def __init__(self, config, consumer_mode, to_producer=True):
''' Init a consumer based on mode activated in input '''
self.config = config
self.config_section = consumer_mode
self.to_producer = to_producer
config_params = self.get_config_items()
try:
self.kafka_hosts = config_params['kafka_hosts']
self.in_topic = config_params['in_topic']
self.out_topic = config_params['out_topic']
self.group = config_params['in_group']
self.zk_hosts = config_params['zookeeper_hosts']
except KeyError:
raise
uf.print_out("Trying to make connection {}".format(self.in_topic))
self.client = KafkaClient(hosts=self.kafka_hosts) # Create a client
self.topic = self.client.topics[self.in_topic] # create topic if not exists
self.consumer = self.topic.get_balanced_consumer( # Zookeeper dynamically assigns partitions
consumer_group=self.group,
auto_commit_enable=True,
zookeeper_connect=self.zk_hosts)
uf.print_out("Made connection")
if self.to_producer: # write into producer
try:
self.out_group = config_params['out_group']
self.out_topic = self.client.topics[config_params['out_topic']]
except KeyError:
raise
else:
self.output = uf.mkdir_if_not_exist() # write to /tmp/exstreamly_cheap
uf.print_out("Created output file or producer stage")
self.partitions = set()
self.msg_cnt = 0 # Num consumed by instance.
self.init_time = datetime.now()
self.start_time = self.init_time
self.url_queue = Queue(maxsize=0) # infinitely sized
self.semaphore = BoundedSemaphore()
os.system('hdfs dfs -put {} {}'.format(self.temp_file_path, cached_fullpath))
#uf.print_out('Removing temporary file - {}'.format(os.path.basename(self.temp_file_path)))
# os.remove(self.temp_file_path)
timestamp = time.strftime('%Y%m%d%H%M%S')
self.temp_file_path = '{}/kafka_{}_{}_{}.dat'.format(output_dir, self.out_topic, self.group, timestamp)
self.temp_file = open(self.temp_file_path, "w")
def get_config_items(self):
''' Retrieve relevant config settings for section
applicable to this type of instance for
group, in_topic, out_topic if available
'''
try:
return dict(self.config.items(self.config_section))
except configparser.NoSectionError:
raise configparser.NoSectionError('No section: {} exists in the config file'
.format(self.config_section))
if __name__ == '__main__':
tmp_out_dir = '/home/ubuntu/exstreamly_cheap_all_deals/ingestion/kafka_messages'
tmp_out_dir = uf.mkdir_if_not_exist(tmp_out_dir)
uf.print_out('Output format: {}'.format(tmp_out_dir))
config = configparser.SafeConfigParser()
config.read('../../config/general.conf')
print '\nConsuming messages...'
cons = ConsumerToHDFS(config, settings.CONSUMER_MODE_DATA)
cons.consume_topic(tmp_out_dir)
def fetch_and_clean_up(index_name):
""" Fetch Elastic data and clean it up """
# Logstash and HDFS general info
output_dir = uf.mkdir_if_not_exist("/tmp/exstreamly_cheap_files/elasticsearch_cleanup")
# logstash_file = os.path.join(output_dir, 'clean_deals.json')
# HDFS Related data
group = "deals_data_hdfs"
topic_id = "elastic_deals_data"
timestamp = time.strftime("%Y%m%d%H%M%S")
hadoop_file = os.path.join(output_dir, "hdfs_{}.dat".format(timestamp))
hadoop_path = "/exstreamly_cheap_main_files/all_deals/history"
cached_path = "/exstreamly_cheap_main_files/all_deals/cached"
hadoop_fullpath = "{}/{}_{}_{}.dat".format(hadoop_path, group, topic_id, timestamp)
cached_fullpath = "{}/{}_{}_{}.dat".format(cached_path, group, topic_id, timestamp)
uf.print_out("Writing the logs to {} which will be pushed to hdfs and S3".format(hadoop_file))
block_cnt = 0
client = make_client()
cc = Search(using=client, index=index_name)
gen = cc.scan()
config = configparser.SafeConfigParser()
config.read("../../config/general.conf")
config_params = uf.get_config_items(config, settings.PRODUCER_CLEAN_ES_DATA)
try:
kafka_hosts = config_params["kafka_hosts"]
topic = config_params["topic"]
group = config_params["group"]
zk_hosts = config_params["zookeeper_hosts"]
except KeyError:
raise
kafka_client = KafkaClient(hosts=kafka_hosts)
kafka_topic = kafka_client.topics[topic] # Create if not exist
uf.print_out("Producing messages to topic {}. Press Ctrl-C to terminate".format(kafka_topic.name))
# Produce to kafka for distributed consumption
hdp_output = open(hadoop_file, "w")
with kafka_topic.get_producer() as producer:
for event in gen:
new_string = dict(eval(event.message.encode("utf-8")))
msg = clean_data(new_string)
