._twitter_api, [{"query" : query}], popular = True, tweet_processor = self._storm_tweet_processor))[0]["tweets"]
except tweepy.TweepError as te:
# We have hit the REST API Rate limit for Twitter https://dev.twitter.com/docs/rate-limiting/1.1, no more tweets for some time
log_tweeter_error(te, sleep_time=self._rate_limit_sleep_time)
return
for pt in tweets:
# Here we add the trending topic name, and take the place name from those
# used internally by get_tweets, instead of the from place names returned by twitter
tup = [pt['author_screen_name'], pt['created_at'],
pt['favorite_count'], pt['hashtags_texts'], pt['in_reply_to_screen_name'],
pt['lang'], pt['retweet_count'], pt['retweeted'],
pt['source'], place, pt['possibly_sensitive'],
pt['text'], topic_name]
storm.emit(tup)
def nextTuple(self):
# 停止一段时间(设置状态位)
time.sleep(15)
batch = 10
bases = ts.get_stock_basics()
code_list = bases.index
total = code_list.__len__()
batch_size = total // batch
pool = multiprocessing.Pool(processes=batch)
results = []
for i in range(batch + 1):
begin_index = i * batch_size
end_index = (i + 1) * batch_size
if end_index > total:
end_index = total
batch_data = code_list.tolist().__getslice__(
begin_index, end_index)
res = pool.apply_async(ts.get_realtime_quotes, (batch_data, ))
results.append(res)
# get_stock_hist_data_batch(code_list = batch_data,start=start,end=end,sh_df=sh_df,sz_df=sz_df,cyb_df=cyb_df,table_name=table_name)
pool.close()
pool.join()
#等待执行完毕
for item in results:
for i, row in item.iterrows():
code = row['code']
sentence = random.choice(SENTENCES)
storm.logInfo("Emiting %s" % sentence)
storm.logInfo("Emiting code:%s row:%s" % (code, row))
storm.emit([code, row])
def emit(self):
if not self.entity:
self.entity = self.getLastEntity()
# We always emit tuple = (entity, "{json string}")
storm.emit([self.entity,json.dumps(self.d)])
def process(self, tuple):
id_tweet, text = tuple.values
storm.logInfo("LT3BOLTINFO")
storm.logInfo(text)
json = get_res(text.encode('utf-8'))
''.join(json)
json = json.split('\n')[-2]
json_string = json.replace("'", '"')
data = simplejson.loads(json_string)
data['id'] = str(id_tweet)
data['source'] = "LT3"
data['info'] = text
if (data['relevance_boolean'] == 1 and data['severity_boolean'] == 1):
data['flag'] = "LT3"
else:
data['flag'] = "none"
del data['relevance_boolean']
del data['severity_boolean']
json_string = simplejson.dumps(data)
storm.emit([json_string])
def nextTuple(self):
try:
input_tuple = None
try:
input_tuple = self.script._static_input.pop(0)
except AttributeError:
# there is no static input
if self.counter >= self.script._num_iterations:
return
except IndexError:
# static input is empty - no more processing
return
storm.log("Dispel4Py ------> %s: input %s" % (
self.scriptname,
input_tuple,
))
outputs = self.script.process(input_tuple)
if outputs is None:
return
for streamname, output in outputs.iteritems():
result = output if isinstance(output, list) else [output]
storm.emit(result, stream=streamname, id=self.counter)
storm.log(
"Dispel4Py ------> %s: emitted tuple %s to stream %s" %
(self.script.id, result, streamname))
self.counter += 1
except:
# logging the error but it should be passed to client somehow
storm.log("Dispel4Py ------> %s: %s" % (
self.scriptname,
traceback.format_exc(),
))
def process(self, tup):
#TO DO: Add check for empty values
if tup.values[0]:
words = tup.values[0].split(" ")
if words:
for word in words:
storm.emit([word])
def process(self, tup):
# storm.log("Dispel4Py ------> %s: Received block." % (self.script.id, ))
try:
inputname = self.inputmapping[tup.component][tup.stream]
storm.log("Dispel4Py ------> %s: Received block at input '%s'" % (
self.script.id,
inputname,
))
# inputs = tup.values
inputs = decode_types(tup.values)
outputs = self.script.process({inputname: inputs})
# storm.log("Dispel4Py ------> %s: Processing complete." % self.scriptname)
if outputs is None:
return
for streamname, output in outputs.iteritems():
result = output if isinstance(output, list) else [output]
try:
storm.emit(result, stream=streamname)
storm.log(
"Dispel4Py ------> %s: Emitted to stream %s: %s" %
