def scan_and_queue(self,p_queue,p_index,p_query={},p_doctype=None,p_scroll_time='5m',p_timeout='1m'):
"""Reads docs from an es index according to a query and pushes them to the queue
p_queue: Queue where items are pushed to
p_scroll_time: Time for scroll method
p_timeout: Timeout - After this period, scan context is closed
p_index: Index where items are picked from
p_doctype: DocType of the items
p_query: ElasticSearch query for scanning the index
"""
try:
param = [{'host':self.host,'port':self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server for reading: %s',json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server for reading: %s',json.dumps(param))
logger.error(e)
sys.exit(EXIT_IO_ERROR)
try:
if 'p_doctype' is not None:
documents = helpers.scan(client=es, query=p_query, size=1000, scroll=p_scroll_time, index=p_index, doc_type=p_doctype, timeout=p_timeout)
else:
documents = helpers.scan(client=es, query=p_query, size=1000, scroll= p_scroll_time, index=p_index, timeout=p_timeout)
for doc in documents:
logger.debug(doc)
p_queue.put(doc)
except Exception as e:
logger.info("Error while scanning ES index %s with query %s",p_index,p_query)
def __init__(self,p_host,p_port,p_base,p_user,p_password,p_connect_timeout=60000):
"""Class creation
p_host: Mongo Server address
p_port: Mongo Server port
p_base: Mongo base
p_user: Mongo user
p_password: Mongo password
"""
self.host = p_host
self.port = p_port
self.base = p_base
self.user = p_user
self.password = p_password
self.connect_timeout = p_connect_timeout
# uri for mongo connection
uri = 'mongodb://%s:%s@%s:%s/%s?connectTimeoutMS=' % (self.user,self.password,self.host,self.port,self.base,self.connect_timeout)
# Connect to mongo
try:
mongo_client = MongoClient(uri)
self.mongo = mongo_client[self.base]
logger.info('Connection succeeded on %s',uri)
except PyMongoError as e:
logger.error('Failed to connect to %s',uri)
logger.error(e)
sys.exit(EXIT_IO_ERROR)
def set_settings(self, p_index, p_conf):
"""Sets the index settings
"""
try:
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
index.set_settings(p_conf)
logger.info('Index %s set', p_index)
except Exception as e:
logger.error('Error setting the index %s', p_index)
logger.error(e)
def set_settings(self,p_index,p_conf):
"""Sets the index settings
"""
try:
client = algoliasearch.Client(self.app_id,self.api_key)
index = client.init_index(p_index)
index.set_settings(p_conf)
logger.info('Index %s set',p_index)
except Exception as e:
logger.error('Error setting the index %s',p_index)
logger.error(e)
def get_settings(self, p_index):
"""Gets the index settings
"""
try:
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
result = index.get_settings()
logger.info('Index %s get', p_index)
return result
except Exception as e:
logger.error('Error getting settings of %s', p_index)
logger.error(e)
def get_settings(self, p_index):
"""Gets the index settings
"""
try:
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
result = index.get_settings()
logger.info('Index %s get', p_index)
return result
except Exception as e:
logger.error('Error getting settings of %s', p_index)
logger.error(e)
def remove_items(self, p_collection, p_query):
"""Execute a delete query on collection using p_query selection
p_collection: mongo collection where to store the docs;
p_query: selection query
"""
try:
self.mongo[p_collection].remove(p_query)
logger.info('Collection items removal done')
except PyMongoError as e:
logger.error('Failed to remove entries from %s',p_collection)
logger.error(e)
sys.exit(EXIT_IO_ERROR)
def count_items(self, p_collection, p_query):
"""Return item count using p_query selection
p_collection: mongo collection to query;
p_query: selection query
"""
try:
return self.mongo[p_collection].find(p_query).count()
except PyMongoError as e:
logger.error('Failed to count entries from %s',p_collection)
logger.error(e)
sys.exit(EXIT_IO_ERROR)
def clear_index(self, p_index):
"""Deletes and index
- p_index: index to delete
- returns true if p_index has been deleted, false if not
"""
delete_ok = True
try:
param = [{'host': self.host, 'port': self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s', json.dumps(param))
logger.error(e)
delete_ok = False
