def start_importing(es, cliargs, logger):
"""Start importing s3 inventory file function.
"""
for i in range(4):
thread = Thread(target=csv_file_reader, args=(s3queue, ))
thread.daemon = True
thread.start()
# start importing S3 inventory file(s)
inventory_files = cliargs['s3']
logger.info('Importing %s S3 inventory file(s)...' % len(inventory_files))
# add fake disk space to index with path set to /s3
data = {
"path": '/s3',
"total": 0,
"used": 0,
"free": 0,
"available": 0,
"indexing_date": datetime.utcnow().isoformat()
}
es.index(index=cliargs['index'], doc_type='diskspace', body=data)
# add all s3 inventory files to queue
for file in inventory_files:
s3queue.put((file, cliargs))
# set up progress bar
bar = progress_bar('Importing')
bar.start()
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
i = 1
while s3queue.qsize() > 0:
try:
percent = int("{0:.0f}".format(
100 * ((len(inventory_files) - s3queue.qsize()) /
float(len(inventory_files)))))
bar.update(percent)
except ZeroDivisionError:
bar.update(0)
except ValueError:
bar.update(0)
time.sleep(.5)
i += 1
bar.finish()
# wait for queue to be empty
s3queue.join()
def dupes_finder(es, q, cliargs, logger):
"""This is the duplicate file finder function.
It searches Elasticsearch for files that have the same filehashes
and adds file hash groups to Queue.
"""
logger.info('Searching %s for all dupe filehashes...', cliargs['index'])
# first get all the filehashes with files that have a hardlinks count of 1
if cliargs['inchardlinks']:
data = {
"size":
0,
"_source": [
'filename', 'filehash', 'path_parent', 'last_modified',
'last_access'
],
"query": {
"bool": {
"must": {
"range": {
"filesize": {
"lte": config['dupes_maxsize'],
"gte": cliargs['minsize']
}
}
}
}
}
}
else:
data = {
"size":
0,
"_source": [
'filename', 'filehash', 'path_parent', 'last_modified',
'last_access'
],
"query": {
"bool": {
"must": {
"term": {
"hardlinks": 1
}
},
"filter": {
"range": {
"filesize": {
"lte": config['dupes_maxsize'],
"gte": cliargs['minsize']
}
}
}
}
}
}
# refresh index
es.indices.refresh(index=cliargs['index'])
# search es and start scroll
res = es.search(index=cliargs['index'],
doc_type='file',
scroll='1m',
size=config['es_scrollsize'],
body=data,
request_timeout=config['es_timeout'])
filehashes = {}
while res['hits']['hits'] and len(res['hits']['hits']) > 0:
for hit in res['hits']['hits']:
filehash = hit['_source']['filehash']
filepath = os.path.join(hit['_source']['path_parent'],
hit['_source']['filename'])
if filehash in filehashes:
filehashes[filehash].append({
'id':
hit['_id'],
'filename':
filepath,
'atime':
hit['_source']['last_access'],
'mtime':
hit['_source']['last_modified'],
'md5':
''
})
else:
filehashes[filehash] = [{
'id':
hit['_id'],
'filename':
filepath,
'atime':
hit['_source']['last_access'],
'mtime':
hit['_source']['last_modified'],
'md5':
''
}]
# use es scroll api
res = es.scroll(scroll_id=res['_scroll_id'],
scroll='1m',
request_timeout=config['es_timeout'])
possibledupescount = 0
for key, value in list(filehashes.items()):
filehash_filecount = len(value)
if filehash_filecount < 2:
del filehashes[key]
else:
possibledupescount += filehash_filecount
logger.info('Found %s possible dupe files', possibledupescount)
if possibledupescount == 0:
return
logger.info('Starting to enqueue dupe file hashes...')