(self.script.id, streamname, str(result)[:200]))
except TypeError:
# encode manually
encoded = encode_types(result)
storm.emit(encoded, stream=streamname)
storm.log("Dispel4Py ------> %s: Emitted to stream %s" %
(self.script.id, streamname))
# except:
# storm.log("%s: %s" % (self.script.id, traceback.format_exc()))
except:
storm.log("Dispel4Py ------> %s: %s" % (
self.script.id,
traceback.format_exc(),
))
def process(self, tup):
url = tup.values[0]
storm.log("HARing "+url)
output = self.get_har_with_image(url)
if output is not "FAIL":
storm.emit(output, anchors=[tup])
storm.ack(tup)
def process(self, tup):
sentence = tup.values[0].lower()
words = sentence.split(" ")
bow = numpy.zeros((self._n,))
for w in words:
bow[self.map[w]]+=1
storm.emit([bow.tolist()])
def nextTuple(self):
id = "periodic_%s" % str(uuid4())
body = json.dumps({'message_id': CHECK_METRIC_ALARM_MSG_ID})
message = "Periodic monitoring message sent [%s] %s"
self.log(message % (id, body))
emit([None, body], id=id)
time.sleep(60)
def process(self, tup):
# Split the inbound sentence at spaces
words = tup.values[0].split(" ")
# Loop over words and emit
for word in words:
storm.logInfo("Emitting-----> %s" % word)
storm.emit([word])
def process(self, tup):
segments = tup.values[1].rstrip('/').rsplit('/', 1)
path = segments[0] if self.match(segments[-1]) else '/'.join(segments)
event = dict(timestamp=tup.values[0], path=path)
kwargs = dict(id=tup.values[2])
try:
# TODO: Retrieve users from all indicies.
events = self.es.get(self.index,
kwargs['id'],
'user',
preference='_primary')
kwargs['version'] = events['_version']
body = {'events': events['_source']['events'] + [event]}
except NotFoundError:
kwargs['op_type'] = 'create'
body = {'events': [event]}
try:
body['rank'] = math.log10(len(body['events'])) / 2
self.es.index(self.index, 'user', body, **kwargs)
paths = list(event['path'] for event in body['events'])
emit([kwargs['id'], paths])
ack(tup)
except TransportError:
fail(tup)
def process(self, tup):
# ********** Read input arguments **********
try:
input_args = json.loads(tup.values[0])
except:
input_args = tup.values[0]
ret_info = tup.values[1]
# ********** Calculate results **********
# Construct required variables
if "query_type" not in input_args:
return storm.emit([json.dumps({"ok": False, "msg": "No `query_type` param provided!"}), ret_info])
query_type = input_args["query_type"].lower()
if query_type == "collections":
answer = self.handler.describe_collections('cenote', input_args["PROJECT_ID"])
return storm.emit([json.dumps({"ok": True, "msg": answer}), ret_info])
timeframe_start = ""
timeframe_end = ""
if "timeframe_start" in input_args:
timeframe_start = input_args["timeframe_start"]
if "timeframe_end" in input_args:
timeframe_end = input_args["timeframe_end"]
info = {
"cenote": {
"url": "/projects/" + input_args["PROJECT_ID"] + "/queries/" + input_args["event_collection"] + "/extraction",
"timeframe_start": timeframe_start,
"timeframe_end": timeframe_end
}
}
columns = None
if "target_property" in input_args:
columns = input_args["target_property"].split(",")
# Execute corresponding query
if query_type == "extraction":
answer = self.reader.read_data("cenote", columns, json.dumps(info))
elif query_type in ["count", "min", "max", "sum", "average", "median"]:
answer = self.reader.perform_operation("cenote", columns, query_type, json.dumps(info))
elif query_type == "percentile":
info["cenote"]["percentile"] = int(input_args["percentile"])
answer = self.reader.perform_operation("cenote", columns, query_type, json.dumps(info))
else:
answer = {"data": "Not implemented yet!"}
# Return results
if "response" in answer and answer["response"] == 200:
# Hack-ia to turn "system.<someoperation>(<column>)" to "<column>"
answer = json.loads(re.sub(r'system\.\w*\(|\)', "", json.dumps(answer, default=datetimeParser)))
return storm.emit([json.dumps({"ok": True, "msg": answer["data"]}), ret_info])
else:
try:
problem = answer["exception"]
except:
problem = answer["data"]
return storm.emit([json.dumps({"ok": False, "msg": problem}), ret_info])
def process(self, tup):
'''We serialize the input and output by json for convenience.'''