try:
es.indices.delete(index=p_index)
logger.info('Index %s deleted', p_index)
except Exception as e:
logger.error('Error deleting the index %s', p_index)
logger.error(e)
delete_ok = False
return delete_ok
def remove_items(self, p_collection, p_query):
"""Execute a delete query on collection using p_query selection
p_collection: mongo collection where to store the docs;
p_query: selection query
"""
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
try:
mongo_connect[p_collection].remove(p_query)
logger.info('Collection items removal done')
except PyMongoError as e:
logger.error('Failed to remove entries from %s', p_collection)
logger.error(e)
def count_items(self, p_collection, p_query):
"""Return item count using p_query selection
p_collection: mongo collection to query;
p_query: selection query
"""
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
try:
return mongo_connect[p_collection].find(p_query).count()
except PyMongoError as e:
logger.error('Failed to count entries from %s', p_collection)
logger.error(e)
def count(self, p_index, p_query={}):
"""Gets the number of docs for a query
p_index: elasticsearch index where to query
p_query: the query to process
return the number of docs from the index p_index and the query p_query
"""
try:
param = [{'host': self.host, 'port': self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s', json.dumps(param))
logger.error(e)
sys.exit(EXIT_IO_ERROR)
try:
result = es.count(index=p_index, body=p_query)
logger.info('Count the number of items from %s for the query %s', p_index, p_query)
except Exception as e:
logger.error('Error querying the index %s with query %s', p_index, p_query)
logger.error(e)
return result['count']
def remove_items(self, p_collection, p_query):
"""Execute a delete query on collection using p_query selection
p_collection: mongo collection where to store the docs;
p_query: selection query
"""
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
try:
mongo_connect[p_collection].remove(p_query)
logger.info('Collection items removal done')
except PyMongoError as e:
logger.error('Failed to remove entries from %s', p_collection)
logger.error(e)
def clear_index(self, p_index):
"""Deletes and index
- p_index: index to delete
- returns true if p_index has been deleted, false if not
"""
delete_ok = True
try:
param = [{'host': self.host, 'port': self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s',
json.dumps(param))
logger.error(e)
delete_ok = False
try:
es.indices.delete(index=p_index)
logger.info('Index %s deleted', p_index)
except Exception as e:
logger.error('Error deleting the index %s', p_index)
logger.error(e)
delete_ok = False
return delete_ok
def count(self, p_index, p_query={}):
"""Gets the number of docs for a query
p_index: elasticsearch index where to query
p_query: the query to process
return the number of docs from the index p_index and the query p_query
"""
try:
param = [{'host': self.host, 'port': self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s',
json.dumps(param))
logger.error(e)
sys.exit(EXIT_IO_ERROR)
try:
result = es.count(index=p_index, body=p_query)
logger.info('Count the number of items from %s for the query %s',
p_index, p_query)
except Exception as e:
logger.error('Error querying the index %s with query %s', p_index,
p_query)
logger.error(e)
return result['count']
def set_mapping(self, p_index, p_mapping):
"""Create an index with a given p_mapping
- p_index: index to delete
- p_mapping: mapping forced
"""
try:
param = [{'host': self.host, 'port': self.port}]
if self.proxy is None:
es = Elasticsearch(param)
else:
es = Elasticsearch(param,
connection_class=MyConnection,
proxies={'http': self.proxy})
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s',
json.dumps(param))
logger.error(e)
try:
es.indices.create(index=p_index, body=p_mapping)
logger.info('Index %s created', p_index)
except Exception as e:
logger.error('Error creating the index %s', p_index)
logger.error(e)
def count_items(self, p_collection, p_query):
"""Return item count using p_query selection
p_collection: mongo collection to query;
p_query: selection query
"""
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
try:
return mongo_connect[p_collection].find(p_query).count()