# We can decide to have logstash read from file instead
# with open(logstash_file, 'a') as log_output:
# log_output.write(json.dumps(msg) + '\n')
# Write to producer.
producer.produce(json.dumps(msg))
# Back up to file for HDFS and S3
hdp_output.write(json.dumps(msg) + "\n")
if hdp_output.tell() > 100000000:
hdp_output.close()
uf.print_out("Block {}: Flushing 100MB file to HDFS => {}".format(str(block_cnt), hadoop_fullpath))
# place blocked messages into history and cached folders on hdfs
os.system("hdfs dfs -put {} {}".format(hadoop_file, hadoop_fullpath))
os.system("hdfs dfs -put {} {}".format(hadoop_file, cached_fullpath))
# Back up in S3
uf.print_out("Syncing {} to S3 for back up".format(output_dir))
os.system("aws s3 sync {} s3://emmanuel-awa/clean_data_from_elastic".format(output_dir))
# Recreate file handler
hadoop_file = os.path.join(output_dir, "hdfs_{}.dat".format(time.strftime("%Y%m%d%H%M%S")))
hdp_output = open(hadoop_file, "w")
uf.print_out("Cleaned {} blocks. File size: {}KB".format(block_cnt, hdp_output.tell() / 1000))
block_cnt += 1
def fetch_sqoot_data(base_url):
''' Fetch Sqoot Data and save relevant information to file '''
files_location = uf.mkdir_if_not_exist() # Folder in /tmp/exstreamly_cheap_files
merchants_file = os.path.join(files_location, 'merchants.json')
products_file = os.path.join(files_location, 'products.json')
events_file = os.path.join(files_location, 'activities_events.json')
food_nitelife_file = os.path.join(files_location, 'dining_nitelife.json')
categories_map = map_categories(base_url)
mvp_categories = [u'product', u'dining-nightlife', u'activities-events']
focus_grp = reduce_categories_scope(categories_map,
mvp_categories)
start_time = datetime.datetime.now()
end_time = start_time + datetime.timedelta(hours=7)
all_deals = []
queue = Queue.Queue()
while start_time < end_time:
try:
# Due to api inconsistencies, to always get the newest ones and page 5
# Duplicates will be batchly processed in SPARK
# Combine both
# Flatten JSON, keep online merchant ID in deals file
# Save Merchant in Merchant Table
# first_100_deals = get_request(base_url, 'deals', 'per_page=100;radius=10000')
# all_deals = all_deals + first_100_deals.json()['deals']
uf.print_out('Crawling first 100 pages')
for num in xrange(1, 101):
uf.print_out('.' * num)
thread_ = threading.Thread(target=get_request, name='Thread{}'.format(num), args=[base_url, 'deals', 'page={};per_page=100;radius=10000'.format(num), queue])
thread_.start()
thread_.join()
while not queue.empty():
all_deals = all_deals + queue.get()
for idx, deal in enumerate(all_deals):
uf.print_out('Processing deal: {}'.format(idx))
# If deal category belongs to mvp, save
category = category_in_mvp(focus_grp, deal['deal']['category_slug'])
if category:
output = OrderedDict()
output['id'] = deal['deal']['id']
output['category'] = category
output['sub_category'] = deal['deal']['category_slug']
output['title'] = deal['deal']['short_title']
output['description'] = deal['deal']['description']
output['fine_print'] = deal['deal']['fine_print']
output['number_sold'] = deal['deal']['number_sold']
output['url'] = deal['deal']['untracked_url']
output['price'] = deal['deal']['price']
output['discount_percentage'] = deal['deal']['discount_percentage']
output['provider_name'] = deal['deal']['provider_name']
output['online'] = deal['deal']['online']
output['expires_at'] = deal['deal']['expires_at']
output['created_at'] = deal['deal']['created_at']
output['updated_at'] = deal['deal']['updated_at']
output['merchant_id'] = deal['deal']['merchant']['id']
# Write deal to file
with open(os.path.join(files_location, str(category) + '.json'), 'a') as f:
f.write(json.dumps(output))
f.write('\n')
# Write merchant info file
merchant_info = deal['deal']['merchant']
if not all(merchant_info.values()):
merchant_info = clean_merchant_info(merchant_info)
with open(os.path.join(files_location, 'merchants.json'), 'a') as f:
f.write(json.dumps(merchant_info))
f.write('\n')
start_time = datetime.datetime.now()
uf.print_out("Time left: {} minute(s)".format((end_time - start_time).seconds / 60))
uf.print_out("Waiting 30mins to crawl again")
uf.spinning_cursor(1800)
except rq.exceptions.ConnectionError:
uf.print_out("[ConnectionError] ==> Issue with API server.")
except rq.exceptions.ConnectTimeout:
uf.print_out("[ConnectionTimeout] ==> Server connection timing out.")