try:
data = array(json.loads(tup.values[1]))
result = self.model.predict(data)
storm.emit([tup.values[0], json.dumps(result.tolist())])
except:
traceback.print_exc(file=open('/tmp/trace_svm_bolt.txt', 'a'))
def process(self, tup):
word = tup.values[0]
count = self.words.get(word)
if count == None:
count = 0
count = count + 1
self.words[word] = count
storm.emit([word, str(count)])
def fail(self, msg_id):
tup, retries = self.buffer[msg_id]
if retries >= 5:
del self.buffer[msg_id]
log('[RabbitMQSpout] Message %s failed for good.' % msg_id)
else:
self.buffer[msg_id] = (tup, retries + 1)
emit(tup, id=msg_id)
def fail(self, cnt_id):
tup, retries = self.buffer[cnt_id]
if retries >= 5:
del self.buffer[cnt_id]
log('[ZonAPISpout] Message %s failed for good.' % cnt_id)
else:
self.buffer[cnt_id] = (tup, retries + 1)
emit(tup, id=cnt_id)
def process(self, tup):
#TO DO: Add check for empty values
if tup.values[0]:
#"Try-Expect" will cause storm workers die
words = tup.values[0].split(" ")
if words:
for word in words:
storm.emit([word])
def process(self, tup):
print "SplitSentenceBolt.process:tup " + str(tup)
print "SplitSentenceBolt.process:tup.values[0] " + str(tup.values[0])
words = tup.values[0].split(" ")
print "SplitSentenceBolt.process:words " + str(words)
for word in words:
storm.emit([word])
print "SplitSentenceBolt.process:emit " + str(word)
def nextTuple(self):
time.sleep(0.2)
# TODO
# Task: randomly generate sentence from sentences string array
# Note: only generate one sentence in this function
sentence = random.choice(SENTENCES)
storm.logInfo("Emiting %s" % sentence)
storm.emit([sentence])
def nextTuple(self):
try:
line = self.f.readline()
storm.emit([line], id=self.tuple_id)
self.tuple_id += 1
except EOFError:
exit()
def process(self,tuple):
tweetid = tuple.values[0]
tweet_date = tuple.values[1]
tweettext = tuple.values[2]
country = tuple.values[3]
hashtags = tuple.values[4]
tweet_text = tuple.values[5]
score = afinn.score(tweettext)
storm.emit([tweetid, tweet_date, score, country, hashtags, tweet_text])
def process(self, tuple):
id = tuple.values[0]
url = tuple.values[1]
# open("/home/roy/output.txt", "a").write(url + '\n')
if url in TWEETERS_DB:
tweeters = TWEETERS_DB[url]
# open("/home/roy/output.txt", "a").write(str(tweeters) + '\n')
for tweeter in tweeters:
storm.emit([id, tweeter])
def process(self, tup):
# Get the word from the inbound tuple
word = tup.values[0]
# Increment the counter
self._counter[word] +=1
count = self._counter[word]
storm.logInfo("Emitting %s:%s" % (word, count))
# Emit the word and count
storm.emit([word, count])
def nextTuple(self):
if self.count < 2:
word = choice(words)
id = str(uuid4())
self.pending[id] = word
storm.rpcMetrics("my-custom-shellspout-metric", 1)
self.count = self.count + 1
storm.log("TesterSpout update my-custom-shellspout-metric "+str(self.count))
storm.emit([word], id=id)
def process(self, tuple):
id = tuple.values[0]
tweeter = tuple.values[1]
# open("/home/roy/output.txt", "a").write(str(tuple) + '\n')
if tweeter in FOLLOWERS_DB:
followers = FOLLOWERS_DB[tweeter]
# open("/home/roy/output.txt", "a").write(str(followers) + '\n')
for follower in followers:
storm.emit([id, follower])
def process(self, tuple): word = tuple.values[0] if word not in self.counts: count = 0 else: count = self.counts[word] count += 1 self.counts[word] = count storm.emit([word, count])
def write(self, output):
result = output if isinstance(output, list) else [output]
try:
storm.emit(result, stream=self.streamname)
storm.log("Dispel4Py ------> Emitted to stream %s." % (self.scriptname, self.streamname))
except TypeError:
# encode manually
encoded = encode_types(result)
storm.emit(encoded, stream=self.streamname)
storm.log("Dispel4Py ------> Emitted to stream %s." % (self.scriptname, self.streamname))
def process(self, tup):
word = tup.values[0]
if self.counts.has_key(word):
count = self.counts[word]
else:
count = 0
count += 1
self.counts[word] = count
storm.log(str(word)+" "+str(count))
storm.emit([word, count])
def process(self, tuple):
place = tuple.values[0]
try:
trends = get_tweets.get_trending_topics_text(self._twitter_api, place)
except tweepy.TweepError as te:
# We have hit the REST API Rate limit for Twitter https://dev.twitter.com/docs/rate-limiting/1.1, no more tweets for some time
log_tweeter_error(te, sleep_time=self._rate_limit_sleep_time)
return
for trend in trends:
storm.emit([place, trend['name'], trend['query']])