except PyMongoError as e:
logger.error('Failed to count entries from %s', p_collection)
logger.error(e)
def scan_and_queue(self, p_queue, p_collection, p_query, p_batch_size=100):
"""Reads docs from a collection according to a query and pushes them to the queue
p_queue: Queue where items are pushed to
p_collection: Collection where items are picked from
p_query: MongoDB query for scanning the collection
p_batch_size: Number of read docs by iteration
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level,
self.formatter)
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
# Scan collection according to the query
documents = mongo_connect[p_collection].find(p_query)
nb_docs = documents.count()
logger.info('Scanning %i items in %s', nb_docs, p_collection)
# Each items is put into the queue
documents.batch_size(p_batch_size)
start_time = time.time()
for doc in documents:
p_queue.put(doc)
with self.counters['nb_items_scanned'].get_lock():
self.counters['nb_items_scanned'].value += 1
if self.counters['nb_items_scanned'].value % self.counters[
'log_every'] == 0:
logger.info(
"Scan in progress : {0} items read from source".format(
self.counters['nb_items_scanned'].value))
# logger.warn('In Queue size : %i',p_queue.qsize())
time_for_x_items = time.time()
if nb_docs == 0:
logger.info("No document to process")
else:
logger.info("Average reading time : %fs",
(time_for_x_items - start_time) / nb_docs)
def scan_and_queue(self,
p_queue,
p_index,
p_query={},
p_connect_timeout=1,
p_read_timeout=30):
"""Reads docs from an Algolia index according to a query and pushes them to the queue
p_queue: Queue where items are pushed to
p_index: Index where items are picked from
p_query: query for scanning the index
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level,
self.formatter)
try:
client = algoliasearch.Client(self.app_id, self.api_key)
client.timeout = (p_connect_timeout, p_read_timeout)
index = client.init_index(p_index)
except Exception as e:
logger.error(e)
sys.exit(EXIT_IO_ERROR)
try:
documents = index.browse_all(p_query)
start = time.time()
for doc in documents:
p_queue.put(doc)
elapsed = time.time() - start
with self.counters['nb_items_scanned'].get_lock():
self.counters['nb_items_scanned'].value += 1
nb_items = self.counters['nb_items_scanned'].value
self.counters['scan_time'].value += elapsed
if nb_items % self.counters['log_every'] == 0:
logger.info("Scan : {0} items".format(nb_items))
logger.debug(" -> Avg scan time : {0}ms".format(
1000 * self.counters['scan_time'].value /
nb_items))
# Start timers reinit
start = time.time()
except Exception as e:
logger.info("Error while scanning Algolia index %s with query %s",
p_index, p_query)
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += 1
def dequeue_and_store(self,p_queue, p_collection):
"""Gets docs from p_queue and stores them in a mongo collection
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_collection: mongo collection where to store the docs;
"""
# uri for mongo connection
uri = 'mongodb://%s:%s@%s:%s/%s?connectTimeoutMS=' % (self.user,self.password,self.host,self.port,self.base,self.connect_timeout)
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connection = mongo_client[self.base]
logger.info('Connection succeeded on %s',uri)
except PyMongoError as e:
logger.error('Failed to connect to %s',uri)
logger.error(e)
sys.exit(EXIT_IO_ERROR)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
poison_pill = False
while not(poison_pill):
try:
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
logger.debug("Mongoio has received 'poison pill' and is now ending ...")
poison_pill = True
p_queue.task_done()
break
#management of 'update/set' style request
try:
find = source_doc['_mongo_find']
except KeyError:
find = {'_id':source_doc['_id']}
try:
update = source_doc['_mongo_update']
except KeyError:
update = source_doc
#insert into collection
try:
mongo_connection[p_collection].update(find,update,upsert=True)
except Exception as e:
logger.error("Document not inserted in Mongo Collection %s", source_doc['_id'])
logger.error(e)
p_queue.task_done()
except KeyboardInterrupt:
logger.info("Mongoio.dequeue_and_store : User interruption of the process")
sys.exit(EXIT_USER_INTERRUPT)
