/
data_export.py
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/
data_export.py
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#!/usr/bin/env python
# Licensed to Cloudera, Inc. under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. Cloudera, Inc. licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from builtins import str
from builtins import object
import json
import logging
import math
import sys
import types
from desktop.lib import export_csvxls
from beeswax import common, conf
if sys.version_info[0] > 2:
from django.utils.translation import gettext as _
else:
from django.utils.translation import ugettext as _
LOG = logging.getLogger()
FETCH_SIZE = 1000
DOWNLOAD_COOKIE_AGE = 1800 # 30 minutes
def download(handle, format, db, id=None, file_name='query_result', user_agent=None):
"""
download(query_model, format) -> HttpResponse
Retrieve the query result in the format specified. Return an HttpResponse object.
"""
if format not in common.DL_FORMATS:
LOG.error('Unknown download format "%s"' % (format,))
return
max_rows = conf.DOWNLOAD_ROW_LIMIT.get()
max_bytes = conf.DOWNLOAD_BYTES_LIMIT.get()
content_generator = DataAdapter(db, handle=handle, max_rows=max_rows, max_bytes=max_bytes)
generator = export_csvxls.create_generator(content_generator, format)
resp = export_csvxls.make_response(generator, format, file_name, user_agent=user_agent)
if id:
resp.set_cookie(
'download-%s' % id,
json.dumps({
'truncated': content_generator.is_truncated,
'row_counter': content_generator.row_counter
}),
max_age=DOWNLOAD_COOKIE_AGE
)
return resp
def upload(path, handle, user, db, fs, max_rows=-1, max_bytes=-1):
"""
upload(query_model, path, user, db, fs) -> None
Retrieve the query result in the format specified and upload to hdfs.
"""
if fs.do_as_user(user.username, fs.exists, path):
raise Exception(_("%s already exists.") % path)
else:
fs.do_as_user(user.username, fs.create, path)
content_generator = DataAdapter(db, handle=handle, max_rows=max_rows, start_over=True, max_bytes=max_bytes)
for header, data in content_generator:
dataset = export_csvxls.dataset(None, data)
fs.do_as_user(user.username, fs.append, path, dataset.csv)
class DataAdapter(object):
def __init__(self, db, handle=None, max_rows=-1, start_over=True, max_bytes=-1, store_data_type_in_header=False):
self.handle = handle
self.db = db
self.max_rows = max_rows
self.max_bytes = max_bytes
self.start_over = start_over
self.fetch_size = FETCH_SIZE
self.limit_rows = max_rows > -1
self.limit_bytes = max_bytes > -1
self.first_fetched = True
self.headers = None
self.num_cols = None
self.row_counter = 0
self.bytes_counter = 0
self.is_truncated = False
self.has_more = True
self.store_data_type_in_header = store_data_type_in_header
def __iter__(self):
return self
# Return an estimate of the size of the object using only ascii characters once serialized to string.
# Avoid serialization to string where possible
def _getsizeofascii(self, row):
size = 0
size += max(len(row) - 1, 0) # CSV commas between columns
size += 2 # CSV \r\n at the end of row
for col in row:
col_type = type(col)
if col_type == int:
if col == 0:
size += 1
elif col < 0:
size += int(math.log10(-1 * col)) + 2
else:
size += int(math.log10(col)) + 1
elif col_type == bytes:
size += len(col)
elif col_type == float:
size += len(str(col))
elif col_type == bool:
size += 4
elif col_type == type(None):
size += 4
else:
size += len(str(col))
return size
def __next__(self):
results = self.db.fetch(self.handle, start_over=self.start_over, rows=self.fetch_size)
if self.first_fetched:
self.first_fetched = False
self.start_over = False
results_headers = results.full_cols()
self.num_cols = len(results_headers)
if self.store_data_type_in_header:
self.headers = [column['name'] + '|' + column['type'] for column in results_headers]
else:
self.headers = [column['name'] for column in results_headers]
if self.limit_bytes:
self.bytes_counter += max(self.num_cols - 1, 0)
for header in self.headers:
self.bytes_counter += len(header)
# For result sets with high num of columns, fetch in smaller batches to avoid serialization cost
if self.num_cols > 100:
LOG.warning('The query results contain %d columns and may take long time to download, reducing fetch size to 100.' % self.num_cols)
self.fetch_size = 100
if self.has_more and not self.is_truncated:
self.has_more = results.has_more
data = []
for row in results.rows():
num_bytes = self._getsizeofascii(row)
if self.limit_rows and self.row_counter + 1 > self.max_rows:
LOG.warning('The query results exceeded the maximum row limit of %d and has been truncated to first %d rows.' % (
self.max_rows, self.row_counter)
)
self.is_truncated = True
break
if self.limit_bytes and self.bytes_counter + num_bytes > self.max_bytes:
LOG.warning('The query results exceeded the maximum bytes limit of %d and has been truncated to first %d rows.' % (
self.max_bytes, self.row_counter)
)
self.is_truncated = True
break
self.row_counter += 1
self.bytes_counter += num_bytes
data.append(row)
return self.headers, data
else:
self.db.close(self.handle)
raise StopIteration