481 lines
19 KiB
Python
481 lines
19 KiB
Python
# Copyright (c) 2010-2011 OpenStack, LLC.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
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# implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from ConfigParser import ConfigParser
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import zlib
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import time
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import datetime
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import cStringIO
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import collections
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from paste.deploy import appconfig
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import multiprocessing
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import Queue
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import cPickle
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import hashlib
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import json
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import io
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from slogging.internal_proxy import InternalProxy
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from swift.common.utils import get_logger, readconf
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from swift.common.daemon import Daemon
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from slogging.log_common import LogProcessorCommon, multiprocess_collate, \
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BadFileDownload
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from slogging import common
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now = datetime.datetime.now
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class LogProcessor(LogProcessorCommon):
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"""Load plugins, process logs"""
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def __init__(self, conf, logger):
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basic_conf = conf['log-processor']
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super(LogProcessor, self).__init__(basic_conf, logger, 'log-processor')
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# load the processing plugins
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self.plugins = {}
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plugin_prefix = 'log-processor-'
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for section in (x for x in conf if x.startswith(plugin_prefix)):
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plugin_name = section[len(plugin_prefix):]
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plugin_conf = conf.get(section, {})
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self.plugins[plugin_name] = plugin_conf
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class_path = self.plugins[plugin_name]['class_path']
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import_target, class_name = class_path.rsplit('.', 1)
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module = __import__(import_target, fromlist=[import_target])
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klass = getattr(module, class_name)
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self.plugins[plugin_name]['instance'] = klass(plugin_conf)
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self.plugins[plugin_name]['keylist_mapping'] = {}
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self.logger.debug(_('Loaded plugin "%s"') % plugin_name)
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def process_one_file(self, plugin_name, account, container, object_name):
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self.logger.info(_('Processing %(obj)s with plugin "%(plugin)s"') %
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{'obj': '/'.join((account, container, object_name)),
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'plugin': plugin_name})
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# get an iter of the object data
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compressed = object_name.endswith('.gz')
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stream = self.get_object_data(account, container, object_name,
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compressed=compressed)
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# look up the correct plugin and send the stream to it
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return self.plugins[plugin_name]['instance'].process(stream,
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account,
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container,
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object_name)
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def get_data_list(self, start_date=None, end_date=None,
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listing_filter=None):
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total_list = []
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for plugin_name, data in self.plugins.items():
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account = data['swift_account']
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container = data['container_name']
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listing = self.get_container_listing(account,
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container,
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start_date,
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end_date)
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for object_name in listing:
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# The items in this list end up being passed as positional
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# parameters to process_one_file.
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x = (plugin_name, account, container, object_name)
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if x not in listing_filter:
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total_list.append(x)
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return total_list
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def generate_keylist_mapping(self):
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keylist = {}
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for plugin in self.plugins:
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plugin_keylist = self.plugins[plugin]['keylist_mapping'] = \
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self.plugins[plugin]['instance'].keylist_mapping()
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if not plugin_keylist:
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continue
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for k, v in plugin_keylist.items():
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o = keylist.get(k)
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if o:
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if isinstance(o, set):
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if isinstance(v, set):
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o.update(v)
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else:
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o.update([v])
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else:
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o = set(o)
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if isinstance(v, set):
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o.update(v)
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else:
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o.update([v])
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else:
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o = v
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keylist[k] = o
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return keylist
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class LogProcessorDaemon(Daemon):
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"""
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Gather raw log data and farm proccessing to generate a csv that is
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uploaded to swift.
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"""
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def __init__(self, conf):
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c = conf.get('log-processor')
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super(LogProcessorDaemon, self).__init__(c)
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self.total_conf = conf
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self.logger = get_logger(c, log_route='log-processor')
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self.log_processor = LogProcessor(conf, self.logger)
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self.lookback_hours = int(c.get('lookback_hours', '120'))
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self.lookback_window = int(c.get('lookback_window',
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str(self.lookback_hours)))
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self.log_processor_account = c['swift_account']
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self.log_processor_container = c.get('container_name',
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'log_processing_data')
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self.worker_count = int(c.get('worker_count', '1'))
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self._keylist_mapping = None
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self.processed_files_filename = 'processed_files.pickle.gz'
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self.format_type = common.get_format_type(c, self.logger,
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'format_type', 'csv')
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def get_lookback_interval(self):
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"""
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:returns: lookback_start, lookback_end.
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Both or just lookback_end can be None. Otherwise, returns strings
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of the form 'YYYYMMDDHH'. The interval returned is used as bounds
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when looking for logs to processes.
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A returned None means don't limit the log files examined on that
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side of the interval.