def scan_and_queue(self, p_queue, p_collection, p_query, p_batch_size=100):
"""Reads docs from a collection according to a query and pushes them to the queue
p_queue: Queue where items are pushed to
p_collection: Collection where items are picked from
p_query: MongoDB query for scanning the collection
p_batch_size: Number of read docs by iteration
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level, self.formatter)
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
sys.exit(EXIT_IO_ERROR)
# Scan collection according to the query
documents = mongo_connect[p_collection].find(p_query)
nb_docs = documents.count()
logger.info('Scanning %i items in %s', nb_docs, p_collection)
# Each items is put into the queue
documents.batch_size(p_batch_size)
start_time = time.time()
for doc in documents:
p_queue.put(doc)
with self.counters['nb_items_scanned'].get_lock():
self.counters['nb_items_scanned'].value += 1
if self.counters['nb_items_scanned'].value % self.counters['log_every'] == 0:
logger.info("Scan in progress : {0} items read from source".format(self.counters['nb_items_scanned'].value))
# logger.warn('In Queue size : %i',p_queue.qsize())
time_for_x_items = time.time()
if nb_docs == 0:
logger.info("No document to process")
else:
logger.info("Average reading time : %fs", (time_for_x_items - start_time)/nb_docs)
def delete_document(self, p_index, p_id):
"""Deletes a doc from an index
- p_index: index where to delete the doc
- p_id: id of the doc to delete
"""
delete_ok = True
try:
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
index.delete_object(p_id)
logger.info('%s deleted from %s', p_id, p_index)
except Exception as e:
logger.error('Error deleting the %s from index %s', p_id, p_index)
logger.error(e)
delete_ok = False
return delete_ok
def delete_document(self,p_index,p_id):
"""Deletes a doc from an index
- p_index: index where to delete the doc
- p_id: id of the doc to delete
"""
delete_ok = True
try:
client = algoliasearch.Client(self.app_id,self.api_key)
index = client.init_index(p_index)
index.delete_object(p_id)
logger.info('%s deleted from %s',p_id, p_index)
except Exception as e:
logger.error('Error deleting the %s from index %s',p_id, p_index)
logger.error(e)
delete_ok = False
return delete_ok
def clear_index(self, p_index):
"""Deletes and index
- p_index: index to delete
- returns true if p_index has been deleted, false if not
"""
delete_ok = True
try:
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
index.clear_index()
logger.info('Index %s deleted', p_index)
except Exception as e:
logger.error('Error deleting the index %s', p_index)
logger.error(e)
delete_ok = False
return delete_ok
def clear_index(self,p_index):
"""Deletes and index
- p_index: index to delete
- returns true if p_index has been deleted, false if not
"""
delete_ok = True
try:
client = algoliasearch.Client(self.app_id,self.api_key)
index = client.init_index(p_index)
index.clear_index()
logger.info('Index %s deleted',p_index)
except Exception as e:
logger.error('Error deleting the index %s',p_index)
logger.error(e)
delete_ok = False
return delete_ok
def scan_and_queue(self,p_queue,p_file, p_xpath):
"""Reads xml files in a directory and pushes them to the queue
p_queue: Queue where items are pushed to
p_file: XML File to scan
p_xpath: XPATH used to split document into multiple docs
"""
logger.info('Scanning xml in %s', p_file)
start_time = datetime.datetime.now()
tree = ET.parse(p_file)
root = tree.getroot()
# Each items is put into the queue
compteur = 0
if p_xpath:
nodeList=root.findall(p_xpath)
else:
nodeList=[root]
for foundElem in nodeList:
compteur = compteur + 1
#logger.debug("queue size=",p_queue.qsize())
#logger.debug(ET.tostring(foundElem, encoding="us-ascii", method="xml"))
try:
p_queue.put(ET.tostring(foundElem, encoding="us-ascii", method="xml"))
except Exception as e:
logger.error(e)
# start_time = datetime.datetime.now()
# for doc in documents.skip(p_skip).limit(p_skip+p_limit):
#for doc in documents:
# compteur = compteur + 1
# if compteur % 500 == 0:
# elsapsed_time = datetime.datetime.now() - start_time
# start_time = datetime.datetime.now()
# logger.info('Pushing item number %i in the queue in %s',compteur,elsapsed_time)
# logger.info('Pushing item number %i in the queue',compteur)
# p_queue.put(doc)
def scan_and_queue(self, p_queue, p_index, p_query={}, p_connect_timeout=1, p_read_timeout=30):
