mtstat/queryengine.py

#! /usr/bin/env python
# Copyright (c) 2013 The Chromium OS Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
from mtlib.log import FeedbackDownloader, FeedbackLog, Log
from mtreplay import MTReplay
import fnmatch
import json
import multiprocessing
import os
import random
import re
import traceback
import urllib


# prepare folder for log files
script_dir = os.path.dirname(os.path.realpath(__file__))
log_dir = os.path.join(script_dir, '../cache/logs/')
if not os.path.exists(log_dir):
  os.mkdir(log_dir)


class Query(object):
  """ Abstract class for queries.

  These objects are applied to files by the QueryEngine, which
  calls FindMatches to execute the search on the replay results.

  capture_logs can be set to true to direct the QueryEngine to return
  the Log object in the QueryResults.
  """
  def __init__(self):
    self.capture_logs = False

  def FindMatches(self, replay_results, filename):
    """ Returns a list of QueryMatch objects """
    return []


class QueryMatch(object):
  """ Describes a match and contains information on how to locate it """
  def __init__(self, filename, timestamp, line):
    self.filename = filename
    self.timestamp = timestamp
    self.line = line

  def __str__(self):
    if self.timestamp:
      return str(self.timestamp) + ": " + self.line
    else:
      return self.line

  def __repr__(self):
    return str(self)


class QueryResult(object):
  """ Describes the results of a query on a file.

  This includes all matches found in this file, the number of
  SYN reports processed and optionally the activity Log object,
  if requested by the Query."""
  def __init__(self, filename):
    self.filename = filename
    self.syn_count = 0
    self.matches = []
    self.log = None


class QueryEngine(object):
  """ This class allows queries to be executed on a large number of log files.

  It managed a pool of log files, allows more log files to be downloaded and
  can execute queries in parallel on this pool of log files.
  """

  def ExecuteSingle(self, filename, query):
    """ Executes a query on a single log file """
    log = Log(filename)
    replay = MTReplay()
    result = QueryResult(filename)

    # find platform for file
    platform = replay.PlatformOf(log)
    if not platform:
      print 'No platform for %s' % os.path.basename(filename)
      return result

    # count the number of syn reports in log file
    result.syn_count = len(tuple(re.finditer("0000 0000 0", log.evdev)))

    # run replay
    try:
      replay_result = replay.Replay(log)
    except:
      return result

    result.matches = query.FindMatches(replay_result, filename)
    if result.matches:
      result.log = replay_result.log

    return result

  def Execute(self, filenames, queries, parallel=True):
    """ Executes a query on a list of log files.

    filenames: list of filenames to execute
    queries: either a single query object for all files,
             or a dictionary mapping filenames to query objects.
    parallel: set to False to execute sequentially.
    """

    print "Processing %d log files" % len(filenames)

    if hasattr(queries, 'FindMatches'):
      queries = dict([(filename, queries) for filename in filenames])

    # arguments for QuerySubprocess
    parameters = [(name, queries[name])
                  for name in filenames if name in queries]

    # process all files either in parallel or sequential
    if parallel:
      pool = multiprocessing.Pool()
      results = pool.map(ExecuteSingleSubprocess, parameters)
      pool.terminate()
    else:
      results = map(ExecuteSingleSubprocess, parameters)

    # count syn reports
    syn_count = sum([result.syn_count for result in results])

    # create dict of results by filename
    result_dict = dict([(result.filename, result)
                        for result in results
                        if result.matches])

    # syn reports are coming at approx 60 Hz on most platforms
    syn_per_second = 60.0
    hours = syn_count / syn_per_second / 60.0 / 60.0
    print "Processed ~%.2f hours of interaction" % hours

    return result_dict

  def SelectFiles(self, number, platform=None):
    """ Returns a random selection of files from the pool """
    # list all log files
    files = [os.path.abspath(os.path.join(log_dir, f))
             for f in os.listdir(log_dir)
             if f.isdigit()]

    if platform:
      print "Filtering files by platform. This may take a while."
      replay = MTReplay()
      pool = multiprocessing.Pool()
      platforms = pool.map(GetPlatformSubprocess, files)
      pool.terminate()

      filtered = filter(lambda (f, p): p and fnmatch.fnmatch(p, platform),
                        platforms)
      files = map(lambda (f, p): f, filtered)
      print "found", len(files), "log files matching", platform

    # randomly select subset of files
    if number is not None:
      files = random.sample(files, number)
    return files

  def DownloadFile(self, id):
    """Download one feedback log into the pool."""
    downloader = FeedbackDownloader()

    filename = os.path.join(log_dir, id)
    if os.path.exists(filename):
      print 'Skipping existing report', id
      return

    print 'Downloading new report', id
    try:
      # might throw IO/Tar/Zip/etc exceptions
      report = FeedbackLog(id, force_latest='pad')
      # Test parse. Will throw exception on malformed log
      json.loads(report.activity)
    except:
      print 'Invalid report %s' % id
      return

    # check if report contains logs and actual events
    if report.activity and report.evdev and 'E:' in report.evdev:
      report.SaveAs(filename)
    else:
      print 'Invalid report %s' % id

  def Download(self, num, offset=0, parallel=True):
    """Download 'num' new feedback logs into the pool."""
    downloader = FeedbackDownloader()

    # download list of feedback report id's
    params = {
      '$limit': str(num),
      '$offset': str(offset),
      'mapping': ':list',
      'productId': '208'  # ChromeOS
    }
    url = ('https://feedback.corp.google.com/resty/ReportSearch?' +
           urllib.urlencode(params))
    data = downloader.DownloadFile(url)
    data = data[data.find('{'):]  # strip garbage before json data

    reports_json = json.loads(data)
    report_ids = [item['id'] for item in reports_json['results']]

    # Download and check each report
    if parallel:
      pool = multiprocessing.Pool()
      results = pool.map(DownloadFileSubprocess, report_ids)
      pool.terminate()
    else:
      results = map(DownloadFileSubprocess, report_ids)

def GetPlatformSubprocess(filename):
  replay = MTReplay()
  log = Log(filename)
  detected_platform = replay.PlatformOf(log)
  if detected_platform:
    print filename + ": " + detected_platform.name
    return filename, detected_platform.name
  else:
    return filename, None

def ExecuteSingleSubprocess(args):
  """ Wrapper for subprocesses to run ExecuteSingle """
  try:
    return QueryEngine().ExecuteSingle(args[0], args[1])
  except Exception, e:
    traceback.print_exc()
    raise e


def DownloadFileSubprocess(id):
  """ Wrapper for subprocesses to run DownloadFile """
  try:
    return QueryEngine().DownloadFile(id)
  except Exception, e:
    traceback.print_exc()
    raise e