def _create_index(user, fs, client, source, destination, index_name):
unique_key_field = destination['indexerPrimaryKey'] and destination[
'indexerPrimaryKey'][0] or None
df = destination['indexerDefaultField'] and destination[
'indexerDefaultField'][0] or None
kwargs = {}
if source['inputFormat'] not in ('manual', 'table'):
stats = fs.stats(source["path"])
if stats.size > MAX_UPLOAD_SIZE:
raise PopupException(_('File size is too large to handle!'))
indexer = MorphlineIndexer(user, fs)
fields = indexer.get_field_list(destination['columns'])
skip_fields = [field['name'] for field in fields if not field['keep']]
kwargs['fieldnames'] = ','.join([field['name'] for field in fields])
if skip_fields:
kwargs['skip'] = ','.join(skip_fields)
fields = [
field for field in fields if field['name'] not in skip_fields
]
if not unique_key_field:
unique_key_field = 'hue_id'
fields += [{"name": unique_key_field, "type": "string"}]
kwargs['rowid'] = unique_key_field
if not destination['hasHeader']:
kwargs['header'] = 'false'
else:
kwargs['skipLines'] = 1
if not client.exists(index_name):
client.create_index(
name=index_name,
config_name=destination.get('indexerConfigSet'),
fields=fields,
unique_key_field=unique_key_field,
df=df,
shards=destination['indexerNumShards'],
replication=destination['indexerReplicationFactor'])
if source['inputFormat'] not in ('manual', 'table'):
data = fs.read(source["path"], 0, MAX_UPLOAD_SIZE)
try:
client.index(name=index_name, data=data, **kwargs)
except Exception, e:
try:
client.delete_index(index_name, keep_config=False)
except Exception, e2:
LOG.warn(
'Error while cleaning-up config of failed collection creation %s: %s'
% (index_name, e2))
raise e
def _create_index(user, fs, client, source, destination, index_name):
unique_key_field = destination['indexerDefaultField'] and destination[
'indexerDefaultField'][0] or None
df = destination['indexerPrimaryKey'] and destination['indexerPrimaryKey'][
0] or None
kwargs = {}
if source['inputFormat'] != 'manual':
stats = fs.stats(source["path"])
if stats.size > MAX_UPLOAD_SIZE:
raise PopupException(_('File size is too large to handle!'))
indexer = MorphlineIndexer(user, fs)
fields = indexer.get_field_list(destination['columns'])
skip_fields = [field['name'] for field in fields if not field['keep']]
kwargs['fieldnames'] = ','.join([field['name'] for field in fields])
if skip_fields:
kwargs['skip'] = ','.join(skip_fields)
fields = [
field for field in fields if field['name'] not in skip_fields
]
if not unique_key_field:
unique_key_field = 'hue_id'
fields += [{"name": unique_key_field, "type": "string"}]
kwargs['rowid'] = unique_key_field
if not destination['hasHeader']:
kwargs['header'] = 'false'
else:
kwargs['skipLines'] = 1
if not client.exists(index_name):
client.create_index(
name=index_name,
fields=fields,
unique_key_field=unique_key_field,
df=df,
shards=destination['indexerNumShards'],
replication=destination['indexerReplicationFactor'])
if source['inputFormat'] != 'manual':
data = fs.read(source["path"], 0, MAX_UPLOAD_SIZE)
client.index(name=index_name, data=data, **kwargs)
return {
'status': 0,
'on_success_url': reverse('indexer:indexes',
kwargs={'index': index_name}),
'pub_sub_url': 'assist.collections.refresh'
}
def _small_indexing(user, fs, client, source, destination, index_name):
kwargs = {}
errors = []
if source['inputFormat'] not in ('manual', 'table', 'query_handle'):
path = urllib.unquote(source["path"])
stats = fs.stats(path)
if stats.size > MAX_UPLOAD_SIZE:
raise PopupException(_('File size is too large to handle!'))