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"""
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if self.lookback_hours == 0:
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lookback_start = None
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lookback_end = None
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else:
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delta_hours = datetime.timedelta(hours=self.lookback_hours)
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lookback_start = now() - delta_hours
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lookback_start = lookback_start.strftime('%Y%m%d%H')
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if self.lookback_window == 0:
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lookback_end = None
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else:
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delta_window = datetime.timedelta(hours=self.lookback_window)
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lookback_end = now() - \
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delta_hours + \
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delta_window
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lookback_end = lookback_end.strftime('%Y%m%d%H')
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return lookback_start, lookback_end
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def get_processed_files_list(self):
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"""
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:returns: a set of files that have already been processed or returns
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None on error.
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Downloads the set from the stats account. Creates an empty set if
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the an existing file cannot be found.
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"""
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try:
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# Note: this file (or data set) will grow without bound.
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# In practice, if it becomes a problem (say, after many months of
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# running), one could manually prune the file to remove older
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# entries. Automatically pruning on each run could be dangerous.
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# There is not a good way to determine when an old entry should be
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# pruned (lookback_hours could be set to anything and could change)
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stream = self.log_processor.get_object_data(
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self.log_processor_account,
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self.log_processor_container,
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self.processed_files_filename,
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compressed=True)
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buf = '\n'.join(x for x in stream)
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if buf:
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files = cPickle.loads(buf)
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else:
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return None
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except BadFileDownload, err:
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if err.status_code == 404:
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files = set()
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else:
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return None
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return files
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def get_aggregate_data(self, processed_files, input_data):
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"""
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Aggregates stats data by account/hour, summing as needed.
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:param processed_files: set of processed files
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:param input_data: is the output from multiprocess_collate/the plugins.
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:returns: A dict containing data aggregated from the input_data
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passed in.
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The dict returned has tuple keys of the form:
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(account, year, month, day, hour)
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The dict returned has values that are dicts with items of this
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form:
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key:field_value
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- key corresponds to something in one of the plugin's keylist
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mapping, something like the tuple (source, level, verb, code)
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- field_value is the sum of the field_values for the
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corresponding values in the input
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Both input_data and the dict returned are hourly aggregations of
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stats.
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Multiple values for the same (account, hour, tuple key) found in
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input_data are summed in the dict returned.
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"""
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aggr_data = {}
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for item, data in input_data:
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# since item contains the plugin and the log name, new plugins will
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# "reprocess" the file and the results will be in the final csv.
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processed_files.add(item)
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for k, d in data.items():
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existing_data = aggr_data.get(k, {})
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for i, j in d.items():
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current = existing_data.get(i, 0)
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# merging strategy for key collisions is addition
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# processing plugins need to realize this
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existing_data[i] = current + j
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aggr_data[k] = existing_data
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return aggr_data
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def get_final_info(self, aggr_data):
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"""
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Aggregates data from aggr_data based on the keylist mapping.
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:param aggr_data: The results of the get_aggregate_data function.
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:returns: a dict of further aggregated data
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The dict returned has keys of the form:
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(account, year, month, day, hour)
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The dict returned has values that are dicts with items of this
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form:
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'field_name': field_value (int)
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Data is aggregated as specified by the keylist mapping. The
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keylist mapping specifies which keys to combine in aggr_data
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and the final field_names for these combined keys in the dict
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returned. Fields combined are summed.
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"""
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final_info = collections.defaultdict(dict)
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for account, data in aggr_data.items():
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for key, mapping in self.keylist_mapping.items():
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if isinstance(mapping, (list, set)):
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value = 0
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for k in mapping:
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try:
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value += data[k]
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except KeyError:
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pass
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else:
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try:
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value = data[mapping]
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except KeyError:
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value = 0
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final_info[account][key] = value
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return final_info
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def store_processed_files_list(self, processed_files):
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"""
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Stores the proccessed files list in the stats account.
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:param processed_files: set of processed files
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"""
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s = cPickle.dumps(processed_files, cPickle.HIGHEST_PROTOCOL)
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f = cStringIO.StringIO(s)
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self.log_processor.internal_proxy.upload_file(f,
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self.log_processor_account,
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self.log_processor_container,
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self.processed_files_filename)
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def get_output(self, final_info):
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"""
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:returns: a list of rows to appear in the csv file or
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a dictionary to appear in the json file.
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csv file:
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The first row contains the column headers for the rest
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of the rows in the returned list.
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Each row after the first row corresponds to an account's
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data.
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json file:
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First level just shows a label "stats_data".
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Second level of stats_data lists account names.
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Each account block starts with a time label, and it
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contains stats of account usage.
