def test_guess_format_invalid_csv_format(self):
indexer = MorphlineIndexer("test", solr_client=self.solr_client)
stream = StringIO.StringIO(TestIndexer.simpleCSVString)
guessed_format = indexer.guess_format({'file': {"stream": stream, "name": "test.csv"}})
guessed_format["fieldSeparator"] = "invalid separator"
fields = indexer.guess_field_types({"file": {"stream": stream, "name": "test.csv"}, "format": guessed_format})['columns']
assert_equal(fields, [])
stream.seek(0)
guessed_format = indexer.guess_format({'file': {"stream": stream, "name": "test.csv"}})
guessed_format["recordSeparator"] = "invalid separator"
fields = indexer.guess_field_types({"file": {"stream": stream, "name": "test.csv"}, "format": guessed_format})['columns']
assert_equal(fields, [])
stream.seek(0)
guessed_format = indexer.guess_format({'file': {"stream": stream, "name": "test.csv"}})
guessed_format["quoteChar"] = "invalid quoteChar"
fields = indexer.guess_field_types({"file": {"stream": stream, "name": "test.csv"}, "format": guessed_format})['columns']
assert_equal(fields, [])
def test_guess_csv_format(self):
stream = StringIO.StringIO(TestIndexer.simpleCSVString)
indexer = MorphlineIndexer("test", solr_client=self.solr_client)
guessed_format = indexer.guess_format(
{'file': {
"stream": stream,
"name": "test.csv"
}})
fields = indexer.guess_field_types({
"file": {
"stream": stream,
"name": "test.csv"
},
"format": guessed_format
})['columns']
# test format
expected_format = self.simpleCSVFormat
assert_equal(expected_format, guessed_format)
# test fields
expected_fields = self.simpleCSVFields
for expected, actual in zip(expected_fields, fields):
for key in ("name", "type"):
assert_equal(expected[key], actual[key])
def guess_format(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'file':
indexer = MorphlineIndexer(request.user, request.fs)
if not request.fs.isfile(file_format["path"]):
raise PopupException(_('Path %(path)s is not a file') % file_format)
stream = request.fs.open(file_format["path"])
format_ = indexer.guess_format({
"file": {
"stream": stream,
"name": file_format['path']
}
})
_convert_format(format_)
elif file_format['inputFormat'] == 'table':
db = dbms.get(request.user)
table_metadata = db.get_table(database=file_format['databaseName'], table_name=file_format['tableName'])
storage = dict([(delim['data_type'], delim['comment']) for delim in table_metadata.storage_details])
if table_metadata.details['properties']['format'] == 'text':
format_ = {"quoteChar": "\"", "recordSeparator": '\\n', "type": "csv", "hasHeader": False, "fieldSeparator": storage['serialization.format']}
elif table_metadata.details['properties']['format'] == 'parquet':
format_ = {"type": "parquet", "hasHeader": False,}
else:
raise PopupException('Hive table format %s is not supported.' % table_metadata.details['properties']['format'])
elif file_format['inputFormat'] == 'query':
format_ = {"quoteChar": "\"", "recordSeparator": "\\n", "type": "csv", "hasHeader": False, "fieldSeparator": "\u0001"}
elif file_format['inputFormat'] == 'rdbms':
format_ = RdbmsIndexer(request.user, file_format['rdbmsType']).guess_format()
format_['status'] = 0
return JsonResponse(format_)
def guess_format(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'file':
path = urllib.unquote(file_format["path"])
indexer = MorphlineIndexer(request.user, request.fs)
if not request.fs.isfile(path):
raise PopupException(
_('Path %(path)s is not a file') % file_format)
stream = request.fs.open(path)
format_ = indexer.guess_format(
{"file": {
"stream": stream,
"name": path
}})
_convert_format(format_)
elif file_format['inputFormat'] == 'table':
db = dbms.get(request.user)
try:
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
except Exception, e:
raise PopupException(
e.message if hasattr(e, 'message') and e.message else e)
storage = {}
for delim in table_metadata.storage_details:
if delim['data_type']:
if '=' in delim['data_type']:
key, val = delim['data_type'].split('=', 1)
storage[key] = val
else:
storage[delim['data_type']] = delim['comment']
if table_metadata.details['properties']['format'] == 'text':
format_ = {
"quoteChar": "\"",
"recordSeparator": '\\n',
"type": "csv",
"hasHeader": False,
"fieldSeparator": storage.get('field.delim', ',')
}
elif table_metadata.details['properties']['format'] == 'parquet':