if cliargs['adaptivebatch']:
batchsize = ab_start
else:
batchsize = cliargs['batchsize']
if cliargs['verbose'] or cliargs['debug']:
logger.info('Batch size: %s' % batchsize)
n = 0
hashgroups = []
for key, value in filehashes.items():
if cliargs['verbose'] or cliargs['debug']:
logger.info('filehash: %s, filecount: %s' % (key, len(value)))
hashgroups.append({'filehash': key, 'files': value})
n += 1
if n >= batchsize:
# send to rq for bots to process hashgroups list
q.enqueue(dupes_process_hashkeys,
args=(
hashgroups,
cliargs,
),
result_ttl=config['redis_ttl'])
if cliargs['debug'] or cliargs['verbose']:
logger.info("enqueued batchsize: %s (batchsize: %s)" %
(n, batchsize))
del hashgroups[:]
n = 0
if cliargs['adaptivebatch']:
batchsize = adaptive_batch(q, cliargs, batchsize)
if cliargs['debug'] or cliargs['verbose']:
logger.info("batchsize set to: %s" % batchsize)
# enqueue dir calc job for any remaining in dirlist
if n > 0:
q.enqueue(dupes_process_hashkeys,
args=(
hashgroups,
cliargs,
),
result_ttl=config['redis_ttl'])
logger.info(
'%s possible dupe file hashes have been enqueued, worker bots processing dupes...'
% possibledupescount)
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
bar = progress_bar('Checking')
bar.start()
else:
bar = None
# update progress bar until bots are idle and queue is empty
while worker_bots_busy([q]):
if bar:
q_len = len(q)
try:
bar.update(q_len)
except (ZeroDivisionError, ValueError):
bar.update(0)
time.sleep(1)
if bar:
bar.finish()
def start_importing(es, cliargs, logger):
"""Start importing s3 inventory file function.
"""
for i in range(4):
thread = Thread(target=csv_file_reader, args=(s3queue, ))
thread.daemon = True
thread.start()
# start importing S3 inventory file(s)
inventory_files = cliargs['s3']
logger.info('Importing %s S3 inventory file(s)...' % len(inventory_files))
# add fake disk space to index with path set to /s3
data = {
"path": '/s3',
"total": 0,
"used": 0,
"free": 0,
"available": 0,
"indexing_date": datetime.utcnow().isoformat()
}
es.index(index=cliargs['index'], doc_type='diskspace', body=data)
# create fake root directory doc
time_utc_now = datetime.utcnow().isoformat()
time_utc_epoch_start = "1970-01-01T00:00:00"
root_dict = {}
root_dict['filename'] = "s3"
root_dict['path_parent'] = "/"
root_dict["filesize"] = 0
root_dict["items"] = 1 # 1 for itself
root_dict["items_files"] = 0
root_dict["items_subdirs"] = 0
root_dict["last_modified"] = time_utc_epoch_start
root_dict["tag"] = ""
root_dict["tag_custom"] = ""
root_dict["indexing_date"] = time_utc_now
root_dict["worker_name"] = "main"
root_dict["change_percent_filesize"] = ""
root_dict["change_percent_items"] = ""
root_dict["change_percent_items_files"] = ""
root_dict["change_percent_items_subdirs"] = ""
es.index(index=cliargs['index'], doc_type='directory', body=root_dict)
diskover.add_crawl_stats(es, cliargs['index'], '/s3', 0)
# add all s3 inventory files to queue
for file in inventory_files:
s3queue.put((file, cliargs))
# set up progress bar
bar = diskover.progress_bar('Importing')
bar.start()
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
i = 1
while s3queue.qsize() > 0:
try:
percent = int("{0:.0f}".format(
100 * ((len(inventory_files) - s3queue.qsize()) /
float(len(inventory_files)))))
bar.update(percent)
except ZeroDivisionError:
bar.update(0)
except ValueError:
bar.update(0)
time.sleep(.5)
i += 1
bar.finish()
# wait for queue to be empty
s3queue.join()
def dupes_finder(es, q, cliargs, logger):
"""This is the duplicate file finder function.
It searches Elasticsearch for files that have the same filehashes
and adds file hash groups to Queue.