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"""
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if self.format_type == 'json':
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all_account_stats = collections.defaultdict(dict)
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for (account, year, month, day, hour), d in final_info.items():
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data_ts = datetime.datetime(int(year), int(month),
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int(day), int(hour))
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time_stamp = data_ts.strftime('%Y/%m/%d %H:00:00')
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hourly_account_stats = \
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self.restructure_stats_dictionary(d)
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all_account_stats[account].update({time_stamp:
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hourly_account_stats})
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output = {'stats_data': all_account_stats}
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else: # csv
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sorted_keylist_mapping = sorted(self.keylist_mapping)
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columns = ['data_ts', 'account'] + sorted_keylist_mapping
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output = [columns]
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for (account, year, month, day, hour), d in final_info.items():
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data_ts = '%04d/%02d/%02d %02d:00:00' % \
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(int(year), int(month), int(day), int(hour))
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row = [data_ts, '%s' % (account)]
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for k in sorted_keylist_mapping:
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row.append(str(d[k]))
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output.append(row)
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return output
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def restructure_stats_dictionary(self, target_dict):
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"""
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Restructure stats dictionary for json format.
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:param target_dict: dictionary of restructuring target
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:returns: restructured stats dictionary
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"""
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account_stats = {}
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access_stats = {}
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account_stats_key_list = \
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self.log_processor.plugins['stats']['keylist_mapping']
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access_stats_key_list = \
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self.log_processor.plugins['access']['keylist_mapping']
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hourly_stats = {'account_stats': account_stats,
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'access_stats': access_stats}
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for k, v in target_dict.items():
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if k in account_stats_key_list:
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account_stats[k] = int(v)
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elif k in access_stats_key_list:
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access_stats[k] = int(v)
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return hourly_stats
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def store_output(self, output):
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"""
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Takes the dictionary or the list of rows and stores a json/csv file of
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the values in the stats account.
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:param output: a dictonary or a list of row
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This json or csv file is final product of this script.
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"""
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if self.format_type == 'json':
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out_buf = json.dumps(output, indent=2)
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h = hashlib.md5(out_buf).hexdigest()
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upload_name = time.strftime('%Y/%m/%d/%H/') + '%s.json.gz' % h
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f = io.BytesIO(out_buf)
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else:
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out_buf = '\n'.join([','.join(row) for row in output])
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h = hashlib.md5(out_buf).hexdigest()
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upload_name = time.strftime('%Y/%m/%d/%H/') + '%s.csv.gz' % h
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f = cStringIO.StringIO(out_buf)
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self.log_processor.internal_proxy.upload_file(f,
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self.log_processor_account,
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self.log_processor_container,
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upload_name)
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@property
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def keylist_mapping(self):
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"""
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:returns: the keylist mapping.
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The keylist mapping determines how the stats fields are aggregated
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in the final aggregation step.
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"""
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if self._keylist_mapping == None:
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self._keylist_mapping = \
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self.log_processor.generate_keylist_mapping()
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return self._keylist_mapping
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def process_logs(self, logs_to_process, processed_files):
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"""
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:param logs_to_process: list of logs to process
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:param processed_files: set of processed files
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:returns: returns a list of rows of processed data.
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The first row is the column headers. The rest of the rows contain
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hourly aggregate data for the account specified in the row.
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Files processed are added to the processed_files set.
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When a large data structure is no longer needed, it is deleted in
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an effort to conserve memory.
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"""
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# map
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processor_args = (self.total_conf, self.logger)
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results = multiprocess_collate(LogProcessor, processor_args,
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'process_one_file', logs_to_process,
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self.worker_count)
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# reduce
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aggr_data = self.get_aggregate_data(processed_files, results)
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del results
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# group
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# reduce a large number of keys in aggr_data[k] to a small
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# number of output keys
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final_info = self.get_final_info(aggr_data)
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del aggr_data
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# output
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return self.get_output(final_info)
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def run_once(self, *args, **kwargs):
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"""
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Process log files that fall within the lookback interval.
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Upload resulting csv or json file to stats account.
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Update processed files list and upload to stats account.
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"""
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for k in 'lookback_hours lookback_window'.split():
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if k in kwargs and kwargs[k] is not None:
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setattr(self, k, kwargs[k])
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start = time.time()
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self.logger.info(_("Beginning log processing"))
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lookback_start, lookback_end = self.get_lookback_interval()
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self.logger.debug('lookback_start: %s' % lookback_start)
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self.logger.debug('lookback_end: %s' % lookback_end)
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processed_files = self.get_processed_files_list()
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if processed_files == None:
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self.logger.error(_('Log processing unable to load list of '
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'already processed log files'))
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return
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self.logger.debug(_('found %d processed files') %
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len(processed_files))
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logs_to_process = self.log_processor.get_data_list(lookback_start,
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lookback_end, processed_files)
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self.logger.info(_('loaded %d files to process') %
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len(logs_to_process))
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if logs_to_process:
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output = self.process_logs(logs_to_process, processed_files)
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self.store_output(output)
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del output
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self.store_processed_files_list(processed_files)
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self.logger.info(_("Log processing done (%0.2f minutes)") %
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((time.time() - start) / 60))
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