format_ = {
"type": "parquet",
"hasHeader": False,
}
else:
raise PopupException(
'Hive table format %s is not supported.' %
table_metadata.details['properties']['format'])
def test_end_to_end(self):
if not is_live_cluster(): # Skipping as requires morplines libs to be setup
raise SkipTest()
cluster = shared_cluster()
fs = cluster.fs
make_logged_in_client(username="******", groupname="default", recreate=True, is_superuser=False)
user = User.objects.get(username="******")
collection_name = "test_collection"
indexer = MorphlineIndexer("test", fs=fs, jt=cluster.jt, solr_client=self.solr_client)
input_loc = "/tmp/test.csv"
# upload the test file to hdfs
fs.create(input_loc, data=TestIndexer.simpleCSVString, overwrite=True)
# open a filestream for the file on hdfs
stream = fs.open(input_loc)
# guess the format of the file
file_type_format = indexer.guess_format({'file': {"stream": stream, "name": "test.csv"}})
field_types = indexer.guess_field_types({"file":{"stream": stream, "name": "test.csv"}, "format": file_type_format})
format_ = field_types.copy()
format_['format'] = file_type_format
# find a field name available to use for the record's uuid
unique_field = indexer.get_unique_field(format_)
is_unique_generated = indexer.is_unique_generated(format_)
# generate morphline
morphline = indexer.generate_morphline_config(collection_name, format_, unique_field)
schema_fields = indexer.get_kept_field_list(format_['columns'])
if is_unique_generated:
schema_fields += [{"name": unique_field, "type": "string"}]
# create the collection from the specified fields
collection_manager = CollectionManagerController("test")
if collection_manager.collection_exists(collection_name):
collection_manager.delete_collection(collection_name, None)
collection_manager.create_collection(collection_name, schema_fields, unique_key_field=unique_field)
# index the file
indexer.run_morphline(MockedRequest(user=user, fs=cluster.fs, jt=cluster.jt), collection_name, morphline, input_loc)
def guess_format(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
if file_format['inputFormat'] == 'file':
path = urllib_unquote(file_format["path"])
indexer = MorphlineIndexer(request.user, request.fs)
if not request.fs.isfile(path):
raise PopupException(
_('Path %(path)s is not a file') % file_format)
stream = request.fs.open(path)
format_ = indexer.guess_format(
{"file": {
"stream": stream,
"name": path
}})
_convert_format(format_)
elif file_format['inputFormat'] == 'table':
db = dbms.get(request.user)
try:
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
except Exception as e:
raise PopupException(
e.message if hasattr(e, 'message') and e.message else e)
storage = {}
for delim in table_metadata.storage_details:
if delim['data_type']:
if '=' in delim['data_type']:
key, val = delim['data_type'].split('=', 1)
storage[key] = val
else:
storage[delim['data_type']] = delim['comment']
if table_metadata.details['properties']['format'] == 'text':
format_ = {
"quoteChar": "\"",
"recordSeparator": '\\n',
"type": "csv",
"hasHeader": False,
"fieldSeparator": storage.get('field.delim', ',')
}
elif table_metadata.details['properties']['format'] == 'parquet':
format_ = {
"type": "parquet",
"hasHeader": False,
}
else:
raise PopupException(
'Hive table format %s is not supported.' %
table_metadata.details['properties']['format'])
elif file_format['inputFormat'] == 'query':
format_ = {
"quoteChar": "\"",
"recordSeparator": "\\n",
"type": "csv",
"hasHeader": False,
"fieldSeparator": "\u0001"
}
elif file_format['inputFormat'] == 'rdbms':
format_ = {"type": "csv"}
elif file_format['inputFormat'] == 'stream':
if file_format['streamSelection'] == 'kafka':
format_ = {
"type": "json",
# "fieldSeparator": ",",
# "hasHeader": True,
# "quoteChar": "\"",
# "recordSeparator": "\\n",
'topics': get_topics(request.user)
}
elif file_format['streamSelection'] == 'flume':
format_ = {
"type": "csv",
"fieldSeparator": ",",
"hasHeader": True,
"quoteChar": "\"",
"recordSeparator": "\\n"
}
elif file_format['inputFormat'] == 'connector':
if file_format['connectorSelection'] == 'sfdc':
sf = Salesforce(username=file_format['streamUsername'],
password=file_format['streamPassword'],
security_token=file_format['streamToken'])
format_ = {
"type":
"csv",
"fieldSeparator":
",",
"hasHeader":