"""
logger.info('Searching %s for duplicate file hashes...', cliargs['index'])
# find the filehashes with largest files and add filehash keys
# to hashgroups
data = {
"size": 0,
"query": {
"bool": {
"must": {
"term": {
"hardlinks": 1
}
},
"filter": {
"range": {
"filesize": {
"lte": config['dupes_maxsize'],
"gte": cliargs['minsize']
}
}
}
}
},
"aggs": {
"dupe_filehash": {
"terms": {
"field": "filehash",
"min_doc_count": 2,
"size": 10000,
"order": {
"max_file_size": "desc"
}
},
"aggs": {
"max_file_size": {
"max": {
"field": "filesize"
}
}
}
}
}
}
# refresh index
es.indices.refresh(index=cliargs['index'])
res = es.search(index=cliargs['index'],
doc_type='file',
body=data,
request_timeout=config['es_timeout'])
logger.info('Found %s duplicate file hashes, enqueueing...',
len(res['aggregations']['dupe_filehash']['buckets']))
# add hash keys to Queue
for bucket in res['aggregations']['dupe_filehash']['buckets']:
q.enqueue(dupes_process_hashkey,
args=(
bucket['key'],
cliargs,
),
result_ttl=config['redis_ttl'])
logger.info('All file hashes have been enqueued')
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
bar = progress_bar('Checking')
bar.start()
else:
bar = None
# wait for queue to be empty and update progress bar
time.sleep(1)
while True:
workers_busy = False
workers = SimpleWorker.all(connection=redis_conn)
for worker in workers:
if worker._state == "busy":
workers_busy = True
break
q_len = len(q)
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs[
'verbose']:
try:
bar.update(q_len)
except ZeroDivisionError:
bar.update(0)
except ValueError:
bar.update(0)
if q_len == 0 and workers_busy == False:
break
time.sleep(.5)
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
bar.finish()
def dupes_finder(es, q, cliargs, logger):
"""This is the duplicate file finder function.
It searches Elasticsearch for files that have the same filehashes
and adds file hash groups to Queue.
"""
logger.info('Searching %s for all duplicate files...', cliargs['index'])
if cliargs['adaptivebatch']:
batchsize = ab_start
else:
batchsize = cliargs['batchsize']
if cliargs['verbose'] or cliargs['debug']:
logger.info('Batch size: %s' % batchsize)
# first get all the filehashes with files that have a hardlinks count of 1
data = {
"size": 0,
"query": {
"bool": {
"must": {
"term": {"hardlinks": 1}
},
"filter": {
"range": {
"filesize": {
"lte": config['dupes_maxsize'],
"gte": cliargs['minsize']
}
}
}
}
}
}
# refresh index
es.indices.refresh(index=cliargs['index'])
# search es and start scroll
res = es.search(index=cliargs['index'], scroll='1m', doc_type='file', size=config['es_scrollsize'],
body=data, request_timeout=config['es_timeout'])
filehashlist = []
filehashcount = 0
while res['hits']['hits'] and len(res['hits']['hits']) > 0:
for hit in res['hits']['hits']:
filehash = hit['_source']['filehash']
if filehash not in filehashlist:
filehashlist.append(filehash)
filehashcount += 1
filehashlist_len = len(filehashlist)
if filehashlist_len >= batchsize:
# send to rq for bots to process file hashkey list
q.enqueue(dupes_process_hashkey, args=(filehashlist, cliargs,), result_ttl=config['redis_ttl'])
if cliargs['debug'] or cliargs['verbose']:
logger.info("enqueued batchsize: %s (batchsize: %s)" % (filehashlist_len, batchsize))
del filehashlist[:]
if cliargs['adaptivebatch']:
batchsize = adaptive_batch(q, cliargs, batchsize)
if cliargs['debug'] or cliargs['verbose']:
logger.info("batchsize set to: %s" % batchsize)
# use es scroll api
res = es.scroll(scroll_id=res['_scroll_id'], scroll='1m',
request_timeout=config['es_timeout'])
# enqueue dir calc job for any remaining in dirlist
if len(filehashlist) > 0:
q.enqueue(dupes_process_hashkey, args=(filehashlist, cliargs,), result_ttl=config['redis_ttl'])
logger.info('%s file hashes have been enqueued' % filehashcount)
if not cliargs['quiet'] and not cliargs['debug'] and not cliargs['verbose']:
bar = progress_bar('Checking')
bar.start()
else:
bar = None
# update progress bar until bots are idle and queue is empty
while worker_bots_busy([q]):
if bar:
q_len = len(q)
try:
bar.update(q_len)
except (ZeroDivisionError, ValueError):
bar.update(0)
time.sleep(1)
if bar:
bar.finish()