"""Reads docs from an Algolia index according to a query and pushes them to the queue
p_queue: Queue where items are pushed to
p_index: Index where items are picked from
p_query: query for scanning the index
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level, self.formatter)
try:
client = algoliasearch.Client(self.app_id, self.api_key)
client.timeout = (p_connect_timeout, p_read_timeout)
index = client.init_index(p_index)
except Exception as e:
logger.error(e)
sys.exit(EXIT_IO_ERROR)
try:
documents = index.browse_all(p_query)
start = time.time()
for doc in documents:
p_queue.put(doc)
elapsed = time.time() - start
with self.counters['nb_items_scanned'].get_lock():
self.counters['nb_items_scanned'].value += 1
nb_items = self.counters['nb_items_scanned'].value
self.counters['scan_time'].value += elapsed
if nb_items % self.counters['log_every'] == 0:
logger.info("Scan : {0} items".format(nb_items))
logger.debug(" -> Avg scan time : {0}ms".format(1000*self.counters['scan_time'].value / nb_items))
# Start timers reinit
start = time.time()
except Exception as e:
logger.info("Error while scanning Algolia index %s with query %s", p_index, p_query)
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += 1
def set_mapping(self, p_index, p_mapping):
"""Create an index with a given p_mapping
- p_index: index to delete
- p_mapping: mapping forced
"""
try:
param = [{'host': self.host, 'port': self.port}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s', json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s', json.dumps(param))
logger.error(e)
try:
es.indices.create(index=p_index, body=p_mapping)
logger.info('Index %s created', p_index)
except Exception as e:
logger.error('Error creating the index %s', p_index)
logger.error(e)
def dequeue_and_store(self,p_queue,p_index,p_nbmax_retry=3):
"""Gets docs from p_queue and stores them in the algolia
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_index: algolia index where to store the docs
p_nbmax_retry: number of tries when failing on a request (default is 3)
"""
client = algoliasearch.Client(self.app_id,self.api_key)
index = client.init_index(p_index)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
poison_pill = False
while not(poison_pill):
try:
bulk = []
while (len(bulk) < self.bulk_size):
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
logger.debug("ESio has received 'poison pill' and is now ending ...")
poison_pill = True
p_queue.task_done()
break
bulk.append(source_doc)
p_queue.task_done()
try_counter = 1
is_indexed = False
while try_counter <= p_nbmax_retry and not is_indexed:
try:
# Bulk indexation
if len(bulk) > 0:
logger.debug("Indexing %i documents",len(bulk))
index.add_objects(bulk)
except Exception as e:
logger.error("Bulk not indexed in algolia - Retry number %i",try_counter)
logger.error(e)
try_counter += 1
else:
is_indexed = True
if not is_indexed:
logger.error("Bulk not indexed in algolia : operation aborted after %i retries",try_counter-1)
except KeyboardInterrupt:
logger.info("ESio.dequeue_and_store : User interruption of the process")
sys.exit(1)
def dequeue_and_store(self,p_queue,p_index,p_timeout=10,p_nbmax_retry=3):
"""Gets docs from p_queue and stores them in the csv file
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_index: elasticsearch index where to store the docs
p_timeout: timeout for bulk (default is 10s)
p_nbmax_retry: number of tries when failing on a request (default is 3)
"""
try:
param = [{'host':self.host,'port':self.port,'timeout':p_timeout,'max_retries':p_nbmax_retry,'retry_on_timeout':True}]
es = Elasticsearch(param)
logger.info('Connected to ES Server: %s',json.dumps(param))
except Exception as e:
logger.error('Connection failed to ES Server : %s',json.dumps(param))
logger.error(e)
sys.exit(EXIT_IO_ERROR)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
poison_pill = False
while not(poison_pill):
try:
bulk = []
while (len(bulk) < self.bulk_size):
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
logger.debug("ESio has received 'poison pill' and is now ending ...")