True,
"quoteChar":
"\"",
"recordSeparator":
"\\n",
'objects': [
sobject['name']
for sobject in sf.restful('sobjects/')['sobjects']
if sobject['queryable']
]
}
else:
raise PopupException(
_('Input format %(inputFormat)s connector not recognized: $(connectorSelection)s'
) % file_format)
else:
raise PopupException(
_('Input format not recognized: %(inputFormat)s') % file_format)
format_['status'] = 0
return JsonResponse(format_)
def guess_format(request):
file_format = json.loads(request.POST.get('fileFormat', '{}'))
file_type = file_format['file_type']
path = urllib_unquote(file_format["path"])
if sys.version_info[0] < 3 and (file_type == 'excel' or path[-3:] == 'xls'
or path[-4:] == 'xlsx'):
return JsonResponse({
'status':
-1,
'message':
'Python2 based Hue does not support Excel file importer'
})
if file_format['inputFormat'] == 'localfile':
if file_type == 'excel':
format_ = {"type": "excel", "hasHeader": True}
else:
format_ = {
"quoteChar": "\"",
"recordSeparator": '\\n',
"type": "csv",
"hasHeader": True,
"fieldSeparator": ","
}
elif file_format['inputFormat'] == 'file':
if path[-3:] == 'xls' or path[-4:] == 'xlsx':
file_obj = request.fs.open(path)
if path[-3:] == 'xls':
df = pd.read_excel(file_obj.read(1024 * 1024 * 1024),
engine='xlrd')
else:
df = pd.read_excel(file_obj.read(1024 * 1024 * 1024),
engine='openpyxl')
_csv_data = df.to_csv(index=False)
path = excel_to_csv_file_name_change(path)
request.fs.create(path, overwrite=True, data=_csv_data)
indexer = MorphlineIndexer(request.user, request.fs)
if not request.fs.isfile(path):
raise PopupException(
_('Path %(path)s is not a file') % file_format)
stream = request.fs.open(path)
format_ = indexer.guess_format(
{"file": {
"stream": stream,
"name": path
}})
_convert_format(format_)
if file_format["path"][-3:] == 'xls' or file_format["path"][
-4:] == 'xlsx':
format_ = {
"quoteChar": "\"",
"recordSeparator": '\\n',
"type": "excel",
"hasHeader": True,
"fieldSeparator": ","
}
elif file_format['inputFormat'] == 'table':
db = dbms.get(request.user)
try:
table_metadata = db.get_table(database=file_format['databaseName'],
table_name=file_format['tableName'])
except Exception as e:
raise PopupException(
e.message if hasattr(e, 'message') and e.message else e)
storage = {}
for delim in table_metadata.storage_details:
if delim['data_type']:
if '=' in delim['data_type']:
key, val = delim['data_type'].split('=', 1)
storage[key] = val
else:
storage[delim['data_type']] = delim['comment']
if table_metadata.details['properties']['format'] == 'text':
format_ = {
"quoteChar": "\"",
"recordSeparator": '\\n',
"type": "csv",
"hasHeader": False,
"fieldSeparator": storage.get('field.delim', ',')
}
elif table_metadata.details['properties']['format'] == 'parquet':
format_ = {
"type": "parquet",
"hasHeader": False,
}
else:
raise PopupException(
'Hive table format %s is not supported.' %
table_metadata.details['properties']['format'])
elif file_format['inputFormat'] == 'query':
format_ = {
"quoteChar": "\"",
"recordSeparator": "\\n",
"type": "csv",
"hasHeader": False,
"fieldSeparator": "\u0001"
}
elif file_format['inputFormat'] == 'rdbms':
format_ = {"type": "csv"}
elif file_format['inputFormat'] == 'stream':
if file_format['streamSelection'] == 'kafka':
format_ = {
"type": "json",
# "fieldSeparator": ",",
# "hasHeader": True,
# "quoteChar": "\"",
# "recordSeparator": "\\n",
'topics': get_topics(request.user)
}
elif file_format['streamSelection'] == 'flume':
format_ = {
"type": "csv",
"fieldSeparator": ",",
"hasHeader": True,
"quoteChar": "\"",
"recordSeparator": "\\n"
}
elif file_format['inputFormat'] == 'connector':
if file_format['connectorSelection'] == 'sfdc':
sf = Salesforce(username=file_format['streamUsername'],
password=file_format['streamPassword'],
security_token=file_format['streamToken'])
format_ = {
"type":
"csv",
"fieldSeparator":
",",
"hasHeader":
True,
"quoteChar":
"\"",
"recordSeparator":
"\\n",
'objects': [
sobject['name']
for sobject in sf.restful('sobjects/')['sobjects']
if sobject['queryable']
]
}
else:
raise PopupException(
_('Input format %(inputFormat)s connector not recognized: $(connectorSelection)s'
) % file_format)
else:
raise PopupException(
_('Input format not recognized: %(inputFormat)s') % file_format)
format_['status'] = 0
return JsonResponse(format_)