indexer = MorphlineIndexer(user, fs)
fields = indexer.get_field_list(destination['columns'])
_create_solr_collection(user, fs, client, destination, index_name, kwargs)
if source['inputFormat'] == 'file':
path = urllib.unquote(source["path"])
data = fs.read(path, 0, MAX_UPLOAD_SIZE)
if client.is_solr_six_or_more():
kwargs['processor'] = 'tolerant'
kwargs['map'] = 'NULL:'
try:
if source['inputFormat'] == 'query':
query_id = source['query']['id'] if source['query'].get('id') else source['query']
notebook = Notebook(document=Document2.objects.document(user=user, doc_id=query_id)).get_data()
request = MockedDjangoRequest(user=user)
snippet = notebook['snippets'][0]
searcher = CollectionManagerController(user)
columns = [field['name'] for field in fields if field['name'] != 'hue_id']
fetch_handle = lambda rows, start_over: get_api(request, snippet).fetch_result(notebook, snippet, rows=rows, start_over=start_over) # Assumes handle still live
rows = searcher.update_data_from_hive(index_name, columns, fetch_handle=fetch_handle, indexing_options=kwargs)
# TODO if rows == MAX_ROWS truncation warning
elif source['inputFormat'] == 'manual':
pass # No need to do anything
else:
response = client.index(name=index_name, data=data, **kwargs)
errors = [error.get('message', '') for error in response['responseHeader'].get('errors', [])]
except Exception, e:
try:
client.delete_index(index_name, keep_config=False)
except Exception, e2:
LOG.warn('Error while cleaning-up config of failed collection creation %s: %s' % (index_name, e2))
def _create_solr_collection(user, fs, client, destination, index_name, kwargs):
unique_key_field = destination['indexerPrimaryKey'] and destination[
'indexerPrimaryKey'][0] or None
df = destination['indexerDefaultField'] and destination[
'indexerDefaultField'][0] or None
indexer = MorphlineIndexer(user, fs)
fields = indexer.get_field_list(destination['columns'])
skip_fields = [field['name'] for field in fields if not field['keep']]
kwargs['fieldnames'] = ','.join([field['name'] for field in fields])
for field in fields:
for operation in field['operations']:
if operation['type'] == 'split':
field[
'multiValued'] = True # Solr requires multiValued to be set when splitting
kwargs['f.%(name)s.split' % field] = 'true'
kwargs['f.%(name)s.separator' %
field] = operation['settings']['splitChar'] or ','
if skip_fields:
kwargs['skip'] = ','.join(skip_fields)
fields = [
field for field in fields if field['name'] not in skip_fields
]
if not unique_key_field:
unique_key_field = 'hue_id'
fields += [{"name": unique_key_field, "type": "string"}]
kwargs['rowid'] = unique_key_field
if not destination['hasHeader']:
kwargs['header'] = 'false'
else:
kwargs['skipLines'] = 1
if not client.exists(index_name):
client.create_index(
name=index_name,
config_name=destination.get('indexerConfigSet'),
fields=fields,
unique_key_field=unique_key_field,
df=df,
shards=destination['indexerNumShards'],
replication=destination['indexerReplicationFactor'])
def _small_indexing(user, fs, client, source, destination, index_name):
unique_key_field = destination['indexerPrimaryKey'] and destination[
'indexerPrimaryKey'][0] or None
df = destination['indexerDefaultField'] and destination[
'indexerDefaultField'][0] or None
kwargs = {}
errors = []
if source['inputFormat'] not in ('manual', 'table', 'query_handle'):
stats = fs.stats(source["path"])
if stats.size > MAX_UPLOAD_SIZE:
raise PopupException(_('File size is too large to handle!'))
indexer = MorphlineIndexer(user, fs)
fields = indexer.get_field_list(destination['columns'])
skip_fields = [field['name'] for field in fields if not field['keep']]
kwargs['fieldnames'] = ','.join([field['name'] for field in fields])
if skip_fields:
kwargs['skip'] = ','.join(skip_fields)
fields = [
field for field in fields if field['name'] not in skip_fields
]
if not unique_key_field:
unique_key_field = 'hue_id'
fields += [{"name": unique_key_field, "type": "string"}]
kwargs['rowid'] = unique_key_field
if not destination['hasHeader']:
kwargs['header'] = 'false'
else:
kwargs['skipLines'] = 1
if not client.exists(index_name):
client.create_index(
name=index_name,
config_name=destination.get('indexerConfigSet'),
fields=fields,
unique_key_field=unique_key_field,
df=df,
shards=destination['indexerNumShards'],
replication=destination['indexerReplicationFactor'])
if source['inputFormat'] == 'file':
data = fs.read(source["path"], 0, MAX_UPLOAD_SIZE)
if client.is_solr_six_or_more():
kwargs['processor'] = 'tolerant'
try:
if source['inputFormat'] == 'query':
query_id = source['query']['id'] if source['query'].get(
'id') else source['query']
notebook = Notebook(document=Document2.objects.document(
user=user, doc_id=query_id)).get_data()
request = MockedDjangoRequest(user=user)
snippet = notebook['snippets'][0]
searcher = CollectionManagerController(user)
columns = [
field['name'] for field in fields if field['name'] != 'hue_id'
]
fetch_handle = lambda rows, start_over: get_api(
request, snippet).fetch_result(
notebook, snippet, rows=rows, start_over=start_over
) # Assumes handle still live
rows = searcher.update_data_from_hive(index_name,
columns,
fetch_handle=fetch_handle,
indexing_options=kwargs)
# TODO if rows == MAX_ROWS truncation warning
else:
response = client.index(name=index_name, data=data, **kwargs)
errors = [
error.get('message', '')
for error in response['responseHeader'].get('errors', [])
]
except Exception, e:
try:
client.delete_index(index_name, keep_config=False)
except Exception, e2:
LOG.warn(
'Error while cleaning-up config of failed collection creation %s: %s'
% (index_name, e2))