poison_pill = True
p_queue.task_done()
break
# Bulk element creation from the source_doc
source_doc['_index'] = p_index
bulk.append(source_doc)
p_queue.task_done()
try_counter = 1
is_indexed = False
while try_counter <= p_nbmax_retry and not is_indexed:
try:
# Bulk indexation
if len(bulk) > 0:
logger.debug("Indexing %i documents",len(bulk))
helpers.bulk(es, bulk, raise_on_error=True)
# es.index(index=self.index,doc_type=p_doctype,body=source_doc)
except Exception as e:
logger.error("Bulk not indexed in ES - Retry n°%i",try_counter)
logger.error(e)
try_counter += 1
else:
is_indexed = True
if not is_indexed:
logger.error("Bulk not indexed in elasticsearch : operation aborted after %i retries",try_counter-1)
except KeyboardInterrupt:
logger.info("ESio.dequeue_and_store : User interruption of the process")
sys.exit(EXIT_USER_INTERRUPT)
def dequeue_and_store(self, p_queue, p_collection, p_upsert=True):
"""Gets docs from p_queue and stores them in a mongo collection
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_collection: mongo collection where to store the docs;
p_upsert: if true, new documents are created, if false they are ignored
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level, self.formatter)
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
# Main loop max retry
main_loop_max_retry = 5
main_loop_retry = 0
poison_pill = False
while not(poison_pill):
try:
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
poison_pill = True
p_queue.task_done()
break
# management of 'update/set' style request
try:
find = source_doc['_mongo_find']
except KeyError:
find = {'_id': source_doc['_id']}
try:
update = source_doc['_mongo_update']
except KeyError:
update = source_doc
# insert into collection
try:
mongo_connect[p_collection].update(find, update, upsert=p_upsert, multi=True if '_mongo_update' in source_doc else False)
except Exception as e:
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += 1
logger.error("Document not inserted in Mongo Collection %s", source_doc['_id'])
logger.error(e)
else:
with self.counters['nb_items_stored'].get_lock():
self.counters['nb_items_stored'].value += 1
if self.counters['nb_items_stored'].value % self.counters['log_every'] == 0:
logger.info("Storage in progress : {0} items written to target".format(self.counters['nb_items_stored'].value))
p_queue.task_done()
except KeyboardInterrupt:
logger.info("Mongoio.dequeue_and_store : User interruption of the process")
poison_pill = True
p_queue.task_done()
except Exception as e:
logger.error("An error occured while storing elements to Mongo : {0}".format(e))
main_loop_retry += 1
if main_loop_retry >= main_loop_max_retry:
logger.error("Too many errors while storing. Process interrupted after {0} errors".format(main_loop_retry))
poison_pill = True
p_queue.task_done()
def dequeue_and_store(self, p_queue, p_index, p_nbmax_retry=3):
"""Gets docs from p_queue and stores them in the algolia
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_index: algolia index where to store the docs
p_nbmax_retry: number of tries when failing on a request (default is 3)
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level,
self.formatter)
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
start = time.time()
poison_pill = False
while not (poison_pill):
try:
bulk = []
while (len(bulk) < self.bulk_size):
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
poison_pill = True
p_queue.task_done()
break
bulk.append(source_doc)
p_queue.task_done()
try_counter = 1
is_indexed = False
while try_counter <= p_nbmax_retry and not is_indexed:
start_bulking = time.time()
try:
# Bulk indexation
if len(bulk) > 0:
index.add_objects(bulk)
except Exception as e:
logger.error(
"Bulk not indexed in algolia - Retry number %i",
try_counter)
logger.error(e)
try_counter += 1
else:
is_indexed = True
now = time.time()
elapsed_bulking = now - start_bulking
elapsed = now - start
with self.counters['nb_items_stored'].get_lock():
self.counters['nb_items_stored'].value += len(bulk)
self.counters[
'whole_storage_time'].value += elapsed
self.counters[
'bulk_storage_time'].value += elapsed_bulking
nb_items = self.counters['nb_items_stored'].value
if nb_items % self.counters[
'log_every'] == 0 and nb_items != 0:
logger.info(
"Store : {0} items".format(nb_items))
logger.debug(
" -> Avg store time : {0}ms".format(
1000 * self.
counters['whole_storage_time'].value /
nb_items))
logger.debug(
" -> Avg bulk time : {0}ms".format(
1000 * self.
counters['bulk_storage_time'].value /
nb_items))
start = time.time()
if not is_indexed:
start = time.time()
logger.error(
"Bulk not indexed in algolia : operation aborted after %i retries",
try_counter - 1)
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += len(bulk)
except KeyboardInterrupt:
logger.info(
"ESio.dequeue_and_store : User interruption of the process"
)
sys.exit(1)
def dequeue_and_store(self, p_queue, p_collection, p_upsert=True):
"""Gets docs from p_queue and stores them in a mongo collection
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_collection: mongo collection where to store the docs;
p_upsert: if true, new documents are created, if false they are ignored
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level,
self.formatter)
# uri for mongo connection
uri = self._get_mongo_uri()
# Connect to mongo
try:
mongo_client = MongoClient(uri)
mongo_connect = mongo_client[self.base]
logger.info('Connection succeeded on %s', uri)
except PyMongoError as e:
logger.error('Failed to connect to %s', uri)
logger.error(e)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
# Main loop max retry
main_loop_max_retry = 5
main_loop_retry = 0
poison_pill = False
while not (poison_pill):
try:
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
poison_pill = True
p_queue.task_done()
break
# management of 'update/set' style request
try:
find = source_doc['_mongo_find']
except KeyError:
find = {'_id': source_doc['_id']}
try:
update = source_doc['_mongo_update']
except KeyError:
update = source_doc
# insert into collection
try:
mongo_connect[p_collection].update(
find,
update,
upsert=p_upsert,
multi=True if '_mongo_update' in source_doc else False)
except Exception as e:
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += 1
logger.error(
"Document not inserted in Mongo Collection %s",
source_doc['_id'])
logger.error(e)
else:
with self.counters['nb_items_stored'].get_lock():
self.counters['nb_items_stored'].value += 1
if self.counters[
'nb_items_stored'].value % self.counters[
'log_every'] == 0:
logger.info(
"Storage in progress : {0} items written to target"
.format(
self.counters['nb_items_stored'].value))
p_queue.task_done()
except KeyboardInterrupt:
logger.info(
"Mongoio.dequeue_and_store : User interruption of the process"
)
poison_pill = True
p_queue.task_done()
except Exception as e:
logger.error(
"An error occured while storing elements to Mongo : {0}".
format(e))
main_loop_retry += 1
if main_loop_retry >= main_loop_max_retry:
logger.error(
"Too many errors while storing. Process interrupted after {0} errors"
.format(main_loop_retry))
poison_pill = True
p_queue.task_done()
def dequeue_and_store(self, p_queue, p_index, p_nbmax_retry=3):
"""Gets docs from p_queue and stores them in the algolia
Stops dealing with the queue when receiving a "None" item
p_queue: queue wich items are picked from. Elements has to be "list".
p_index: algolia index where to store the docs
p_nbmax_retry: number of tries when failing on a request (default is 3)
"""
logger = get_logger_mp(__name__, self.log_queue, self.log_level, self.formatter)
client = algoliasearch.Client(self.app_id, self.api_key)
index = client.init_index(p_index)
# Loop untill receiving the "poison pill" item (meaning : no more element to read)
# Main loop max retry
main_loop_max_retry = 5
main_loop_retry = 0
start = time.time()
poison_pill = False
while not(poison_pill):
try:
bulk = []
while (len(bulk) < self.bulk_size):
source_doc = p_queue.get()
# Manage poison pill
if source_doc is None:
poison_pill = True
p_queue.task_done()
break
bulk.append(source_doc)
p_queue.task_done()
try_counter = 1
is_indexed = False
while try_counter <= p_nbmax_retry and not is_indexed:
start_bulking = time.time()
try:
# Bulk indexation
if len(bulk) > 0:
index.add_objects(bulk)
except Exception as e:
logger.error("Bulk not indexed in algolia - Retry number %i", try_counter)
logger.error(e)
try_counter += 1
else:
is_indexed = True
now = time.time()
elapsed_bulking = now - start_bulking
elapsed = now - start
with self.counters['nb_items_stored'].get_lock():
self.counters['nb_items_stored'].value += len(bulk)
self.counters['whole_storage_time'].value += elapsed
self.counters['bulk_storage_time'].value += elapsed_bulking
nb_items = self.counters['nb_items_stored'].value
if nb_items % self.counters['log_every'] == 0 and nb_items != 0:
logger.info("Store : {0} items".format(nb_items))
logger.debug(" -> Avg store time : {0}ms".format(1000 * self.counters['whole_storage_time'].value / nb_items))
logger.debug(" -> Avg bulk time : {0}ms".format(1000 * self.counters['bulk_storage_time'].value / nb_items))
start = time.time()
if not is_indexed:
start = time.time()
logger.error("Bulk not indexed in algolia : operation aborted after %i retries", try_counter - 1)
with self.counters['nb_items_error'].get_lock():
self.counters['nb_items_error'].value += len(bulk)
except KeyboardInterrupt:
logger.info("ESio.dequeue_and_store : User interruption of the process")
poison_pill = True
p_queue.task_done()
except Exception as e:
logger.error("An error occured while storing elements to Algolia : {0}".format(e))
main_loop_retry += 1
if main_loop_retry >= main_loop_max_retry:
logger.error("Too many errors while storing. Process interrupted after {0} errors".format(main_loop_retry))
poison_pill = True
p_queue.task_done()
